Biomechanical Analysis of the Sit-to-Stand Transition 2015

Biomechanical Analysis of the Sit-to-Stand

Transition

A Thesis submitted to The University of Manchester for the

degree of Master of Philosophy in the Faculty of Engineering and

Physical Sciences

2015

Ivette Yadira Campos Padilla

SCHOOL OF MECHANICAL, AEROSPACE AND CIVIL ENGINEERING

Biomechanical Analysis of Sit to Stand Transition

2

Table of Contents TABLE OF CONTENTS............................................................................................................. 2LIST OF FIGURES ..................................................................................................................... 5

LIST OF TABLES ....................................................................................................................... 8LIST OF ABBREVIATIONS ..................................................................................................... 9

NOMENCLATURE................................................................................................................... 11

ABSTRACT................................................................................................................................ 12DECLARATION........................................................................................................................ 13

COPYRIGHT............................................................................................................................. 13ACKNOWLEDGMENTS ......................................................................................................... 15

CHAPTER 1. INTRODUCTION ....................................................................................... 16

1.1. General Objective .....................................................................................................18

1.2. Specific Objectives ...................................................................................................18

1.3. Thesis Overview .......................................................................................................19

CHAPTER 2. LITERATURE REVIEW........................................................................... 21

2.1. Biomechanics of Human Movement ........................................................................21

2.1.1.Background of the Study of Human Movement ....................................................22

2.2. Activities of Daily Living .........................................................................................24

2.2.1.Phases of the Sit to Stand Transition......................................................................25

2.3. Sit-to-Stand Transition Test ......................................................................................26

2.3.1.Technology to Analyse the Sit-to-Stand Transition ...............................................28

2.3.2.Sit-to-Stand Transition in Physical Therapy ..........................................................30

2.4. Effects of Pathological Conditions on the Human Motion .......................................33

2.4.1.Effects of Ageing on the Human Motion. ..............................................................34

2.4.2.Younger vs Older Performance ..............................................................................35

2.5. Summary ...................................................................................................................37

CHAPTER 3. METHODOLOGY...................................................................................... 39

3.1. Experimental Set-Up.................................................................................................39

3.2. Data Acquisition .......................................................................................................42

3.3. Data Processing.........................................................................................................45

3.3.1.Biomechanical Model Creation and Kinematics Data Analysis ............................45

3.3.2.Analysis of Kinematic Data, Principal Component Analysis and Kinetic

analysis 47


3

3.3.3.Time and Magnitude Normalization ......................................................................48

3.3.4.Principal Component Analysis ...............................................................................49

3.3.5.Kinetics...................................................................................................................49

3.4. Summary ...................................................................................................................51

CHAPTER 4. BIOMECHANICAL ANALYSIS COMPARISON OF SIT-TO-

STAND TRANSITION PERFORMANCE BETWEEN YOUNGER AND OLDER PARTICIPANTS................................................................................................ 53

4.1. Introduction...............................................................................................................53

4.2. Whole-Body Centre of Mass Kinematics .................................................................54

4.2.1.Kinematic Analysis Comparison of the Whole-Body Centre of Mass for self-

selected Slow, Normal and Fast Speeds ...........................................................................56

4.2.2.Kinematic Analysis Comparison of the Centre of Mass’ Segments for self-

selected at Slow, Normal and Fast Speeds. ......................................................................57

4.2.3.Kinetic Analysis Comparison between the Joint’s Centres of Younger and

Older Participants .............................................................................................................59

4.2.4.Comparison of the Principal Component Analysis applied to Momenta

Variables of the WB CoM for self-selected Slow, Normal and Fast................................67

4.2.5.Comparison of the Principal Component Analysis applied to Momenta

Variables of the Centre of Mass Segments for self-selected Slow, Normal and Fast

Speeds 69

4.2.6.Summary ................................................................................................................73

CHAPTER 5. CONCLUSIONS AND DISCUSSION....................................................... 74

5.1. Younger Participants Performance of the Sit-to-Stand Transition ...........................74

5.2. Older Participants’ Performance of the Sit-to-Stand Transition...............................75

5.3. Conclusions of the Sit-to-Stand Transition Comparison Performance between

Younger and Older Participants...........................................................................................76

5.3.1.Principal Component Analysis of the Whole-Body Centre of Mass......................76

5.3.2.Principal Component Analysis applied to Momenta Variables of the

Segments Centre of Mass .................................................................................................77

5.3.3.Kinetic Analysis of the Joints.................................................................................78

REFERENCES........................................................................................................................... 80APPENDIX 1 .............................................................................................................................. 88

Ethical Document ................................................................................................................88

APPENDIX 2 .............................................................................................................................. 90Arrangement of the Anatomical Landmarks .......................................................................90

APPENDIX 3 .............................................................................................................................. 92


4

Data Process using Vicon Nexus Software..........................................................................92

APPENDIX 4 .............................................................................................................................. 94Data Motion Analysis ..........................................................................................................94

APPENDIX 5 .............................................................................................................................. 96Normalization Processing ....................................................................................................96

APPENDIX 6 .............................................................................................................................. 98Statistics ...............................................................................................................................98

Principal Component Analysis of the Whole-Body .........................................................98

Principal Component Analysis of the body segments ......................................................99

APPENDIX 7 ............................................................................................................................ 101

Kinetic Analysis...................................................................................................................101


5

List of figures

Figure 1. Sit to stand cycle normalized at 100%. Showing: at 0% sitting position; at

10% beginning of upper body movement (trunk, arms and pelvis); at 27% start to

rise; at 27% starting the HAT swinging movement; at 35% body leaves the chair;

maximum velocity is reached at 45% then at 73% body begins to stabilize the

standing position. (modif.) [6, 47]. .................................................................................25

Figure 2. Support base. a) Chair base support (when the person is sitting its CoM is

placed over the area of the five branches of the chair) and b) Feet base support

(when the person is standing its CoM is placed over the area of the feet)......................27

Figure 3. Three different sitting positions of a person. ...................................................28

Figure 4. Force-plate operation. a) Force-plate with a coordinate systems that helps

to measure the GRF and the CoP. b) Forces applied to the plate when a foot is on it.

c) Four sensors measure the position and the magnitude of the force. d) Resultant

force from the foot forces, which is the GRF applied (modif.)[161]..............................29

Figure 5. Different kinds of protocols used in the STS transition analysis. a) STS

transition analysis for younger males participants using a chair without armrest and

backrest [90], b) STS transition analysis for older females participants using a chair

with backrest, and c) STS analysis for older female rheumatoid arthritis patients

using an ejector chair [91]...............................................................................................31

Figure 6. Characterization of Pathologic Sit-to-Stand. (modif.) [11]. ............................34

Figure 7. Marker distribution over the body. a) Markers position on the landmarks

(modif.)[122]. b) Photograph of reflective markers placed and a participant for the

STS transition..................................................................................................................41

Figure 8. Motion capture system from VICON Company. a) Gait-Lab of The

University of Manchester, b) Infrared cameras from VICON system used in the

Gait–Lab..........................................................................................................................42

Figure 9. Illustration of the two static positions of the STS transition. a) In-vivo

capturing of the human body. b) Link-segment model from SMAS. .............................44

Figure 10. Data pre-processing. a) Virtual reconstruction on VICON Nexus System.

b) Markers identified and labelled according their anatomical position.........................45


6

Figure 11. Illustration of static sitting position of the eight link-segment model. ..........47

Figure 12. Flow Chart showing the Methodology used to estimate the STS

transition motion. ............................................................................................................52

Figure 13. Translational motion of the whole-body centre of mass along sit-to-stand

transition. a) Three dimensional plot of the whole-body centre of mass position in a

sagittal plane during the STS transition. b) Representative STS transition cycle

defining relevant time points...........................................................................................54

Figure 14. Average velocities of the whole-body centre of mass from whole

participants. .....................................................................................................................55

Figure 15. Comparison of trajectories of the whole-body centre of mass of younger

and older healthy participants at three different speeds. a) WB CoM position, b)

WB CoM linear momentum, c) WB CoM angular momentum......................................57

Figure 16. Comparison of the HAT centre of mass angular momentum at fast speed

of younger and older healthy participants.......................................................................58

Figure 17. Comparison of the angular momentum of the thigh and shank segments

of younger and older healthy participants at three different speeds. a) Thigh

segments analysis, b) Shank segment analysis. ..............................................................59

Figure 18. Comparison of the kinetic analysis result of the waist joint centre of

younger and older healthy participants at three different speeds. a) Waist joint

centre torque, b) Waist joint centre power. .....................................................................60

Figure 19. Comparison of the kinetic analysis result of the hip joints centre of

younger and older healthy participants at three different speeds. a) Hip joints centre

torque, b) Hip joints centre power, c) Hip joints centre work done................................62

Figure 20. Comparison of the kinetic analysis result of the knee joints centre of

younger and older healthy participants at three different speeds. a) Knee joints

centre torque, b) Knee joints centre power, c) Knee joints centre work done. ...............64

Figure 21. Comparison of the kinetic analysis result of the ankle joints centre of

younger and older healthy participants at three different speeds. a) Ankle joints

centre torque, b) Ankle joints centre power, c) Ankle joints centre work done. ............66

Figure 22. Comparison of the principal component analysis of the whole-body

centre of mass of younger and older healthy participants at three different speeds. a)

First principal component (PC1) and Z-scores momentum analysis, b) Second


7

principal component (PC2) and Z-scores momentum analysis, c) Third principal

component (PC3) and Z-score momentum analysis. ......................................................68

Figure 23. Comparison of the first principal component (PC1) and Z-score

momentum of the HAT segment momentum variables of younger and older healthy

participants at three different speeds...............................................................................69


momentum analysis of the pelvis segment momentum variables of younger and

older healthy participants at three different speeds.........................................................70


momentum analysis of the thighs segment momentum variables of younger and



momentum analysis of the shanks segment momentum variables of younger and



momentum analysis of the feet segment momentum variables of younger and older

healthy participants at three different speeds. .................................................................73

Figure 36. Labelling windows from VICON Nexus software to label the human

motion captured...............................................................................................................92

Figure 37. Before and After of Labelling Markers in Vicon Software...........................93

Figure 38. Model of the human body during STS transition. .........................................95

Figure 39. Vector velocity from the trial one of participant number one, showing

the parameters needed to normalize the trials. ................................................................97

Figure 40. Markers, joint, and CoP position on sagittal and frontal view at 35% of

the STS transition cycle. ...............................................................................................102

Figure 41. Flow diagram used to obtain the ankle force...............................................103

Figure 42. Flow diagram to obtain the ankle torque. ....................................................104


8

List of Tables

Table 1. Anthropometric individual data from all participants involved in this study. ..40

Table 2. Technical Markers ............................................................................................90

Table 3. Anatomical Markers..........................................................................................91


9

List of Abbreviations

ADL Activities of Daily Living

STS Sit-to-stand

MoCap motion capture

WB whole-body

CoM centre of mass

HAT head, arms, trunk

PCA Principal Component Analysis

AP Anteroposterior axis

ML Medio lateral axis

RoM Range of Motion

DOF Degree of Freedom

P Position point

r Vector of P

O Origin of Coordinates

X Anteroposterior axis

Y Vertical axis

Z Medio lateral axis

SI International System of Units

W Weight

F Force

GRF Ground Reaction Force

CoP Centre of Pressure

GPE Gravitational Potential Energy

h Height

2D Two Dimensions

3D Three Dimensions

EMG Electromyography

VICON Vicon Analysis System


10

ABC Activities-Specific Balance Confidence

FES Falls Efficacy Scale

SD Standard Deviation


11

Nomenclature

N Newton

m Metre

cm Centimetre

mm Millimetre

g Gram

kg Kilogram

kg m/s Linear Momentum

Kg m2/s Angular Momentum

J Joule


12

Abstract The Sit-to-Stand (STS) transition is a voluntary daily activity that consists of rising from a sitting position to a standing position, an activity that is typically performed by a person several times a day. To undertake the activity successfully requires the coordination of the body limbs in order to transfer the body weight between the sitting and standing positions, maintaining the balance, in order to avoid a fall. A biomechanical analysis of the STS transition provides useful information about the motor ability and control strategy of a person and as such, it is commonly employed to assess functional performance, and as an indicator of lower limb strength in the elderly and in people with disabling diseases. The aim of the work described in this thesis was to investigate and analyse the STS transition in two groups of healthy subjects, a cohort (n=10) of younger adult participants (age range 28±2 years) and a cohort (n=10) of older adult participants (age range 56±8 years), in order to identify the differences in the performances within and between the two groups when the STS transition was undertaken at different speeds. The two groups of participants performed STS transition trials at three, different, self-selected speeds (normal, slow and fast) during which data was recorded from a caption systems, consisting of a set of six infrared-cameras and two force plates. The in-vivo data obtained was applied to a link segment biomechanical model enabling the kinematic contribution of the major body segments to the STS activity to be determined for each participant. A principal component analysis (PCA) was undertaken to identify any aggregate and segmental differences in the STS transition performance between speeds. In addition, a kinetic analysis was performed to determine the torque and power contributions of the lower limb joints during the STS transition. The results from the analysis showed that younger and older participants performed the STS transition with a similar pattern, but they used different strategies to ascend according to the speed at which the activity was being performed. The younger participants used the same strategy at slow speed than the older participants used at slow and normal speeds. Likewise, the younger participants used the same strategy at normal and fast speeds as the older participants used at fast speed. From the segmental analysis it was found that the upper-body and pelvis segments presented the larger variability than the other segments. From the joint analysis, the knee and hip joints were identified as the joints that provide the greatest contribution to the STS transition as they generated most of the power and torque required for the activity. The results obtained and the methodology developed could help clinicians with the diagnosis, planning and selection of treatment for patients with a lack of mobility. This type of analysis may also find application in fields such as robotics, ergonomics and sports training.


13

Declaration

Any portion of the work referred to in the thesis has been submitted in support of an

application for another degree or qualification of this or any other university or

other institute of learning.

Copyright

i. The author of this thesis (including any appendices and/or schedules to this

thesis) owns certain copyright or related rights in it (the “Copyright”) and she

has given The University of Manchester certain rights to use such Copyright,

including for administrative purposes.

ii. Copies of this thesis, either in full or in extracts and whether in hard or

electronic copy, may be made only in accordance with the Copyright, Designs

and Patents Act 1988 (as amended) and regulations issued under it or, where

appropriate, in accordance with licensing agreements which the University

has from time to time. This page must form part of any such copies made.

iii. The ownership of certain Copyright, patents, designs, trademarks and other

intellectual property (the “I Intellectual Property Rights”) and any

reproductions of copyright works in the thesis, for example graphs and tables

(“Reproductions”), which may be described in this thesis, may not be owned

by the author and may be owned by third parties. Such Intellectual Property

and Reproductions cannot and must not be made available for use without the

prior written permission of the owner(s) of the relevant Intellectual Property

and/or Reproductions.


14

iv. Further information on the conditions under which disclosure, publication and

commercialisation of this thesis, the Copyright and any Intellectually

Property Rights and/or Reproductions described in it may take place is

available in the University IP Policy (see

http:/documents.manchester.ac.uk/DocuInfo.aspx?DocID=487), in any

relevant Thesis restriction declarations deposited in the University Library,

The University Library’s regulations (see

http://manchester.ac.uk/library/aboutus/regulations) and in The University’s

policy on Presentation of Theses.


15

Acknowledgments

I would like to express my gratitude to everyone who supported me through the

course of this research. I am thankful for their help, support and friendly advice

during the research.

My sincere thanks to Prof Teresa Alonso-Rasgado, Dr Alan Walmsley and to the

Bioengineering Research Group for their guidance to improve this work.

I would like thanking to my family for their support along this investigation. To my

friends for their friendship, help and support during the development of this

research.

I wish to gratefully acknowledge the PhD scholarship financial support provided by

the National Council on Science and Technology of Mexico (CONACYT).


16

Chapter 1. Introduction

People perform many activities during their life such as walking, running, jumping,

climbing stairs, and sit-to-stand (STS). The correct performance of these tasks

requires a sufficient level of mobility and thus allows a person to move

independently.

The STS transition is one of the most complicated and frequent activities executed

by a person during an ordinary day. The ability to move the body from a sitting

position to standing position is a task that demands coordination and balance of the

whole body [1-3]

The STS task requires coordination, adequate mobility and balance, and sufficient

strength in the lower limbs to reach the standing position, and is essential for

functional mobility [4, 5]. STS tests are commonly used to estimate function in a

person, and can be used as an indicator of lower extremity strength. STS tests can

be important indicators of mobility limitations that are typically present as early

indications of a degenerative process [6, 7]. Hence, the importance of human

motion studies that allows a segmental analysis of the movement in the STS

transition. The main aim in evaluating an individual’s performance during activities

such as the STS transition is to identify potential opportunities to improve the

ability to move in order to regain or maintain physical independence [8-10].

STS transition has been used as a motion test where the person’s ability to perform

the STS activity without losing balance is analysed and evaluated, thus providing

the functional level of mobility [11, 12]. Clinical balance tests have revealed that

the STS transition is one of the most common activities related to the risk of falling

[12-15], because of the complexity involved in performing it.

Healthy younger people easily perform the STS transition. However, for people

with medical conditions such as Parkinson’s disease, the after effects of a stroke, or


17

motor impairment as a result of ageing, the ability to move is diminished. In such

cases, the WB CoM trajectory presents smalls displacements, which can be affected

body control which may be reflected in the loss of balance, increasing the risk of

falls [3]. The frequently undertaken STS activity can therefore be a challenge to

complete it for people in these circumstances [16].

In undertaking research into the STS transition, several physiologists have found it

beneficial for analysis purposes. The majority of the research into STS to-date has

been carried out to examine the performance of older people [17-19], obese

participants, women during pregnancy [20] and children with disabilities [21].

These studies say that STS transition in those people present abnormalities during

the performance, presented an analysis in just two dimensions and using only three

segments. For that reason, this research focuses on developing a methodology

which will allows an analysis of the performance of the human body throughout the

STS transition. This thesis describes an investigation into the STS transition in two

groups of healthy subjects, a group of younger adults and a group of older adults,

when the STS activity is undertaken at different speeds, enabling a comparison of

performance to be made both within and between the groups.

The first study presented investigated the STS performance in younger, healthy

participants (n=10, mean age 28±2 years) when the activity was undertaken at three

self-selected speeds: slow, normal and fast. Kinematic and kinetic analysis [22, 23]

was applied to a 13 segment whole body biomechanical model constructed using

motion capture and force plate data obtained from participants trials in order to

elucidate the contribution of the major body segments to the kinematics of the STS

transition. Principal Component Analysis (PCA) was applied to the whole body and

segmental momentum components in order to identify the more significant

momentum variables and to determine any gross and segmental level differences in

these variables between the speeds.

Next, the STS transition performance of a group of more older, healthy participants

(n=10, mean age 56±8 years) was investigated. The older participants undertook the


18

same series of STS trials as the group of younger participants i.e. at slow, normal

and fast self-selected speeds while data was captured from the motion capture

system and force plates, enabling a link segment whole body biomechanical model

to be constructed for each participant and facilitating kinematic and kinetic

analyses. PCA was again applied to the momentum variables to analyse segment

performance and examine any differences in performance between the three speeds.

Finally, the performance of the younger and older groups of participants when

undertaking the STS transition at the three speeds selected were compared and

analysed enabling the similarities and differences in the strategies employed by the

two groups to be identified.

1.1. General Objective

The aim of the work described in this thesis was to investigate how the transition

from a sitting to a standing position (STS transition) is accomplished in healthy

younger and older adults, thus enabling the movement to be identified in both

groups of participants.

This analysis of the STS transition enables the mobility pattern to be determined

and explains the contribution of the body’s segments and joints during the motion.

Furthermore, it aids in identifying any performance differences related with the

speed at which the activity is performed and the age of the person undertaking the

task.

1.2. Specific Objectives

To determine the kinematics and kinetics of the whole-body (WB), body segments

and body joints in two groups of healthy participants (younger adults, mean age

28±2 years and older adults, mean age 56±8 years) when undertaking the STS


19

transition at slow, normal and fast self-selected speeds. PCA was used to clarify the

performance of each segment in the execution of the STS transition.

To identify the similarities and differences existing between the performances of

the participant groups at the three different STS transition speeds considered.

To develop a methodology to quantify the momentum variables during the STS

transition based on human motion analysis involving anthropometric measurements

obtained from in-vivo motion capture of the participant/patient using computational

estimation. To indicate how the results could be useful as an intervention in

therapy/treatment and/or design of new assistive devices to help restoration of

normal functions.

1.3. Thesis Overview

This thesis is focused on the analysis of the STS transition. In addition to this

introductory Chapter, the thesis consists of five further chapters that have been

organized as follows:

The second Chapter describes early studies of human motion and the chronology

through to the present day. Also, this chapter gives a brief introduction to the

biomechanics related to human motion. Also explains the characterization of the

STS transition and provides a literature review of relevant analyses undertaken in

previous research. It defines the methods, equipment and tools used to analyse the

transition. Problems and disorders that can affect mobility are also described in this

Chapter

The third Chapter explains the methodology used to perform the STS trials detailed

in this thesis. A brief introduction to the equipment is provided and preparation of

the participant, video recording and data processing are described.


20

The fourth Chapter shows the comparison results of the STS transition analysis

between the performances of the two groups of participants, younger and older

adults, for the STS transition trials undertaken at slow, normal and fast self-selected

speeds.

Finally, the fifth Chapter presents the discussions and conclusions drawn from the

research described in this thesis.


21

Chapter 2. Literature Review

This chapter explains human motion studies and the factors that affect movement.

As well as reviews of the STS transition studies and its use in clinical applications

to evaluate mobility in people with disabling diseases and ageing. STS transition is

a complex activity that involves much effort from the neuro-muscular and skeletal

systems. It consists of a number of consecutive motion phases that allow the subject

to move from the sitting position to the standing position. In order to successfully

complete the STS transition, the human body requires a very high degree of

flexibility with sufficient stability to maintain the equilibrium of the whole

movement and to undertake the coordinated movement patterns [1, 2]. An

important use for STS studies is for estimating the variations in the performance of

different subjects or groups of subjects.

2.1. Biomechanics of Human Movement

The scientific study of the human motion is called kinesiology. It describes,

analyses, and evaluates the biomechanical systems. This science is developed with

interaction from others fields of knowledge such as physiology, psychology,

neuroscience, anatomy and biomechanics [24, 25]. Biomechanics describes the

interaction of the human body with the external environment, obtaining truthful

approximations of its performance by mechanics engineering theories [26-31].

Biomechanics relates the application of engineering principles to biological

systems, so that, engineering analysis may be used to understand the function of the

biological systems. Different techniques and methods are used such as experimental

measurements, modelling, and computer simulation. However, the complexity of

biological systems means that these methods are still being refined in order to

obtain more accurate results [32].


22

Biomechanics in human movement can be defined as the muscular, joints and

skeletal actions of the body when performing a task. The precise understanding of

human movement has a great impact on the improvement of the physical

capabilities, rehabilitation and injury prevention [24, 30, 33-35].

To perform locomotion it is necessary that all the components of the living being

are controlled and coordinated [36]. The study of human motion has been useful to

understand how the body moves. The human body produces internal forces to

produce limb movements. Biomechanics examine the effects of these forces and the

external reaction forces that acting on the motion body. The study of biomechanics

is divided into two areas kinematics and kinetics. Kinematics describes the motion

of bodies without considering the causes of movement (displacement, velocity,

acceleration, time, etc.) and kinetics studies the forces that produce the motion

(force, torque, power, etc.) [26, 33, 37-39].

2.1.1. Background of the Study of Human Movement

The early methods used to analyse human motion in living beings were based on

drawings or photographs. Then, with the invention of cinematography, motion

analysis improved, enabling more accurate measurements to be obtained. Early

studies revealed that animal movements were more complex than was originally

thought, and that successful movement performance required control and

coordination of all the components of the body system [40, 41].

The study of human motion became more important during the World wars, as a

result of the wounds inflicted that caused disability in both civilians and soldiers.

Therefore, the demand for orthotics and prostheses was substantial, which led an

increased interest in the clinical analysis of human motion performance.

In the 1970’s the main clinical analysis undertaken was the gait test. This test

involved the walking activity, which was considered to be a dynamic mobility

measure [29, 33, 34, 36]. Subsequently during the 1980’s, researchers noticed that


23

the STS was also an important activity that required co-ordination, control and

strength, mainly in the lower limbs. This activity leads to a basic clinical test being

established to measure the level of mobility of people.

Nowadays, the demands of daily life, the interaction with the environment, the

increase in life expectancy of human beings, and an understanding of how the body

can produce motion, are the main factors which are fuelling the interest and

motivation of researchers to observe and interpret human motion.

Through time, a many research studies have developed devices and technologies

focused on the study of human motion. These studies begin with the parameterization

of different tasks in order to obtain a classification of the movements and the phases

that involves their performance.

Subsequently the studies of forces exerted by the body on the Earth were correlated

to the motion performance [29, 33, 38]. The need to study the human body’s

behaviour, actions and reactions, as well as to improve any abnormal condition, are

the most important reasons for the study of human locomotion research studies,

which has allowed clinicians and researchers apply this knowledge to the

development of devices and novel techniques that improve human health [29, 33,

34].

Some scientists frequently use motion tracking systems, motion capture,

electrophysiology (of muscle and brain activity), and ultrasonography in order to

analyses of human locomotion. These techniques and associated equipment are

helpful in creating models in order to simulate the human body system and hence

for exploring and estimating body performance using analytical methods [24, 29,

30, 42]. Currently, it is able to rehabilitate the human body and improve or recover

the locomotion, understand the differences existing between a normal and

pathological function and prevent and treat injuries. Some direct applications are in

orthopaedics, ergonomics, rehabilitation, and sports [30].


24

2.2. Activities of Daily Living

People perform many different activities during an ordinary day. In the field of

healthcare they are called the activities of daily living (ADL). The ADL are

fundamental tasks that, almost all of the time, the person performs automatically

(unconsciously) but at the same time with the intention to move. The motor system

is the ability to perform these intentional movements. When the motor system is in

good condition it is not a problem to perform the ADL successfully, but if the

motor system is impaired it will be more difficult to perform them [43, 44]. The

human body shows a very high degree of flexibility with sufficient stability. To

maintain the equilibrium all the movements must be coordinated in to patterns to

allow the motion [45].

The variety of ADL depends on the person’s profile, but some ADL are more

functionally demanding such as walking, running, jumping, climbing stairs and sit

to stand. Biomechanics has been focused on these tasks because their analysis

helped medical researchers to measure the ability of each patient in order to predict

disabilities.

It has been found that many of the functionally demanding tasks start whit the

person sitting and needing to stand up in order to begin another locomotion task;

this is the sit-to-stand transition and this is one of the most frequent activities during

the day [46].

In order to successfully perform the STS transition, the individual requires adequate

strength and coordination of whole body systems. If any of the systems are altered

or damaged the level of mobility decreases affecting the normal body motion.

People with pathologies often present with difficulties balancing the body and

therefore is more challenging to perform the STS transition successfully.


25

2.2.1. Phases of the Sit to Stand Transition

The STS movement has four principal phases: sitting, initiation, ascending, and

standing. Each phase is composed of events, which change the orientation and

position (pose) of the body components (segments and joints). The STS transition

begins when the person is sitting on a firm surface such as a chair or the edge of a

bed, it’s called first base of support.

Then movement begins when the upper-body moves forwards, following this an

impulse to initiate the ascension is required in order to leave the chair by the

extension of the lower limbs, until all the body mass is placed over the feet (second

base of support). It continues with the extension of the whole body, to finally

standing straight over the feet [1, 2, 4, 6, 11, 18, 47-50]. These are called the phases

of the STS transition. Figure 1 shows these phases as a percentage of the movement

time [6, 48, 49, 51].

Figure 1. Sit to stand cycle normalized at 100%. Showing: at 0% sitting position; at 10%

beginning of upper body movement (trunk, arms and pelvis); at 27% start to rise; at 27% starting the HAT swinging movement; at 35% body leaves the chair; maximum velocity is reached at 45% then at 73% body begins to stabilize the standing position. (modif.) [6, 47].

In the beginning it is necessary to have an impulse, of sufficient magnitude to

transfer the upper-body forward and upright, in order get-up and leave the chair. At


26

braking impulse is required to stop the movement and to obtain the balance to

complete the transition, thus completing the transfer, and finishing the movement

standing on the feet [11].

The STS transition is a complex motion performed several times through the day,

so its analysis has been of interest for different specialist such as researchers,

clinicians, therapists and coaches. Each specialist follows different procedures

according to the purpose of their analysis, so the STS transition has been used as a

human motion test.

2.3. Sit-to-Stand Transition Test

The evaluation of the sit-to-stand test is applied to find the functional level of

mobility performed by individuals and so estimate the ability of each person to rise

from the sitting position until they reach the standing position without any injury or

fall. It is also used to analyse injuries and musculoskeletal disease. This kind of

motion test helps to improve the human motion, and even helps the clinicians to

choose a better therapy for the individual.

During the STS transition, there are four factors, which helps to determine the

levels of stability and mobility: size of the support base, location of the CoM

projection within the support base and the participants body mass or body weight

[52].

The STS transition represents a challenge for the human body’s balance, due to the

transition requires the change between two bases of support. The base of support is

the area where the CoM of the WB is supported, in general are the different points

of contact that exist between the human body with the ground, the first is created by

the chair and the second created by the area around the feet [53]. The STS activity

requires the quick transference of the WB CoM from the chair’s support base to the

feet’s support base as is showed in the Figure 2.


27

a) b)

Figure 2. Support base. a) Chair base support (when the person is sitting its CoM is

placed over the area of the five branches of the chair) and b) Feet base support (when the person is standing its CoM is placed over the area of the feet).

The support base function is to help to balance and stabilize the body throughout

the STS transition. The most challenging moment of the whole transition is when

the body leaves the chair, because that is when the body changes its support base:

from the chair to the feet. This transition produces an abrupt movement mainly

caused by the forces and torques produced by segments and joints of the body in

order to ascend and stand [54].

The significance, of the base of support, is due to this determine the stability and

mobility of the human body. The stability depends of the support base size, as the

support base area is wider the stability increase, but as the base area is narrow the

stability decrease. Because is easily to maintain the balance of the CoM of the WB

within the expanded base of support.

The location of the CoM reflects the projection within the support base. This factor

involves the amount of effort that the subject will perform to get up of the chair. It

depends on different environment characteristics such as the height of the chair sit

and the sitting position of the subject [55]. Figure 3 shows there different locations

of the CoM over the area of the initial support base (chair).

The body mass or body weight also gives an idea of the performance of the subject.

Due to a bigger mass the muscles needs more momenta and therefore more power

to get up of the chair.


28

Figure 3. Three different sitting positions of a person.

The relevance of these factors is that every time that STS transition is performed

the coordination of the subject body is assessed [8, 10, 56-65]. Hence the objective

of the STS transition test motion, that is to quantify the functional mobility of the

human body without losing the balance or becoming unsteady. Regardless of the

methodology used, STS test is a reliable and trustworthy method to determine the

loss of balance [66-69].

Clinicians and by researchers has been interested in the analysis of the STS in order

to described the assessment of the stability, coordination, posture correction,

strength and motor control of the body during their performance [70].

2.3.1. Technology to Analyse the Sit-to-Stand Transition

The importance of the STS transition analysis underlies the identification of the

human posture during the motion, improving the performance, avoiding injuries

and suggesting a better treatment to recover the mobility.


29

Test motion analysis has always been conducted with the technology available at

the time of study; starting with the use of simple drawings, followed by photo an

video tracking systems, and lately by computers software and electronic systems

[39, 156].

The motion analysis is frequently completed by the use of Biomechanics platform,

better known as force-plates. The force-plates define the force applied by the

participant on the plate, called ground-reaction-force (GRF), and its point of

application or centre of pressure (CoP). During the data acquisition from the motion

Caption system (MoCap). The force-plates sensors capture the force that the body

exerts over them [5, 159, 160]. Then the device uses 4 force sensors fixed in the

plate corners in order to capture and measure the GRF and CoP of the foot, in the

Figure 4 presents how the force-plate operates.

Figure 4. Force-plate operation. a) Force-plate with a coordinate systems that helps to

measure the GRF and the CoP. b) Forces applied to the plate when a foot is on it. c) Four sensors measure the position and the magnitude of the force. d) Resultant force from the

foot forces, which is the GRF applied (modif.)[161].

When a foot touches a force-plate, it causes various reactions on the contact zone

(Figure 4.b). Those reactions are captured by the four sensors in the plate and are

then characterized by four ground reaction forces (R1, R2, R3, R4) (Figure 4.c).

The sum of these four reactions is the magnitude of the GRF (Figure 4.d). The GRF


30

is represented in the three components (X, Y, Z), given the magnitude and direction

of the vector force.

2.3.2. Sit-to-Stand Transition in Physical Therapy

The original purpose of the first STS test applications was to measure the functional

strength of the muscles in the lower limbs [58, 66, 71]. However has been also

applied the STS transition test to predict disabilities, incidence of care nursing and,

surprisingly, the death related to the loss of balance [68, 72, 73]. The STS test is

considered to be a helpful tool in the comparison of the people’s performance with

different demographic information such as age, gender, and pathologies [18, 20, 74-

78].

The clinical analysis depends of the clinician expertise and of his skills to evaluate

and report a good diagnosis of the person performance [79]. Each clinician has the

own method to determine how well people can rise from a chair. And although the

clinician is helped by an controlled questionnaire based on the activities-specific

balance confidence scale (ABC) and falls efficacy scale (FES), that provide a

measure of confidence in perform the transition [80, 81], this gives a vast

inaccuracy on the mobility description due to there are not a protocol established.

One of the most important findings of the clinical test suggests that STS transition

is an activity that is difficult to perform when the human body is not working

perfectly [69].

The biomechanical analysis, provide a complete study with better accuracy on the

mobility description. Although use the basis of the clinical analysis, it use auxiliary

biomechanics methods and better technology to analyse, process and describe the

mobility of a person (already described above). The biomechanics methods are

based on the dynamic measurements of the persons, and mathematical estimations

of the person transition.


31

The STS analysis is used to compare the performance between normal patterns and

pathologic patterns of the transition in patients with disabilities, diseases, or

different capabilities [11, 17, 20, 48, 66, 70, 79, 82, 83]. Furthermore, more recent

STS analyses were initiated to include the study of the forces exerted by the body to

the ground in order to estimate the joints’ behaviour [29, 33, 38, 40, 71, 84].

Subsequently, the study of the STS transition has driven the focus of clinicians and

researchers to apply this knowledge, to the development of devices and novel

techniques, that aim to benefit and improvement the human body health [29, 33, 34,

79, 85, 86]. Each human being is different in both, their anatomy and characteristics

and in their performance of the ADL. Those particular characteristics provide a

unique strategy, style and distinctive movement for their motion. Each individual

will never repeat exactly the same movement.

However, if the performances of the STS transition trials are regulated, by the

control of certain variables as shown in Figure 5, similar patterns along the trials

can been obtained. Similar movement patterns allow the comparison between

individuals, as long as the protocol of the STS test remains the same, between

participants and the trials recorder [49, 87-89].

a) b) c)

Figure 5. Different kinds of protocols used in the STS transition analysis. a) STS transition analysis for younger males participants using a chair without armrest and backrest

[90], b) STS transition analysis for older females participants using a chair with backrest, and c) STS analysis for older female rheumatoid arthritis patients using an ejector chair

[91].


32

Among the variables that had been controlled over different research groups the

following ones can be mentioned as shown in Figure 5 [7, 11, 17, 92-100]:

• Chair type such as high, with or without armrest, with or without backrest,

with or without caster.

• Preparation of the participant such as arms crossed, arms in front, arms on

the waist, arms helping the seat unload, arms at sides, the position of the

feet.

• Anthropometry of the participant such as gender, age, height, lifestyle,

health status.

• Activity performance speed.

There are different types of techniques to achieve the ascension, hence the wide

range of studies such as sit-to-stand, sit-to-stand-to-sit, five times sit-to-stand,

stand-to-sit-to-stand, sit-to-stand-up-and-go, and complete squat.

The sit-to-stand performance begins from a static sitting position to a standing static

position [6, 7]. The sit-to-stand-to-sit begins with the sitting static position,

ascending to standing, and finally to standing [95, 101]. The five time sit-to-stand

test repeats the similar movements that the sit-to-stand test but in this one the

participants must to perform the motion five consecutive times [67, 68, 102].

The stand-to-sit-to-stand begins with the static standing position, moving to sitting,

and finally to standing position again [103]. The sit-to-stand-up-and-go begins with

the static position sitting, ascending to standing, followed walking or running [10,

104]. Finally, the complete squat does not need a chair and begins with the standing

static position to moving downwards as much as possible to then returning to

standing position [105-107].

The STS test has been focused on selected aspects such as the body CoM

adjustment to maintain the balance, the body segments contribution during the

movement performance, the importance of the base support during the test, the

weight transference performed by the body, the GRF and the CoP trajectory, the

amount of power and energy required to rise, and the body elements behaviour at


33

different speeds in order to understand the performance [1, 6, 17, 22, 72, 92, 108-

110].

The biomechanical analysis has established that the people use a different technique

to stand up form sitting, but in somehow the healthy people has a similar pattern to

perform this activity. The sequence to active the muscles and create joint rotations

seems to be the same strategy used by healthy subjects. However as it gets older the

strategy used by people presents a change in their performance.

2.4. Effects of Pathological Conditions on the Human Motion

There is a wide range of pathologies that affect the systems of the body, and so

directly affect the mobility of the patients, some of which lead to difficulty in

performing an STS transition.

Patients with conditions such as strokes, Parkinson’s disease, and multiple sclerosis

show problems in the strength, flexibility, and control of their movements [51].

Older people also present problems of mobility due to the muscle weakness of their

body [46]. Furthermore pain in the joints and limbs, such as for patients with

rheumatoid arthritis and osteoarthritis, reduces the range of motion, which limits

their ability to carry out the STS activity [70, 111]. Other limitations are presented

in people with temporary conditions, such as pregnancy, recovering from injury, or

having strain and cramp, which restrict the limbs’ motions. These conditions

represent a considerable risk to the person, which can result in falls, which cause

injuries, bone fractures, and sometimes death [14, 15, 20, 112-114].

The pathological effects may produce a change in the control of movement, thus

modifying the movement variables such as the segment position, velocity, and

momentum. For example during the STS transition, when a person produces non-

controller movements it is harder for the body to balance itself during the activity,

thus losing the base of support during the ascending phase and hence when the fall


34

is initiated. Figure 6 shows the characterization of a pathologic STS transition [11,

80].

Figure 6. Characterization of Pathologic Sit-to-Stand. (modif.) [11].

Another complication developed is bad posture on the activities of the ADL, such

as standing, sitting and walking. Bad posture during any activity can cause an

imbalance on the musculoskeletal system, which produces an increase of stress on

the human body causing fatigue and pain. The big problem when bad posture is

presented is that, when the body tries to restore the balance on the systems,

deformities can be caused and body energy wasted which is the origin of

disabilities [115].

2.4.1. Effects of Ageing on the Human Motion.

Ageing is the biological process to which everybody is subject to through life.

Ageing is associated with a change in the characteristics of the human body over a

period of time. The characteristics change according to the physical activity and the

daily diet that each person has in their lifestyle.

Some of the changes could be induced by physical strength, ability to respond,

agility and elasticity of the tissues and wear of the components of the body. The


35

wear of the components makes them more fragile, particularly the skin, muscles

and bones. This decreases the flexibility, reaction time and the range of motion, as

well as making the joints feeling stiff, in many cases with pain, when performing

the ADL.

Ineffective movement at the joints affects the ADL, such as walking, running,

jumping and STS transition, which could lead to loss of balance therefore

increasing the risk of falls. The consequences of falls are sometimes terrible,

causing breakdowns, wounds, psychological problems and, at times, death. Because

the healing process is slow in older adults, and the immune system does not react

with the desired effectiveness, infections and more complications may result.

Loss of balance is also affected by impairment of senses such as hearing and vision.

The hearing sense is related to the vestibular system, which is one of the important

systems that help to maintain the balance. Inadequately function of the vestibular

system may cause problems of imbalance such as disorientation, dizziness and

vertigo.

The older population are sometimes reluctant to undertake physical activity,

because a few falling motion problems sometimes cause depression because the

older abandon their favourite activities, which produce in them a sense of loss.

Hence, the importance of analysing and studying human motion in older individuals

as it will identify the motion failure. If the patient is not treated, bodily functions

continue to deteriorate until the patient needs assistance, from either another person

or special devices (orthotic), to help them to stand-up and to prevent falls [51].

2.4.2. Younger vs Older Performance

Usually, the STS transition is easily performed by younger people but older people

find it a big challenge to complete it. This is due to ageing impairing the body’s

functions, therefore altering the potential and/or ability of the person to move


36

independently, which often affects the body’s control and may be reflected in the

loss of balance and may cause the person fall.

The inability to rise from a chair is presented mainly by chronic patients and older

subjects. For that reason the STS transition is a crucial component in the assessment

of older people’s mobility. Several studies have been focused on this subject with

the intention of minimising the overwhelming hazard of falling when older people

are performing their ADL. Both, clinician and researchers have hypothesised that

fatigue can affect the body functions therefore altering the potential and ability of

the person to move independently [3]. This potentially, could make them stay

prisoners in their chairs [116].

Studies have demonstrated that the performance of the STS transition between

younger and older subjects is similar in their kinematics, kinetics (such as

velocities, maximal joint and segmental angles and torques), effort required to

ascend, and also in the timing and duration of each phase [26, 69, 74]. However,

changes have been found in the momentum of the CoM during the ascending of the

STS transition across different speeds, with older subjects using a big angular

momentums in order to help them to stand up with less effort [93, 117]. One

assumption found and identified, that an exaggerated trunk flexion and linear

momentum on the vertical axis strategy was used by aging people to stand up from

a sitting position [100, 118]. Another assumption found that the older people placed

their feet further back which showed that was more difficult for the older than for

the younger when rising from a chair [41]. Likewise, other study found that the

significant differences in the performance of the older people may be due to the

WB CoM acceleration description [119].

Although many studies have been interested in the different strategies performed by

the older population and some differences have been described there are no studies

that declare where those differences come from or which strategies were used. Or

any studies that could explain which is the most important contribution of each

lower limb component at the time of performing the transition. Hence the


37

importance of this research is to use a different methodology with the intention of

filling the gap on the STS transition analysis.

The physical performance measures may offer advantages over self-reporting

measures in terms of validity, reproducibility, sensitivity to change, applicability to

cross-national and cross cultural studies and the ability to characterize high levels

of function.

The immediate application of STS transition analysis lies in understanding the

differences that exist between a normal and pathological performance. Depending

on the STS transition analysis and the interpretation of the results, the data can be

used in several applications such as to improve physical performance, to

recommend orthopaedic treatment, to assist in sports medicine, to allow the

development of care therapy, to reduce the possibility of injury or to recover from

an injury and to solve different balance disorders [120]. The outcome can also be

oriented to areas such as ergonomics and assistive technology which allows the

development of devices to be used to improve peoples’ health [30].

2.5. Summary

In order to understand the human motion and its abnormal conditions, it is most

important to study the behaviour of the human body (actions and reactions).

Biomechanics provides the knowledge and tools to analyse the human body

locomotion. Biomechanics studies have direct application in orthopaedics,

ergonomics, rehabilitation, and sports [30]. It is also very useful to understand

abnormalities and pathologies and to reduce the possibility of injury.

Biomechanics studies have demonstrated that humans perform their ADL using

different strategies according with their age groups. Older people present a big

challenge to complete their activities in comparison with younger people. The aging

impairments alter the body’s functions and control, reducing the potential for a

person to move independently. STS is one of the most complete ADL. It is


38

important to understand its biomechanics as it provides the level of mobility of a

subject.

The applications for human caption system are very wide. It is used in different

fields of the industry such as in animation (video games, virtual reality, web,

filmmaking movies, television shows) by using the equipment in subjects (such as

actors, performers and athletes) to obtain the motion from them and so the

computer generated imagery can simulates the movements with more accuracy. In

engineering (ergonomics, robotics) by using the equipment in related subjects in

order to obtain the motion information and so devices and new equipment can be

designed.

In scientific research (biomechanical analysis, sports) by using the equipment in

required subjects (participants, patients) to obtain the motion information with the

intention to study and analyse the causes and effects of the motion (patterns,

strategies). With the obtained data from the MoCap a biomechanical model is

constructed in order to simulate the human body’s performance as well as to

minimize the effort of the analysis.

Although, there have been several of studies focused on the STS transition

performance, there is not yet a complete study that can provide information about

the importance of the contribution of each lower limb. Hence the reasons that

makes relevant this study, because it is possible to find and understand differences,

between two different groups of participants, when the motion is performed at three

different self-selecting speeds.

This research used a descriptive model to develop a general analysis of the lower

limbs’, which allows the development of a novel methodology to study the STS

transition performance of the participants. This method is a useful tool to estimate

the weight transfer and the estimation of the segments and joint variables. With this

method were able to identify the normal pattern of movement that the different

groups perform during STS transition to determine their functional efficiency and

quantifying the differences in biomechanical parameters of body motion.


39

Chapter 3. Methodology

This chapter explains the methods, equipment and tools used to analyse the STS

transition are discussed. Human motion data is obtained directly from each

participant, using Video Motion Capture (MoCap) method. MoCap tries to follow

and track body movements, detecting reflective markers, placed on specific

landmarks over the participant body. A data sheet is obtained with the position and

direction of these markers over time. Whereas, the ground reaction force (GRF) and

the centre of pressure (CoP) are obtained. STS transition motion was performed ten

times at same conditions, in order to produce an accurate database from test

performance.

3.1. Experimental Set-Up

Twenty healthy volunteers, ten younger and ten older were required to participate

in this study. The recruitment method was set mainly by placed advertisement

around of the Manchester Universtiy’s Campus, and also asking to friends and

colleagues to participate. The criteria to select the participants were healthy

volunteers with no previous history of neuromuscular or musculoskeletal

impairment; the participants were required in two groups younger (n=10, average

age 28+/-2 years old) and older (n=10, average age 56+/-8 years old).

The individually participant data is showed in the Table 1 [18, 70, 76, 93, 100,

121]. The protocol was approved by the Ethics committee of The University of

Manchester (approval No 11443 shown in Appendix 1) and written informed

consent was obtained from all the participants prior to the study starting.


40

Table 1. Demographic individual data from all participants involved in this study.

# Participant Age Gender Height Mass # Participant Age Gender Height Mass

Younger (years) (m) (kg) Older (years) (m) (kg)

1 26 M 1.64 62 11 50 M 1.81 75

2 29 M 1.8 96 12 59 F 1.5 64

3 38 M 1.81 90 13 60 M 1.66 79

4 32 F 1.64 65 14 56 F 1.53 68

5 29 M 1.74 65 15 78 M 1.63 76

6 26 F 1.54 53 16 53 M 1.88 101

7 29 M 1.79 79 17 54 F 1.56 65

8 32 F 1.52 61 18 53 M 1.59 68

9 28 F 1.6 72 19 50 F 1.56 68

10 28 M 1.8 97 20 55 F 1.56 62

The MoCap system used reflective spherical markers in order to obtain the position,

orientation, and direct movement of each participant. A total of 76 reflective

spherical markers were placed on each subject; 32 markers were placed over

standardized anatomical landmarks and 11 technical markers (each one consisting

of a set of 4 reflective markers on a rigid base) were positioned on different body

segments. In addition 11 virtual markers were defined. In the next page, Figure 7

shows the position of the markers. In the Apendix 2 is showed the complete list of

the markers.

The placement of the markers was repeated, assessment and tested several times

before to place them on the volunteers. I was the only person in charge to place

these markers in order to increased accuracy in the data obtained. The anatomical

and virtual marker positions were used to estimate participant anthropometric data.

The technical markers were used to track the movement of the body segments

during the STS transition trials.


41

Anatomical Markers

1,2, Xiphoid Process and Jugular Notch 3,4 Spinous Process C7 and T8 5,6 Right Humeral Condyles 7,8 Right Styloid Process

9,10 Left Humeral Condyles 11,12 Left Styloid Process 13,14 Right Femoral Epicondyles 15,16 Right Fibula-head and Tibial-

tuberosity 17,18 Left Femoral Epicondyles 19,20 Left Fibula-head and Tibial-

tuberosity 21,22 Right Malleoli

23 Left Calcaneus 24,25 Left Malleoli

26 Right Calcaneus 27,28,29 Right Metatarsals 30,31,32 Left Metatarsals

Technical Markers 33 Head 34 Trunk 35 Back Pelvis

a)

36 Right Upper-Arm 37 Right Lower-Arm 38 Left Upper-Arm 39 Left Lower-Arm 40 Right Thigh 41 Right Shank 42 Left Thigh 43 Left Shank

Virtual Markers 44 Vertex 45 Right Temporomandibular Joint 46 Left Temporomandibular Joint 47 Right Glenohumeral Joint 48 Left Glenohumeral Joint 49 Right Elbow Joint 50 Left Elbow Joint 51 Right Superior Iliac Spine 52 Right Posterior Iliac Spine 53 Left Superior Iliac Spine 54 Left Posterior Iliac Spine 55 Right Hip Joint

b) 56 Left Hip Joint Figure 7. Marker distribution over the body. a) Markers position on the landmarks

(modif.)[122]. b) Photograph of reflective markers placed and a participant for the STS transition.

The virtual markers also gave the position and orientation of the difficult bony

landmarks. As these landmarks are challenging to locate with a simple marker for


42

that reason is used a wand with two markers on it at a known distance to obtain

more accurate measurements.

3.2. Data Acquisition

The participants undertook the trials in a gait analysis laboratory. A VICON Motion

Analysis System (VICON Motion Systems Ltd, Oxford, UK) consisting of 6 Model

MX-T Series Cameras running VICON Nexus 7 software was used to detect and

measure the three-dimensional location of the markers placed on the subjects

during the trials. The VICON Nexus T-wand calibration system was used to

calibrate the motion capture volume for data capture and post-processing. Data

were captured from the cameras at 200 frames per second (fps). Two AMTI

BP400600 force plates (Advanced Mechanical Technology, Inc., Watertown, MA,

USA) were used to measure the GRF under each foot and to determine the CoP

location synchronously with the kinematic data at a sampling rate of 1000Hz. The

kinematic data were stored and analysed off-line on a personal computer (DELL

Inc., Round Rock, TX, USA). Figure 8 shows the VICON Nexus system used.

a) b)

Figure 8. Motion capture system from VICON Company. a) Gait-Lab of The University of Manchester, b) Infrared cameras from VICON system used in the Gait–Lab.

Having positioned the reflective spherical markers on the bony landmarks of the

subject, a calibration procedure was followed in order to identify and locate

anatomical landmarks, and functional joint centres [122, 123].


43

In order to identify the anatomical landmarks, participants were asked to undertake

three static trials in pre-determined positions. Subjects were first required to stand

upright on a marker placed on the floor in the middle of the gait laboratory, looking

straight ahead with their arms abducted so that they were parallel to the ground.

Participants held this position while data from the cameras were captured for three

seconds. The participants then turned 90° about the vertical axis and assumed the

same position as previously: looking straight ahead with their arms abducted. This

position was held while data were again recorded from the cameras for three

seconds. The third static test was undertaken with the subject turning through a

further 90° before assuming the same position with the arms abducted after which

camera data were recorded.

The virtual landmarks were then identified using the VICON wand. Participants

were asked to stand upright in the fundamental position on the marker placed in the

middle of the gait laboratory floor looking straight ahead with arms straight down

by the side. Subjects were requested to hold this position and remain static while

the investigator touched each virtual landmark individually with the wand. The

wand was held against each virtual landmark in turn for three seconds while data

from the cameras were captured. To establish the virtual landmark, which is the

proximal point, was calculated from the stick wand. The virtual landmark was

subtracted from distal distance, of the two markers, of the wand stick in the 3D

anatomical reference frame on the global axes [124, 125].

In order to identify the four functional joint centre of each of the hip and shoulder

joints, subjects were required to again stand upright on the marker in the middle of

the gait laboratory and undertake a series of dynamic trials in which data were

recorded from the cameras. Taking each arm in turn, keeping the trunk steady and

without bending the arm at the elbow, subjects were asked to move their arm first

straight out in front, then straight back behind them, the to the side, and then in a

circular motion from front to back. Then, taking each leg in turn, keeping the trunk

steady and without bending the leg at the knee, they were then required to move


44

their leg straight out in front, then straight behind them, the to the side, and then in

a circular motion from front to back.

Having performed the calibration procedure, the STS trials were then undertaken.

Participants sat on a backless chair with one foot on each of the two force plates.

The height of the chair was adjusted so that the thighs of the participants were

parallel to the floor and perpendicular to the shanks, ensuring each subject started

the STS task from the same body position. Figure 9 shows the stating and final

position of the STS movement.

Keeping their hands on their waist, participants were then asked to perform the STS

task while data were recorded from the cameras and the force plates. Subjects

performed ten STS trials at a self-selected speed: this was the normal speed.

Subsequently, participants performed ten trials at a self-selected slow and fast

speed, with a two minute rest between conditions.

a) b)

Figure 9. Illustration of the two static positions of the STS transition. a) In-vivo capturing of the human body. b) Link-segment model from SMAS.

VICON Nexus software was used to reconstruct the three-dimensional (3D)

position of the markers, in order to identify and label each marker according to its

anatomical location (Figure 10a). During reconstruction gaps in individual marker

trajectories were detected and filled in order to obtain a smooth trajectory, which


45

was then exported to a text file (*.txt). Once reconstructed, all markers were

labelled to allow construction of the link-segment model (Figure 10b).

a) b)

Figure 10. Data pre-processing. a) Virtual reconstruction on VICON Nexus System. b) Markers identified and labelled according their anatomical position.

3.3. Data Processing

3.3.1. Biomechanical Model Creation and Kinematics Data Analysis

The first step involved utilising the Salford Motion Analysis System (SMAS) [125-

128] to create a 3D link-segment biomechanical model and obtain kinematic

information. SMAS is a MATLAB (MathWorks Inc., Cambridge, UK) based

software package for undertaking kinematic analysis of biomechanical multi-

segment systems using marker-based photogrammetric devices. It utilises a least

square method to estimate the position and orientation of the body segments based

on the technical marker data and the calibrated anatomical system technique

(CAST) protocol to enable mapping between technical and anatomical frames

[129].

Data from the static and dynamic calibration procedures and the STS trials for the

slow, normal and fast speed output from VICON Nexus software were input into


46

SMAS where marker coordinates were processed using a Butterworth 4th order zero

phase lag, low-pass filter with a cut-off frequency of 6Hz. In SMAS, the anatomical

and virtual landmark and functional joint centre identification data obtained from

the calibration procedure were used to create a 3D link-segment model for each

participant. This model consisted of thirteen segments: head, trunk, pelvis, right and

left upper-arms, lower-arms, thighs, shanks and feet as previously showed in Figure

9.

Using SMAS, the positions of the Centre of Mass (CoM) of each segment of the

model were calculated, using the Equation 1:

…….…………. Equation 1

Where: is the centre of mass position vector of the “ith” segment; is the

proximal point radius from the origin of the global coordinate system; mi is the

relative proportion of the “ith” segment mass with respect to the total body mass;

and is the distal point radius vector from the origin of the global coordinate

system.

The position vector for the WB CoM was calculated from the Equation 2:

…………………. Equation 2

Where: is the whole-body centre of mass position vector; is the

centre of mass position vector of the “ith” segment; mi s the relative proportion of

the “ith” segment mass with respect to the total body mass; and M is the total body

mass of the participant.

The technical marker data from the STS trials were then associated with the 3D

model enabling kinematic information to be calculated. Linear and angular velocity,

linear and angular acceleration and angular momentum were calculated for the

CoM of the WB and the CoM of each segment throughout each STS trial.


47

Functional joint centre locations during the STS trials were estimated using

standard kinematic expressions [24].

3.3.2. Analysis of Kinematic Data, Principal Component Analysis and

Kinetic analysis

Having obtained the kinematics a suite of in house MATLAB based routines was

used to perform time and magnitude normalization, descriptive statistical analysis

of the kinematic data, a PCA of the momentum variables [130-132], and a kinetic

analysis of the joint torque, power and work during the STS trials.

For the kinetic analysis, biomechanical model kinematic data obtained from the

SMAS software were input into the MatLab routines along with the data obtained

from the force plates during the STS transition trials, (the GRF and CoP location).

During the STS trials, the hands were placed on the waist in order to minimize the

effect of upper-limb segment movement during the STS transition. Since the lower

limbs are the main segments of the body that are active during this motion, the

head, arms and trunk were combined and considered as a single segment (HAT)

resulting in a 3D biomechanical model consisting of 8 segments as shown in Figure

11.

A) HAT segment B) waist joint C) pelvis segment D) hip joints E) thigh segments F) knee joints G) shank segments H) ankle joints I) feet segments

Figure 11. Illustration of static sitting position of the eight link-segment model.


48

Subsequently the linear momentum of the Whole Body CoM and each of the eight

body segments was calculated using Equation 3.

…………..…….……. Equation 3

Where: is the linear momentum vector of the “ith” segment, mi is the relative

proportion of the “ith” segment mass with respect to the total body mass, and is

the linear velocity vector of the “ith” segment CoM.

3.3.3. Time and Magnitude Normalization

In order to facilitate analysis of the variables and comparison between participants,

the kinematic data obtained from the STS trials were normalized with respect to

time. The time normalization procedure consisted of determining the point in the

STS transition corresponding to the beginning of movement, when the magnitude

of the resultant WB CoM velocity vector was still zero. This was taken as the

starting point in the STS transition (0% of the movement). The end of the STS

transition (100% of the movement) was taken to be the point where the magnitude

of the resultant WB CoM velocity vector was again zero, corresponding to the

participant being in the standing position.

In addition, magnitude normalization of the linear and angular momentum variables

was performed in order to obtain dimensionless momenta and allow comparison of

the data between participants. Linear momentum was normalized with respect to

the product of the body mass of the participant and the mean vertical velocity of the

WB CoM for each trial. Angular momentum was normalized with respect to the

product of the body mass of the participant, the mean vertical velocity of the WB

CoM and the final displacement vector of the WB CoM for each trial.


49

Once the normalization had been performed descriptive statistics were calculated

for the kinematic data variables for the ten STS trials undertaken by each

participant at each of the three speeds considered, normal, slow and fast.

3.3.4. Principal Component Analysis Principal Component Analysis (PCA) may be used in the analysis of human

movement to partition the variance in the data into independent orthogonal axes

allowing the identifications of the important combinations of variable in the time

series data [130, 133, 134]. PCA was performed on the momentum variables for the

WB and for each of the body segments in order to assess any gross and segmental

differences between the three speeds considered. Tuning and weighting coefficients

and variances for each component were obtained and mean and standard deviation

values calculated for each participant [131, 132].

3.3.5. Kinetics

The MatLab routines were also used to perform a kinetic data analysis for the STS

trials. Using functional joint centre positions obtained from the SMAS software, in

conjunction with the GRF and CoP location data obtained from the force plates, the

joint force, torque, power and work developed at each lower limb joint in the STS

trials was calculated. Firstly, the functional joint centre position data were time

normalised using the same time normalization procedure that was utilised for the

kinematic data. Next, the raw GRF and CoP data were filtered using a 4th order

zero-lag, low pass Butterworth filter with a cut-off frequency of 200 Hz.

The data were transformed into the global coordinate frame and resampled to obtain

the same sampling frequency as the kinematic data, and time normalized. Next, the

distances and angles between the CoM of the lower limb segments, functional joint

centre positions and CoP were calculated enabling joint forces, torques, power and


50

work to be estimated using the inverse dynamics method and using the body-free

diagram showed in the Figure 11 [24, 30, 135].

The torque, power and work data from the seat-unload point (35% of the STS

transition cycle) were calculated prior to normalization. Additionally that the data

was estimated from the seat-unload point due to the data obtained from the forces-

plates before this point is not real because the participant is sitting on the chair and

the force-plates were not able to measure that force.

The mathematical expressions to calculate kinetic variables force, torque, power

and work are as follows in the Equation 4, Equation 5, Equation 6, and Equation 7.

Force

………………………. Equation 4

Where: is the resultant force vector of the “jth” joint; mi is the relative proportion

of the “ith” segment mass; is the linear acceleration vector of the “ith” segment

CoM; n is the number of forces that are acting on the “ith” segment; Fn are the force

vectors acting on the “ith” segment.

Torque

……………. Equation 5

Where: τj is the resultant torque vector about the “jth” joint; Ii is the moment of

inertia of the “ith” segment; αi is the angular acceleration vector of the “ith”

segment; s is the number of torques acting on the “ith” segment; Ms are the torque

vectors acting on the “ith” segment; n is the number of force vectors that are acting

on the “ith” segment; Fn are the force vectors acting on the “ith” segment; dn is the

radial distance to the point where the force is applied.


51

Power

…..................... Equation 6

Where: Pj is the power of the “jth” joint; τj is the torque about the “jth” joint; ωd is

the angular velocity of the adjacent distal segment and ωp is the angular velocity of

the adjacent proximal segment.

Work

……………….….. Equation 7

Where: Wj is the work done for the “jth” joint; t2 is time point of the movement end;

t1 is time point of the movement start; Pj is the power at the “jth” joint and dt is the

derivate with respect to time.

3.4. Summary

This methodology has been helped to analyse the human motion in Manchester

University Gait-lab’s of the Pariser Building. Figure 12 shows the methodology

used to estimate and analyse the movement on both groups of participants.

This methodology was used to analyse the STS transition was by motion caption,

which was obtained directly from the participants. An individual open-chain model

is required in order to obtain the anthropometric data from each participant.

Kinematics analysis was estimated to WB and segments CoM, principal component

analysis was applied to the momenta variables.


52

Figure 12. Flow Chart showing the Methodology used to estimate the STS transition

motion.

Also a kinetics analysis was estimated for the body joints in order to compare firstly

between speeds for each group, and then between performances of the two groups.


53

Chapter 4. Biomechanical Analysis Comparison of Sit-to-Stand

Transition Performance between Younger and Older Participants

4.1. Introduction

In this chapter the biomechanical performance of the two groups of healthy,

younger and older participants (n=10, average age 28±2 and 56±8 years old,

respectively) is analysed during STS transition trials undertaken at three self-

selected speeds (slow, normal and fast). Then in-vivo data obtained from a motion

capture system and force plates during the trials is applied to a link segment

biomechanical model facilitating both a kinematic and kinetic analysis of the STS

activity enabling the contribution of the major body segments and joints to the STS

transition to be elucidated. In addition, PCA is applied to the whole body and

segmental momentum components in order to identify the more significant the

more significant momentum variables and to determine any gross and segmental

level differences in these variables between the speeds.

In particular the approach taken consisted of:

a. Estimating the momentum components of the CoM of the WB in order to

quantify the amount of movement in the human body.

b. Applying PCA to the normalized WB CoM momentum components in order

to determine the contribution of momentum variables that maximise the

variance explained in each condition.

c. Where PCA showed differences in the momentum components of the WB

CoM between the speeds PCA was applied at the segmental level to help

identify the segment(s) in which the difference originated.

Although the differences in the performance in the STS activity between younger

and older people have been extensively studied, concluding that younger people


54

perform STS easily with a minimum risk of losing balance and falling, whereas for

older people the risk of losing balance is a greater and consequently the risk of fall

during the STS transition is higher, a firm methodology has not been developed for

analysing, differentiating and identifying where the differences in the strategies

associated with age and speed originate. For that reason this research is focused in

identify and analyse the similarities and differences in the strategies employed by

the two groups of participants when undertaking the activity at different speeds.

4.2. Whole-Body Centre of Mass Kinematics

In this section linear and angular momentum results are reported for the WB CoM

since these variables explain the translational and rotational motion of the WB and

the segments during the STS transition. The linear and angular momentum data

combined with the calculation of the position of the centre of mass enabled the

phases of the STS transition to be determined. The phases: static sitting, initiation,

ascent, stabilization and static standing are shown in Figure 13 and are in agreement

with those in the literature [1, 84, 116, 136].

a) b)

Figure 13. Translational motion of the whole-body centre of mass along sit-to-stand transition. a) Three dimensional plot of the whole-body centre of mass position in a sagittal

plane during the STS transition. b) Representative STS transition cycle defining relevant time points.


55

In addition, highlighted in the plots presented in this section are the two important

points in the STS transition, namely:

1) Seat-unload, representing the point at which the initiation phase ends and

the ascending phase begins, which occurs approximately 35% of the way through

STS transition; and

2) Standing, representing the point at which the ascending phase finishes

and the stabilization phase begins, which occurs at approximately 60% of the way

through the STS transition.

Results are presented for the STS trials undertaken by the participants at slow,

normal and fast speeds. The velocity of the participants’ performance is showed in

the Figure 14. Which also shows the average for each velocity: 0.05 ±0.01 for the

slow speed, 0.08 ±0.01 for the normal speed, and 0.13 ±0.01 and 0.12±0.01, for

younger and older participants, respectively at fast speed.

Figure 14. Average velocities of the whole-body centre of mass from whole participants.


56

For each velocity, the mean and standard deviation (SD) for the variables of interest

were calculated first for each participant over ten trials and then the overall mean

and SD were calculated for all the participants. The results are presented for the AP

(global X), vertical (global Y) and ML (global Z) axes as shown in Figure 9.

The kinematics results seem to be similar performance, for both groups of

participants, in CoM of the both WB and segments. Following comparisons of the

results are based on the difference in the performance of the older participants with

respect to the younger participants.

4.2.1. Kinematic Analysis Comparison of the Whole-Body Centre of

Mass for self-selected Slow, Normal and Fast Speeds

The Figure 15a shows the slow position of the WB CoM, which presented

differences attributable the increase stature younger participants compared with the

older participants.

The WB CoM normalised linear momentum in older participants presented

insignificant differences on all the axes at three speeds in compassion with the

younger participants as shown in Figure 15b.

The WB CoM normalized angular momentum showed no variation about the AP

and vertical axes at the three speeds, the normalised angular momentum about the

ML axis showed a clear difference at fast speed. At fast speed, the older

participants showed the greater peak normalized angular momentum about the ML

axis before seat-unload and on the ascending phase (Figure 15c).


57

Figure 15. Comparison of trajectories of the whole-body centre of mass of younger and

older healthy participants at three different speeds. a) WB CoM position, b) WB CoM linear momentum, c) WB CoM angular momentum.

4.2.2. Kinematic Analysis Comparison of the Centre of Mass’ Segments

for self-selected at Slow, Normal and Fast Speeds.

The results from the kinematic analysis on the segments indicated that the

normalised linear momentum of the HAT, pelvis, thigh, shank and foot segments

were similar at three speeds for both younger and older participants. HAT exhibited

the dominant normalised linear and angular momentum.

The normalised angular momentum of the HAT segment at slow and fast speeds,

showed no difference between groups. However, at fast speed the older participants

exhibited greater normalised angular momentum than the younger participants

about the ML axis as shown in Figure 16.


58

Figure 16. Comparison of the HAT centre of mass angular momentum at fast speed of

younger and older healthy participants.

The segments that showed the most substantial normalised angular momentum

about the three axes were the thighs and shanks segments.

The older participants’ thigh and shank segments showed greater peak normalised

angular momentum about the three axes than the younger participants (at all the

speeds). The normalised angular momentum about the AP and vertical axes for

contralateral joint tended to cancel each other out. Figure 17 shows the normalised

angular momentum of the thigh and shank segments about the ML axis. The

increased normalised angular momentum illustrates that the older participants relied

more on segment rotation to accomplish the STS transition.


59

Figure 17. Comparison of the angular momentum of the thigh and shank segments of

younger and older healthy participants at three different speeds. a) Thigh segments analysis, b) Shank segment analysis.

4.2.3. Kinetic Analysis Comparison between the Joint’s Centres of

Younger and Older Participants

The kinetic analysis of the lower limbs showed that the torque, power and work at

the joints had a similar trend in both participants’ groups. However, despite the

general similarities, the kinetics analysis showed some differences in the magnitude

of the variables.


60

Waist Joint

The normalised torque at the waist was minimal in comparison with the other joints

but similar in both participant groups at the three speeds. Figure 18 shows the

comparison between younger and older participants kinetic analysis of the waist

joint. The waist torque about AP and vertical axes are insignificant in comparison

with the torque performed about ML axis at all the speeds. Even though its small

contribution, the older participants exhibited an increment in the torque about the

ML axis during the stabilization phase mainly at slow speed (Figure 18a).

The waist joint power presented a higher amount for the older participants mainly

in the ascending phase of the STS transition cycle (Figure 18b), which indicates

that older participants could rely more on the joint waist power in order to extend

the HAT (upper-body).

Figure 18. Comparison of the kinetic analysis result of the waist joint centre of younger

and older healthy participants at three different speeds. a) Waist joint centre torque, b) Waist joint centre power.


61

The result of the analysis of the work performed by the waist joint did not present

any change either in trend or amount at the three speeds in both participants’

groups, obtaining the minimum work done during the STS transition cycle in

comparison to the others lower limb joints.

Hip Joints

The normalised torques about the AP and vertical axes tended to cancel each other.

The normalised torque at the hip joints about the ML axis is the one that contributes

to the STS transition. This torque was similar at the beginning of the STS transition

in both participants’ groups at the three speeds. However, once the transition

reached the stabilization phase the older participants exhibited an increment in all

the cases as shown in Figure 19a.

The hip joints presented a decrement of power at slow and normal speeds in the

older participants. At fast speed presented a similar amount of peak power for the

younger and older participants. However, the power exhibited a decrement at 35%

of the transition cycle in the older participants at the three speeds (Figure 19b). In

general the muscles action between the younger and older participants was doing

similar things, functioning concentrically in order to extend the upper-body.

In regards to the hip joints work, the older participants presented a substantial

decrement on the amount at the three speeds, in comparison to the younger

participants as shown in Figure 19c.


62

Figure 19. Comparison of the kinetic analysis result of the hip joints centre of younger

and older healthy participants at three different speeds. a) Hip joints centre torque, b) Hip joints centre power, c) Hip joints centre work done.


63

Knee Joints

Same as the in the hip joints, the torques about AP and vertical axes are cancelled

each out with their counterpart. So that, the torque on the knee joints about the ML

axis, is the one that contributes to the STS transition. Torque on the knees about

ML axis was similar at the beginning of the STS transition cycle in both

participants’ groups but at the end of the stabilization phase it exhibited an

increment at all speeds as shown in Figure 20a. ???

The Figure 20b shows that the power produced by the knee joints presented a

similar trend in both participants’ groups at all speeds. The differences found were

that in the left joint at normal and fast speeds the older participants had a decrement

in the amount of power, whereas at slow speed presented the similar amount that

the younger participants. In the right joint at normal and fast speeds the power

remained similar for both groups and at slow speed presented an increment in the

amount of power. Same as in the hip joints, the muscles responsible of the knee

joints movement were doing similar things, functioning concentrically to extend the

pelvis and trunk with the purpose to start the ascending phase until reach the

standing position.

In regards to the knee joints work, the older participants in the left side presented a

substantial decrement on the amount of work at the three speeds, in comparison to

the younger participants. In the right side, for the older subjects the work done at

the knee joint remained similar that the work done by the younger participants in

normal and fast speeds and a little higher at sow speed as shown in the Figure 20c.

The variations between the left and the right sides for the work done and power

implies that the older participants had a tendency to lean more on the left side.


64

Figure 20. Comparison of the kinetic analysis result of the knee joints centre of younger

and older healthy participants at three different speeds. a) Knee joints centre torque, b) Knee joints centre power, c) Knee joints centre work done.


65

Ankle Joints

The torque in ankle joints, unlike in the other joints, was active at the end of the

STS transitions (Stabilization phase). Torque in ankle joints also have the same

behaviour than hip and knees of being cancelled about AP and vertical axes are

cancelled each out with their counterpart. So that, the torque on the ankle joints

about the ML axis, is the main contributor to the STS transition.

Figure 21a shows that the ankle joints torque about ML axis presented a similar

trend in both participants’ groups, however the older participants exhibited a

decrement in comparison with the younger group mainly during the ascension

phase at the three speeds.

The power in ankle joints got its peak point at the end of the ascending phase. It

was higher for the older participants at all speeds, being the highest variation at

normal speed as shown in Figure 21b.

Same as in the waist joint, the results of the kinetic analysis exhibited that the work

done by the ankle joints had not significant variations between both groups of

participants at any of the three speeds.


66

Figure 21. Comparison of the kinetic analysis result of the ankle joints centre of younger

and older healthy participants at three different speeds. a) Ankle joints centre torque, b) Ankle joints centre power, c) Ankle joints centre work done.


67

4.2.4. Comparison of the Principal Component Analysis applied to

Momenta Variables of the WB CoM for self-selected Slow, Normal and

Fast.

Based on the PCA results of the WB CoM, the strategies to perform the STS

transition were found different for both groups of age and also different at the three

speeds.

The execution of the STS transition of the older participants had similar strategy at

slow and normal speeds which was also similar to the strategy in younger

participants at slow speed, as is showing in the Figure 22a. This strategy presented

as significant variable the angular momentum (forward rotation). On the other

hand, the older participants, at fast speed, exhibited similar strategies to the younger

participants at both normal and fast speed. In this case the strategy relied on the

significance of the linear momentum (forward movement) to perform the STS

transition (Figure 22).


68

Figure 22. Comparison of the principal component analysis of the whole-body centre of

mass of younger and older healthy participants at three different speeds. a) First principal component (PC1) and Z-scores momentum analysis, b) Second principal component (PC2)

and Z-scores momentum analysis, c) Third principal component (PC3) and Z-score momentum analysis.


69

4.2.5. Comparison of the Principal Component Analysis applied to

Momenta Variables of the Centre of Mass Segments for self-selected

Slow, Normal and Fast Speeds

HAT Segment

The Figure 23 shows the comparison of the PCA applied to the HAT segment,

which presented the same differences that in the WB CoM. Older participants at

slow and normal speeds and younger participants at slow speed presented the same

strategy where the most significant variable was the angular momentum (forward

rotation). Moreover, the older participants at fast speed and younger at both normal

and fast speeds presented another strategy where the significant variable was the

linear momentum (forward movement). Noteworthy, younger participants were also

aware of the control of the lateral movements and rotations.

Figure 23. Comparison of the first principal component (PC1) and Z-score momentum

of the HAT segment momentum variables of younger and older healthy participants at three different speeds.


70

Pelvis Segment

The Figure 24 shows the comparison of the PCA applied to the pelvis segment.

Older participants at normal speed and younger participants at slow speed presented

the same strategy where the most significant variable was the vertical linear

momentum (upward movement). Moreover, the older participants at fast speed and

younger at both normal and fast speeds presented another strategy where the

significant variable was the linear momentum in AP axis (forward movement).

Noteworthy, younger participants at slow speed presented as important variable the

angular momentum about the ML axis (forward rotation).

Figure 24. Comparison of the first principal component (PC1) and Z-score momentum analysis of the pelvis segment momentum variables of younger and older healthy

participants at three different speeds.

Thigh Segments

The Figure 25 shows the comparison of the PCA applied to the thighs segments.

This analysis exhibited that the thigh segments did not present any significant

variations either between the groups’ ages or between the speeds. All the

participants presented that the main variable involved in the thighs during the STS

transition was the linear momentum in AP and vertical axes.


71

Figure 25. Comparison of the first principal component (PC1) and Z-score momentum analysis of the thighs segment momentum variables of younger and older healthy


Shank Segments

The Figure 26 shows the comparison of the PCA applied to the shanks segments.

This analysis exhibited that the shank segments presented variations between

strategies to perform the STS transition in both groups’ ages.

Older participants presented as main variable the angular momentum about the

vertical and ML axes at all speeds. While for the younger participants the main

variable was the angular momentum about the AP axis at all speeds.


72

Figure 26. Comparison of the first principal component (PC1) and Z-score momentum analysis of the shanks segment momentum variables of younger and older healthy


Feet Segments

The Figure 27 shows the comparison of the PCA applied to the feet segments. This

analysis exhibited that the feet segments did not present any significant variations

either between the groups ages and the speeds. All the participants presented that

the main variable involved in the thighs during the STS transition was the angular

momentum about vertical and ML axes.


73

Figure 27. Comparison of the first principal component (PC1) and Z-score momentum

analysis of the feet segment momentum variables of younger and older healthy participants at three different speeds.

4.2.6. Summary

Numerous studies have shown that STS transition has identified that the

performance is affected by pathological problems as well as by the ageing. It has

been noticed that the lifestyle and the daily diet that each person also can affect the

mobility.

In this study was found that there are no big differences between these two groups

of participants, it could be due to the both participants’ groups are healthy without

any musculoskeletal injury. The main pattern of the STS transition cycle looks very

similar in both groups, obtaining the same predominant variables on the kinematics

analysis as well as in their kinetics analysis. However there are slight changes on

their performance between the both groups and between the speeds. Also, it was

found by PCA that they use different type of strategy in order to rise, leave the

chair and stand without losing the balance.


74

Chapter 5. Conclusions and Discussion

This thesis has presented the analysis of the performance of the STS transition for

two different age groups: younger adults and older adults when the activity was

undertaken at three, self-selected speeds. The difference in performance at the three

speeds was assessed both within and between the two age groups considered. This

chapter presents the main conclusions from the analysis for each group as well as

for the comparative analysis, which compared the performance of one group to the

performance of the other.

5.1. Younger Participants Performance of the Sit-to-Stand Transition

The results from the kinetic analysis support the results of the PCA, which suggest

that a different strategy is utilised when performing the STS transition at slow

speed than at normal and fast speeds. Namely, at slow speed, the joint power

presents some disturbances in their pattern during the ascending phase, mainly in

the hip and knee joints. At slow speed the work done at the knee joints is less and

the standard deviation of the power at the hip and knee joints is higher. Indicating

that the hip joints’ increases the contribution at slow speed in order to help the knee

joints to perform the seat-unload and the ascending phase.

The most significant finding from the kinetic analysis is that the work at the hips

joints is greater at slow speed in comparison to the normal and fast speeds

indicating that the hip joints are responsible for rotating the trunk forward-

backward. This supports the findings of the PCA on the WB CoM, which indicates

that at slow speed the participants need more angular momentum about the ML axis

(Lz) to successfully perform the STS transition than at the normal and fast speeds.

In summary, this study has investigated the STS transition in order to determine if

there are any differences when the activity is undertaken at different speeds. In


75

addition, the investigation evaluated the segmental contributions to the STS

transition. The main finding of the investigation is that participants utilised a

different strategy for performing the STS transition at slow speed than at normal

and fast speeds, depending more on the generation of forward rotation than the

other speeds. It was found that the kinematics of the head, arms and trunk (HAT)

segment closely resembled that of the WB CoM, a finding that confirms that the

HAT is the dominant segment in determining the location and motion of the WB

CoM. In addition, it was determined that the ML angular momentum of the thigh

and shank segments is the major determinant of the motion of the HAT and WB

CoM. Furthermore, the knee and hip joints were found to generate the majority of

the power and torque required for the STS activity.

5.2. Older Participants’ Performance of the Sit-to-Stand Transition

Measurements of the ability in the older to move and balance the body are

correlated with the predictions to fall. Older people are subject to their physical

condition decreasing and this affect the mental confidence, which increases the risk

to fall. Knowing their mobility level helps to provide them with confidence in the

performance of the different activities not only in the STS transition.

This study has determined that the older subjects who participated in the study

utilised a different strategy when undertaking the STS transition at fast speed

compared at normal and slow speeds. When undertaken at normal and slow speeds

the STS transition depended more on the generation of forward rotation than at fast

speed. It was also found that the HAT segments made the greatest contribution to

the WB CoM movement during the STS activity.

The performance at normal speed seems to be more practiced by the older

participants than the others speeds, although it has been noticed by PCA that the

older participants use the same strategy at slow and normal speeds to stand-up.


76

5.3. Conclusions of the Sit-to-Stand Transition Comparison

Performance between Younger and Older Participants

Numerous studies have shown that STS transition has identified that the

performance is affected by pathological problems as well as by ageing. It has been

noticed that the lifestyle and the daily diet that each person has can also affect their

mobility.

However this study found that whilst there are no big differences between the two

groups of participants, it should be taken into account that both groups were healthy

and did not exhibit any musculoskeletal injuries. The performance trend of the STS

transition cycle looked very similar in both groups, obtaining the same predominant

variables on the kinematics analysis as well as in their kinetics analysis. However

slight changes were found in their performance in both groups at different speeds.

The PCA also found that both groups used different types of strategy in order to

rise, leave the chair and stand, without losing the balance.

5.3.1. Principal Component Analysis of the Whole-Body Centre of Mass

The PCA analysis found that the WB CoM performed a different strategy on

younger participants at slow speed in comparison with their performance at normal

and fast speeds. The PCA suggested that at slow speed younger participants bend

their CoM forward more than in the others speeds, which is confirmed by the

segmental and the kinetics analysis.

The PCA on older participants found that the WB CoM performed a different

strategy at fast speed in comparison with their performance at normal and slow

speeds. The PCA suggested that during the WB CoM transition at normal and slow


77

speeds older participants bend and transfer their CoM forward more than at fast

speed. This is confirmed by the segmental and kinetics analysis.

The comparative results were confirmed because the kinematic analysis on the

segments showed that the angular momentum of the older participants increased

with respect to the angular momentum of the younger participants.

5.3.2. Principal Component Analysis applied to Momenta Variables of

the Segments Centre of Mass

The PCA on the HAT segment was a significant outcome to find the strategy used

by the participants, due to the main differences identified in the analysis of both

groups of participants. Whereas the analysis of the remaining segments showed

similar results. So, it can be concluded that the HAT is the segment which the body

needs to deal with during the transition.

The PCA of the HAT confirmed that the strategies used for the WB CoM required

more angular momentum at slow speed for the younger participants, and at slow

and normal speeds for the older participants. It means that the trunk was more bent

forward at the mentioned speeds.

The PCA of the pelvis segment showed that there was no clear pattern between

their principal components which indicated that pelvis did not have an important

contribution for the STS transition. Concluding that pelvis only helped to transmit

and stabilize the movement from the lower limbs to the HAT segment.

The PCA of the thigh segments showed similar performance in both participants’

groups. The thigh segments had a similar strategy to move, they focused their

performance on the forward and upward movements.

The PCA on the shanks showed that they changed their strategy to move in both of

the participants’ groups. The younger participants’ group focussed on the internal-


78

external and forward rotations, whilst for the older the shanks were more focussed

on the lateral rotations.

According to the statements previously written, the thigh segments are responsible

for moving the trunk and pelvis forwards and upwards. While the shank segments

are responsible for helping the thigh segments to move and balance the upper-body.

The PCA on the feet showed that they were focussed on the internal-external

rotation in both groups of participants and at all speeds. Concluding, that the feet

are the responsible for stabilising the movement.

5.3.3. Kinetic Analysis of the Joints

With the kinetic analyses the results from the PCA were confirmed, as the results of

both of the participants’ groups were similar. From the waist kinetic analysis it was

found that the pelvis segment could be considered to be a connection that allows the

transfer of energy from the lower limbs to the upper-body.

The kinetic analysis showed that torques, power, and work from the knee and hip

joints were responsible for generating almost all the momentum and power

necessary for the seat-unload point. After the seat-unload the hip joints’ torques

rotated the HAT and pelvis forwards, while the knee joints’ torques contributed to

rotate the thigh backwards, and therefore the pelvis and HAT as well. Thus

generating the forward-backward angular momentum needed to push forward the

pelvis and HAT segments in order to leave the chair and therefore the upward linear

momentum.

The higher hip torque at the end of the cycle identified that the older participants

needed a bigger break torque in order to get the stabilization of the body.

The ankle joints’ torques, at the end of the STS transition, indicated that they are

responsible for dissipating the angular momentum generated by the hip and knees


79

earlier in the movement. Therefore the shank segments connect the thigh with the

feet segments in order to transfer the energy between them. Regarding the ankle

joints’ power, it is active all along the STS transition indicating that ankle joints

also help in stabilising the WB CoM including during the standing posture.

The feet are the base of support after participants leave the chair, hence that the

kinematic of the foot segments showed an insignificant activity.

This study helped to quantitatively and descriptively recognizes the broad range of

strategies that people use to perform the same task. The main application of

individual STS performance studies is to improve the ability to move in order to

obtain physical independence.

5.4. Future Work

In this work there are some scopes that can be added to improve the analysis of the

human movement. Due to kinetics and kinematics analysis only was done in this

study. From previous studies was expected to find a significant changes in the

comparison between the younger and older participants performance. Therefore the

slightly changes found, must to be clarified with the help of other technologies such

as electromyography and ultrasound.

Because the electromyography helps to measure the muscle response activity when

the muscle is active during the activity. And the ultrasound scan and produce

images of the musculoskeletal tissues. This can help to project muscle activation

during the activity, and so find abnormal muscular activation.


80

References

1. Nuzik, S., et al., Sit-to-stand movement pattern. A kinematic study. Physical Therapy, 1986. 66(11): p. 1708-1713.

2. Schenkman, M., et al., Whole-body movements during rising to standing from sitting. Physical Therapy, 1990. 70(10): p. 638-648.

3. Riley, P.O., D.E. Krebs, and R.A. Popat, Biomechanical analysis of failed sit-to-stand. IEEE Transactions on Rehabilitation Engineering, 1997. 5(4): p. 353-359.

4. Roberts, P.D. and G. McCollum, Dynamics of the sit-to-stand movement. Biological Cybernetics, 1996. 74(2): p. 147-157.

5. Papa, E. and A. Cappozzo, Sit-to-stand motor strategies investigated in able-bodied young and elderly subjects. Journal of Biomechanics, 2000. 33(9): p. 1113-1122.

6. Mazza, C., M. Zok, and U. Della Croce, Sequencing sit-to-stand and upright posture for mobility limitation assessment: determination of the timing of the task phases from force platform data. Gait and Posture, 2005. 21(4): p. 425-431.

7. Allin, S. and A. Mihailidis, Sit to stand detection and analysis, in AI in Eldercare: New Solutions to Old Problems: Papers from the AAAI Fall Symposium. 2008, Association for the Advancement of Artificial Intelligence: Toronto. p. 1-3.

8. Beninato, M., L.G. Portney, and P.E. Sullivan, Using the international classification of functioning, disability and health as a framework to examine the association between falls and clinical assessment tools in people with stroke. Physical Therapy, 2009. 89(8): p. 816-825.

9. Boulgarides, L.K., et al., Use of clinical and impairment-based tests to predict falls by community-dwelling older adults. Physical Therapy, 2003. 83(4): p. 328-339.

10. Hatch, J., K.M. Gill-Body, and L.G. Portney, Determinants of balance confidence in community-dwelling elderly people. Physical Therapy, 2003. 83(12): p. 1072-1079.

11. Pai, Y.C., et al., Control of body centre of mass momentum during sit-to-stand among young and elderly adults. Gait and Posture, 1994. 2(2): p. 109-116.

12. World Health Organization. Ageing and Life Course Unit., WHO global report on falls prevention in older age. 2008, Geneva, Switzerland: World Health Organization. iv, 47 p.

13. Blomqvist, S., et al., Test-retest reliability, smallest real difference and concurrent validity of six different balance tests on young people with mild to moderate intellectual disability. Physiotherapy, 2012. 98(4): p. 313-319.

14. Rubenstein, L.Z., Falls in older people: epidemiology, risk factors and strategies for prevention. Age and Ageing, 2006. 35(2): p. ii37-ii41.

15. Masud, T. and R.O. Morris, Epidemiology of falls. Age and Ageing, 2001. 30(4): p. 3-7.


81

16. Schwenk, M., et al., Test-retest reliability and minimal detectable change of repeated sit-to-stand analysis using one body fixed sensor in geriatric patients. Physiological Measurement, 2012. 33(11): p. 1931-1946.

17. Feland, J.B., R. Hager, and R.M. Merrill, Sit to stand transfer: performance in rising power, transfer time and sway by age and sex in senior athletes. British Journal of Sports Medicine, 2005. 39(11): p. 1-2.

18. Baer, G.D. and A.M. Ashburn, Trunk movements in older subjects during sit-to-stand. Archives of Physical Medicine and Rehabilitation, 1995. 76(9): p. 844-849.

19. Fotoohabadi, M.R., E.A. Tully, and M.P. Galea, Kinematics of rising from a chair: image-based analysis of the sagittal hip-spine movement pattern in elderly people who are healthy. Physical Therapy, 2010. 90(4): p. 561-571.

20. Gilleard, W., J. Crosbie, and R. Smith, A longitudinal study of the effect of pregnancy on rising to stand from a chair. Journal of Biomechanics, 2008. 41(4): p. 779-787.

21. Cignetti, F., et al., Updating process of internal models of action as assessed from motor and postural strategies in children. Neuroscience, 2013. 233: p. 127-138.

22. Pai, Y.C. and M.W. Rogers, Segmental contributions to total body momentum in sit-to-stand. Medicine and Science in Sports and Exercise, 1991. 23(2): p. 225-230.

23. Bieryla, K.A., D.E. Anderson, and M.L. Madigan, Estimations of relative effort during sit-to-stand increase when accounting for variations in maximum voluntary torque with joint angle and angular velocity. Journal of Electromyography and Kinesiology, 2009. 19(1): p. 139-144.

24. Winter, D.A., Biomechanics and motor control of human movement. 4 th ed. 1930, New Jersey: John Wiley & Sons Inc. 370.

25. Lee, S.H., Biomechanical modeling and control of the human body for computer animation, in Computer Science. 2008, University of California: Los Angeles, California. p. 143.

26. Freivalds, A., Biomechanics of the upper limbs. First ed. 2000, United States of America: CRC Press. 564.

27. Enderle, J.D. and J.D. Bronzino, Introduction to biomedical engineering. 3 rd ed. Biomedical Engineering, ed. Third. 2012, United Kingdom: Elsevier Inc. 1253.

28. Nordin, M. and V.H. Frankel, Basic biomechanics of the musculoskeletal system. 4 th ed. 2012, China: Wolters Kluwer. 454.

29. Andriacchi, T., P. and E.J. Alexander, Studies of human locomotion: past, presente and future. Journal of Biomechanics, 2000. 33: p. 1217-1224.

30. Robertson, D.G., et al., Research methods in biomechanics. 1 st ed. 2004, United States of America: Human Kinetics. 308.

31. Cael, C., Functional anatomy. 2010, Baltimore: Wolters Kluwer. 446. 32. Kaya, B.K., D.E. Krebs, and P.O. Riley, Dynamic stability in elders:

momentum control in locomotor ADL. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 1998. 53(2): p. 126-134.

33. Knudson, D., Fundamentals of biomechanics, ed. Second. 2007, California: Springer. 319.

34. Nordin, M. and V.H. Frankel, Biomecanica basica del sistema musculosqueletico, ed. Tercera. 2004, Madrid: Mc Graw Hill. 324.


82

35. Peterson, D.R. and J.D. Bronzino, Biomechanics principles and applications. 2008: CRC Press. 349.

36. Cappozzo, A., Gait analysis methodology. Human Movement Science, 1984. 3: p. 24-50.

37. Kerr, A., Introductory biomechanics. 2010, Glasgow: Elsevier. 150. 38. Mow, V.C. and R. Huiskes, Basic orthoapedic biomechanics and mechano-

biology. Third ed, ed. Third. 2005, Philadelphia: Lippincott Williams and Wilkins. 736.

39. Marieb, E.N. and K. Hoehn, Human anatomy and physiology, ed. Seventh. 2007: Pearson Education Inc. 1264.

40. Jones, F.P., et al., Quantitative analysis of abnormal movement: the sit-to-stand pattern. American Journal of Physical Medicine, 1963. 42: p. 208-218.

41. Wheeler, J., et al., Rising from a chair. Influence of age and chair design. Physical Therapy, 1985. 65(1): p. 22-26.

42. Aggarwal, J.K. and Q. Cai, Human motion analysis: a review. Computer Vision and Image Understanding, 1999. 73(3): p. 428-440.

43. Hikosaka, O. and M. Isoda, Brain mechanisms for switching from automatic to controlled eye movements. Progress in Brain Research, 2008. 171: p. 375-382.

44. Wiener, J.M., et al., Measuring the activities of daily living: comparisons across national surveys. Journal of Gerontology, 1990. 45(6): p. S229-S237.

45. Kerr, K.M., et al., Analysis of the sit-stand-sit movement cycle in normal subjects. Clinicla Biomechanics, 1997. 12(4): p. 236-245.

46. Van der Heijden, M.M., et al., Muscles limiting the sit-to-stand movement: an experimental simulation of muscle weakness. Gait and Posture, 2009. 30(1): p. 110-114.

47. Etnyre, B. and D.Q. Thomas, Event standardization of sit-to-stand movements. Physical Therapy, 2007. 87(12): p. 1651-1666.

48. Kerr, K.M., et al., Analysis of the sit-stand-sit movement cycle in normal subjects. Clinical Biomechanics, 1997. 12(4): p. 236-245.

49. Janssen, W.G., H.B. Bussmann, and H.J. Stam, Determinants of the sit-to-stand movement: a review. Physical Therapy, 2002. 82(9): p. 866-879.

50. Kerr, K.M., et al., Standardization and definitions of the sit-stand-sit movement cycle. Gait and Posture, 1994. 2(3): p. 182-190.

51. Saint-Bauzel, L., et al., Pathological sit-to-stand predictive models for control of a rehabilitation robotic device. IEEE International Symposium on Robot and Human Interactive Communication, 2007. 1-3(16th): p. 1166-1171.

52. Whiting, W.C. and S. Rugg, Dynatomy: dynamic human anatomy. 2006: William C. Whiting and Stuart Rugg. 246.

53. Gillette, J.C. and C.A. Stevermer, The effects of symmetric and asymmetric foot placements on sit-to-stand joint moments. Gait Posture, 2012. 35(1): p. 78-82.

54. Akram, S.B. and W.E. McIlroy, Challenging horizontal movement of the body during sit-to-stand: impact on stability in the young and elderly. Journal of Motor Behavior, 2011. 43(2): p. 147-153.

55. Mazza, C., et al., Association between subject functional status, seat height, and movement strategy in sit-to-stand performance. Journal of the American Geriatrics Society, 2004. 52(10): p. 1750-1754.


83

56. Ricard, P.E.H., J.C. Paz, and M.P. West, Chapter 23 - Functional Tests, in Acute care handbook for physical therapists. 2014, W.B. Saunders: St. Louis. p. 471-483.

57. Raad, J., et al., A brief review of the activities-specific balance confidence scale in older adults. Archives of Physycal Medicine and Rehabilitation, 2013. 94: p. 1426-1427.

58. Novy, D.M., M.J. Simmonds, and C.E. Lee, Physical performance tasks: what are the underlying constructs? Archives of Physical Medicine and Rehabilitation, 2002. 83(1): p. 44-47.

59. Tiedemann, A., et al., The comparative ability of eight functional mobility tests for predicting falls in community-dwelling older people. Age and Ageing, 2008. 37(4): p. 430-435.

60. Jones, S.E., et al., The five-repetition sit-to-stand test as a functional outcome measure in COPD. Thorax, 2013. 68(11): p. 1015-1020.

61. Christiansen, C.L. and J.E. Stevens-Lapsley, Weight-bearing asymmetry in relation to measures of impairment and functional mobility for people with knee osteoarthritis. Archives of Physical Medicine and Rehabilitation, 2010. 91(10): p. 1524-1528.

62. McMillan, A.G. and J.P. Scholz, Early development of coordination for the sit-to-stand task. Human Movement Science, 2000. 19(1): p. 21-57.

63. Tsutsumi, T., et al., Time course analysis of angular control of the body and head while rising from a chair. Acta Otolaryngologica, 2004. 124(7): p. 798-802.

64. Giansanti, D. and G. Maccioni, Physiological motion monitoring: a wearable device and adaptative algorithm for sit-to-stand timing detection. Physiological Measurement, 2006. 27(8): p. 713-723.

65. Seven, Y.B., N.E. Akalan, and C.A. Yucesoy, Effects of back loading on the biomechanics of sit-to-stand motion in healthy children. Human Movement Science, 2008. 27(1): p. 65-79.

66. Mong, Y., T.W. Teo, and S.S. Ng, 5-repetition sit-to-stand test in subjects with chronic stroke: reliability and validity. Archives of Physical Medicine Rehabilitation, 2010. 91(3): p. 407-413.

67. Buatois, S., et al., Five times sit to stand test is a predictor of recurrent falls in healthy community-living subjects aged 65 and older. Journal of the American Geriatrics Society, 2008. 56(8): p. 1575-1577.

68. Duncan, R.P., A.L. Leddy, and G.M. Earhart, Five times sit-to-stand test performance in Parkinson's disease. Archives of Physical Medicine and Rehabilitation, 2011. 92(9): p. 1431-1436.

69. Dubost, V., et al., Decreased trunk angular displacement during sitting down: An early feature of aging. Physical Therapy, 2005. 85: p. 404 - 412.

70. Galli, M., et al., Quantitative analysis of sit to stand movement: experimental set-up definition and application to healthy and hemiplegic adults. Gait and Posture, 2008. 28(1): p. 80-85.

71. Csuka, M. and D.J. McCarty, Simple method for measurement of lower extremity muscle strength. The American Journal of Medicine, 1985. 78(1): p. 77-81.

72. Guralnik, J.M., et al., A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of


84

mortality and nursing home admission. Journal of Gerontology, 1994. 49(2): p. M85-M94.

73. Shumway-Cook, A., S. Brauer, and M. Woollacott, Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Physical Therapy, 2000. 80(9): p. 896-903.

74. Ikeda, E.R., et al., Influence of age on dynamics of rising from a chair. Physical Therapy, 1991. 71(6): p. 473-481.

75. Pai, Y.-C., et al., Control of body centre of mass momentum during sit-to-stand among young and elderly adults. Gait and Posture, 1994. 2(2): p. 109-116.

76. Mourey, F., et al., Standing up from a chair as a dynamic equilibrium task: a comparison between young and elderly subjects. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 2000. 55(9): p. B425-B431.

77. McGibbon, C.A., D.E. Krebs, and D.M. Scarborough, Vestibulopathy and age effects on head stability during chair rise. Acta Otolaryngologica, 2001. 121(1): p. 52-58.

78. Lindemann, U., et al., Coordination of strength exertion during the chair-rise movement in very old people. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 2007. 62(6): p. 636-640.

79. Whitney, S.L., et al., Clinical measurement of sit-to-stand performance in people with balance disorders: validity of data for the five-times-sit-to-stand test. Physical Therapy, 2005. 85(10): p. 1034-1045.

80. Powell, L.E. and A.M. Myers, The activities-specific balance confidence (ABC) scale. The Journal of Gerontology A Biological Sciences and Medicine Science, 1995. 50A(1): p. M28-M34.

81. Yardley, L., et al., Development and initial validation of the Falls Efficacy Scale-International (FES-I). Age Ageing, 2005. 34(6): p. 614-619.

82. Lomaglio, M.J. and J.J. Eng, Muscle strength and weight-bearing symmetry relate to sit-to-stand performance in individuals with stroke. Gait and Posture, 2005. 22(2): p. 126-131.

83. Epifanio, I., et al., Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit-to-stand movement. Medical and Biological Engineering Computing, 2008. 46(6): p. 551-561.

84. Kralj, A., R.J. Jaeger, and M. Munih, Analysis of standing up and sitting down in humans: definitions and normative data presentation. Journal of Biomechanics, 1990. 23(11): p. 1123-1138.

85. Jeng, S.F., et al., Reliability of a clinical kinematic assessment of the sit-to-stand movement. Physical Therapy, 1990. 70(8): p. 511-520.

86. Shum, G.L., J. Crosbie, and R.Y. Lee, Effect of low back pain on the kinematics and joint coordination of the lumbar spine and hip during sit-to-stand and stand-to-sit. Spine, 2005. 30(17): p. 1998-2004.

87. Shepherd, R.B. and A.M. Gentile, Sit-to-Stand - Functional-Relationship between Upper-Body and Lower-Limb Segments. Human Movement Science, 1994. 13(6): p. 817-840.

88. Khemlani, M.M., J.H. Carr, and W.J. Crosbie, Muscle synergies and joint linkages in sit-to-stand under two initial foot positions. Clinical Biomechanics, 1999. 14(4): p. 236-246.


85

89. Lord, S.R., et al., Sit-to-stand performance depends on sensation, speed, balance, and psychological status in addition to strength in older people. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 2002. 57(8): p. M539-M543.

90. Nairn, B.C., S.R. Chisholm, and J.D.M. Drake, What is slumped sitting? A kinematic and electromygraphical evaluation. Manual Therapy, 2013: p. 1-8.

91. Munro, B.J., et al., A kinematic and kinetic analysis of the sit-to-stand transfer using an ejector chair: implications for elderly rheumatoid arthritic patients. Journal of Biomechanics, 1998. 31(3): p. 263-271.

92. Riley, P.O., et al., Mechanics of a constrained chair-rise. Journal of Biomechanics, 1991. 24(1): p. 77-85.

93. Pai, Y.C. and M.W. Rogers, Control of body mass transfer as a function of speed of ascent in sit-to-stand. Medicine and Science in Sports and Exercise, 1990. 22(3): p. 378-384.

94. Mourey, F., et al., A kinematic comparison between elderly and young subjects standing up from and sitting down in a chair. Age and Ageing, 1998. 27(2): p. 137-146.

95. Anglin, C. and U.P. Wyss, Arm motion and load analysis of sit-to-stand, stand-to-sit, cane walking and lifting. Clinical Biomechanics, 2000. 15(6): p. 441-448.

96. Gaul-Aláčová, P., et al., Analysis of the sitting-to-standing movement in variously demanding postural situations. 2003: Czech Republic. p. 57-64.

97. Sibella, F., et al., Biomechanical analysis of sit-to-stand movement in normal and obese subjects. Clinical Biomechanics, 2003. 18(8): p. 745-750.

98. Bernardi, M., et al., Determinants of sit-to-stand capability in the motor impaired elderly. Journal of Electromyography and Kinesiology, 2004. 14(3): p. 401-410.

99. Mathiyakom, W., et al., Modifying center of mass trajectory during sit-to-stand tasks redistributes the mechanical demand across the lower extremity joints. Clinical Biomechanics, 2005. 20(1): p. 105-111.

100. Scarborough, D.M., C.A. McGibbon, and D.E. Krebs, Chair rise strategies in older adults with functional limitations. Journal of Rehabilitation Research and Development, 2007. 44(1): p. 33-42.

101. Claeys, K., et al., Altered preparatory pelvic control during the sit-to-stance-to-sit movement in people with non-specific low back pain. Journal of Electromyography and Kinesiology, 2012. 22(6): p. 821-828.

102. Bohannon, R.W., Test-retest reliability of the five-repetition sit-to-stand test: a systematic review of the literature involving adults. Journal of Strength and Conditioning Research, 2011. 25(11): p. 3205-3207.

103. Crosbie, J., et al., Do people with recurrent back pain constrain spinal motion during seated horizontal and downward reaching? Clinical Biomechanics, 2013. 28(8): p. 866-872.

104. Kerr, A., et al., Measuring movement fluency during the sit-to-walk task. Gait & Posture, 2013. 37(4): p. 598-602.

105. Miletello, W.M., J.R. Beam, and Z.C. Cooper, A biomechanical analysis of the squat between competitive collegiate, competitive high school, and novice powerlifters. Journal of Strength and Conditioning Research, 2009. 23(5): p. 1611-1617.


86

106. Escamilla, R.F., et al., A three-dimensional biomechanical analysis of the squat during varying stance widths. Medicine and Science in Sports and Exercise, 2001. 33(6): p. 984-998.

107. Isear, J.A., Jr., J.C. Erickson, and T.W. Worrell, EMG analysis of lower extremity muscle recruitment patterns during an unloaded squat. Medicine and Science in Sports and Exercise, 1997. 29(4): p. 532-539.

108. Eng, J.J. and D.A. Winter, Estimations of the horizontal displacement of the total body centre of mass: considerations during standing activities. Gait and Posture, 1993. 1(3): p. 141-144.

109. Schultz, A.B., N.B. Alexander, and J.A. Ashton-Miller, Biomechanical analyses of rising from a chair. Journal of Biomechanics, 1992. 25(12): p. 1383-1391.

110. Janssen, W.G., et al., Validity of accelerometry in assessing the duration of the sit-to-stand movement. Medical and Biological Engineering and Computing, 2008. 46(9): p. 879-887.

111. Segal, N.A., et al., Association Between Chair Stand Strategy and Mobility Limitations in Older Adults With Symptomatic Knee Osteoarthritis. Archives of Physical Medicine and Rehabilitation, 2013. 94(2): p. 375-383.

112. Galumbeck, M.H., et al., The sit and stand chair. A revolutionary advance in adaptive seating systems. Journal of Long-Term Effects of Medical Implants, 2004. 14(6): p. 535-543.

113. DeSure, A.R., et al., An exercise program to prevent falls in institutionalized elderly with cognitive deficits: a crossover pilot study. Hawaii Journal of Medicine and Public Health, 2013. 72(11): p. 391-395.

114. Cuddihy, P.E., et al., Radar walking speed measurements of seniors in their apartments: technology for fall prevention, in 34th Annual international Conference of the IEEE Engineering in Medicine and Biology Society. 2012: San Diego, California USA p. 260-263.

115. Goossens, R.H. and C.J. Snijders, Design criteria for the reduction of shear forces in beds and seats. Journal of Biomechanics, 1995. 28(2): p. 225-230.

116. Aissaoui, R. and J. Dansereau. Biomechanical analysis and modelling of sit to stand task: a literature review. in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. 1999. Tokyo, Jpn: IEEE.

117. Van Lummel, R.C., et al., Automated approach for quantifying the repeated sit-to-stand using one body fixed sensor in young and older adults. Gait and Posture, 2013. 38(1): p. 153-156.

118. Guzman, R.A., et al., Differences in momentum development when standing up from a chair between elderly with and without frequent falls history. Revista Española de Geriatria y Gerontologia, 2009. 44(4): p. 200-204.

119. Fujimoto, M. and L.S. Chou, Dynamic balance control during sit-to-stand movement: an examination with the center of mass acceleration. Journal of Biomechanics, 2012. 45(3): p. 543-548.

120. Harrison, D.D., et al., Sitting biomechanics part I: review of the literature. Journal of Manipulative and Physiological Therapeutics, 1999. 22(9): p. 594-609.

121. Yoshioka, S., et al., Computation of the kinematics and the minimum peak joint moments of sit-to-stand movements. Biomed Eng Online, 2007. 6: p. 26.

122. Gross, J.M., J. Fetto, and E. Rosen, Musculoskeletal Examination. 2009, Chichester, West Sussex: Wiley-Blackwell. 463.


87

123. Whittle, M.W., Gait Analysis an Introduction, ed. Fourth. 2007, China: Elsevier. 255.

124. Yoo, B., S. Kim, and A. Merryweather, A technique for dynamic marker identification to locate occluded anatomical landmarks, in Conference of The American Society of Biomechanics 2011, T.A.S.o. Biomechanics, Editor. 2011. p. 1-2.

125. Ren, L., R. Jones, and D. Howard, Generalized approach to three-dimensional marker-based motion analysis of biomechanical multi-segment system, in International Society of Biomechanics XXth Congress. 2005: Cleveland, OH, USA. p. 235.

126. Zhao, G.R., et al., A customized model for 3D human segmental kinematic coupling analysis by optoelectronic stereophotogrammetry. Science China Technological Sciences, 2010. 53: p. 2947-2953.

127. Ren, L., R.K. Jones, and D. Howard, Whole body inverse dynamics over a complete gait cycle based only on measured kinematics. Journal of Biomechanics, 2008. 41(12): p. 2750-2759.

128. Ren, L., D. Howard, and L. Kenney, Computational models synthesize human walking. Journal of Bionic Engineering, 2006. 3: p. 127-138.

129. Cappozzo, A., et al., Position and orientation in space of bones during movement: anatomical frame definition and determination. Clinical Biomechanics, 1995. 10(4): p. 171-178.

130. Bennett, B.C., et al., Angular momentum of walking at different speeds. Human Movement Science, 2009. 29(1): p. 114-24.

131. Daffertshofer, A., et al., PCA in studying coordination and variability: a tutorial. Clinical Biomechanics, 2004. 19(4): p. 415-428.

132. Herr, H. and M. Popovic, Angular momentum in human walking. The Journal of Experimental Biology, 2008. 211: p. 467-481.

133. Robert, T., et al., Angular momentum synergies during walking. Experimental Brain Research, 2009. 197(2): p. 185-197.

134. Bennett, B.C., T. Robert, and S.D. Rossell, Angular momentum: insights into walking and its control, in International Conference on Intelligent Robots and Systems. 2011, IEEE: San Francisco, CA, USA. p. 3969-3974.

135. Zatsiorsky, V.M., Kinetics of human motion. 2002: Human Kinetics. 653. 136. McGibbon, C.A., et al., Instant of chair-rise lift-off can be predicted by foot-

floor reaction forces. Human Movement Science, 2004. 23(2): p. 121-132. 137. Jo, Y.N., M.J. Kang, and H.H. Yoo, Estimation of muscle and joint forces in

the human lower extremity during rising motion from a seated position. Journal of Mechanical Science and Technology, 2013. 28(2): p. 467-472.

138. Kim, S.M., et al., Study of Knee and Hip Joints' Moment Estimation by Biomechanical Simulation During Various Motion Changes. World Congress on Engineering and Computer Science, 2009. I and II: p. 785-788.

139. Schofield, J.S., et al., Characterizing asymmetry across the whole sit to stand movement in healthy participants. Journal of Biomechanics, 2013. 46(15): p. 2730-2735.

140. Jenkyn, T.R. and A.C. Nicol, A multi-segment kinematic model of the foot with a novel definition of forefoot motion for use in clinical gait analysis during walking. Journal of Biomechanics, 2007. 40(14): p. 3271-3278.


88

Appendix 1

Ethical Document


89


90

Appendix 2

Arrangement of the Anatomical Landmarks

Markers were placed by double side adhesive tape on the principal distal limbs of

the body of the participant with the aim to identify the principal body landmarks

and segments involved in the motion. Markers positions were based on the

international society of biomechanics (ISB) recommendations and previously

published studies [140]. The Table 2 and Table 3 show the name and position of

both types of markers.

Table 2. Technical Markers

No Body Segment Technical Markers Name 1 Head head_ant, head_post, head_Rlat, headLlat 2 Trunk ster_1, ster_2, ster_3, ster_4 3 Pelvis pel_1sr, pel_2, pel_3, pel_4 4 Right Humerus Rhum_1, Rhum_2, Rhum_3, Rhum_4 5 Right Forearm Rfarm_1, Rfarm_2, Rfarm_3, Rfarm_4 6 Left Humerus Lhum_1, Lhum_2, Lhum_3, Lhum_4 7 Left Forearm Lfarm_1, Lfarm_2, Lfarm_3, Lfarm_4 8 Right Thigh Rthg_1, Rthg_2, Rthg_3, Rthg_4 9 Right Shank Rleg_1, Rleg_2, Rleg_3, Rleg_4

10 Left Thigh Lthg_1, Lthg_2, Lthg_3, Lthg_4 11 Left Shank Lleg_1, Lleg_2, Lleg_3, Lleg_4


91

Table 3. Anatomical Markers Anatomical Landmark Description Anatomical

Landmark Description Head Right Femur

VERT Vertex, head cranial point RLEP Right lateral epicondyle RGON Right temporomandibular joint RMEP Right medial epicondyle LGON Left temporomandibular joint HJCR Right hip joint centre

Torso Right Shank PXIP Xiphoid process RHFB Right apex of fibula head IJUG Jugular notch RTTB Right tibial tuberosity C7SP Spinous process C7 RMML Right medial malleolus T8SP Spinous process T8 RLML Right lateral malleolus

Pelvis Right Foot RASIS Right anterior superior iliac spine CAR Right upper ridge of the calcaneus LASIS Left anterior superior iliac spine FMR Right dorsal of first metatarsal head RPSIS Right posterior superior iliac spine SMR Right dorsal of second metatarsal head LPSIS Left posterior superior iliac spine VMR Right dorsal of fifth metatarsal head

Right Humerus Left Femur RMHU Right medial humeral epicondyle LLEP Left lateral epicondyle RLHU Right lateral humeral epicondyle LMEP Left medial epicondyle GJCR Right glen humeral joint centre HJCL Left hip joint centre

Right Forearm Left Shank RRSY Right radial styloid LHFB Left apex of fibula head RUSY Right ulnar styloid LTTB Left tibial tuberosity EJCR Right elbow joint centre LMML Left medial malleolus

LLML Left lateral malleolus Left Humerus Left Foot

LMHU Left medial humeral epicondyle CAL Left upper ridge of the calcaneus LLHU Left lateral humeral epicondyle FML Left dorsal of first metatarsal head GJCL Left glen humeral joint centre SML Left dorsal of second metatarsal head

VML Left dorsal of fifth metatarsal head Left Forearm

LRSY Left radial styloid LUSY Left ulnar styloid EJCL Left elbow joint centre


92

Appendix 3

Data Process using Vicon Nexus Software

Using Vicon Nexus Software, markers will be detected and engraved frame by

frame in a text data, the 3D tracking is used during the motion, this process create

3D points from 2D rays capture and then sorts the points into trajectories. This

means that at least two cameras must to able to capture each marker to obtain a

continuous trajectory. Then the markers must to be identified, to accomplish the

labelling of each trial captured there are three labelling windows in Vicon software:

manual labelling, gap filling and pattern fill, like is showed in the Figure 28.

Figure 28. Labelling windows from VICON Nexus software to label the human motion captured.

First each marker is labelled manually with the name from their anthropometric

position given above, but sometimes some of them are lost and this impede tracking

the trajectory, in this cases are useful the other two windows; gap filling helps to

found the gap or unlabeled marker into the recorded trial, and pattern fill helps to

visualize the trajectory, if the suggested trajectory is correct the gap can be filled.

The Figure 29 shows the participant model before and after of the marker labelling.


93

Figure 29. Before and After of Labelling Markers in Vicon Software.

When all of the markers are labelled in a correct way and all their trajectories are

correct, these are exported like a text file (txt), this data is filtered to obtain

position, angles, velocity, acceleration and distances.


94

Appendix 4

Data Motion Analysis

The data files are processed in the MatLab platform, where General Motion

Analysis System Software (GMAS) has its principal operation. Data files are

loaded into GMAS, where the data is processed to obtain a biomechanical model of

the human body. The principal process is listed below:

• Construction of a biomechanical rigid body model; a link-segment model to

represent the human body is created from anatomical landmarks positioned

in order to show the arrangement and distribution of the segments in a three-

dimensional coordinate system

• Acquisition and construction of joint centres

• Definition of local coordinates systems of each segment

• Analyse the biomechanical model data to obtain: orientation and position of

body segments.

The resultant model of the human body must to look like is showed in the Figure

30.


95

Figure 30. Model of the human body during STS transition.

Then the position of the centre of mass (CoM) of each segment is obtained,

followed with the calculation of linear and angular velocities and accelerations of

the CoM’s segments, joint rotations, linear and angular momentum of each segment

were calculated too.


96

Appendix 5

Normalization Processing

In order to perform a better comparison of the data, all trails were normalized. It

was defined as the time 0% (start of the movement) when the vector velocity of the

WB CoM corresponded to zero, which corresponded to the sitting phase, during the

STS cycle. Similarly, the time 100% (end of movement) was defined as the time

when the vector velocity of the WB CoM reduces until it reaches zero. This

position corresponds to the standing position of the STS transition cycle. The

calculation of the transition’s duration was performed employing the normalized

time data that allows the expression of the transition’s performance time as a

percentage of the total movement duration.

In order to perform data normalization, with respect to the time interval the absolute

vector velocity for each trial was calculated. The maximum pick value was

identified and divided by two in order to obtain the values corresponding to the first

and second half curve vector value (t1 and t2). The middle point of the curve (T

period) was calculated employing ¡Error!No se encuentra el origen de la referencia., which is described as follow:

T=t2−t1 2

……………………….. Equation 8

Where: T is the half time period of the curve vector velocity, t1 is the first point of

the half value of the curve vector velocity, t2 is the second point of the half value of

the curve vector velocity.

STS transition start time is obtained using Equation 9, subtracting t1 to the two

times T period. STS transition end time is obtained using Equation 10, adding t2 to

the three times T period.


97

Where: start is the 0% of

the STS transition cycle, end is the 100% of the STS transition cycle, T is the

period of the half value of the curve vector velocity, t1 is the first point of the half

value of the curve vector velocity, t2 is the second point of the half value of the

curve vector velocity.

Figure 31 corresponds to a representative image of the vector velocity obtained for

one of the trials following data normalization.

Figure 31. Vector velocity from the trial one of participant number one, showing the

parameters needed to normalize the trials.

start=t1−T∗2 …………………….… Equation 9

end=t2 +T∗3 ……………………….

Equation 10


98

Appendix 6

Statistics

All data analysis was performed using principal component analysis (PCA), which

reduces the dimensionality of the data set in order to obtain the most influential

variable; in this study the most influential segment to perform STS transition was

determined by PCA. The analysis required unit’s normalization, the linear and

angular momentum were normalized with respect to the weight, height and average

CoM velocity of each participant.

Principal Component Analysis of the Whole-Body

In order to identify the momentum variable most influential during the STS

transition cycle along the three axes in the WB CoM PCA analysis was employed.

The set data was arranged into a matrix, which includes six variables (linear and

angular momentum in their three components AP, vertical, and transversal axes (X,

Y, and Z)), at 100 points corresponding to time cycle. Equation 11 shows the

matrix to perform the analysis of the WB CoM data.

Equation 11

Where WBPCA is the WB CoM matrix of the Z-scores of the momentum variables,

Z-px1 is the first value of the Z-score of linear momentum on the AP axis, Z-px

100 is

the last value of the Z-score of linear momentum on the AP axis, Z-Lx1 is the first

value of the Z-score of angular momentum about the AP axis, Z-Lx100 is the last

value of the Z-score of angular momentum about the AP axis, Z-py1 is the first value

of the Z-score of linear momentum on the vertical axis, Z-py100 is the last value of

the Z-score of linear momentum on the vertical axis, Z-Ly1 is the first value of the


99

Z-score of angular momentum about the vertical axis, Z-Ly100 is the last value of the

Z-score of angular momentum about the vertical axis, Z-pz1 is the first value of the

Z-score of linear momentum on the ML axis, Z-pz100 is the last value of the Z-score

of linear momentum on the ML axis, Z-Lz1 is the first value of the Z-score of

angular momentum about the ML axis, Z-Lz100 is the last value of the Z-score of

angular momentum about the ML axis.

The WBPCA matrix was analysed using MatLab routine princomp (principal

component) of the statics toolbox as is shown in Equation 12.

……. Equation 12

Where: coefs is the command to obtain the weighing coefficients, scores is the

command to obtain the tuning coefficients, variances is the command to obtain the

variance, princomp is the command to analyse in order to obtain the the principal

components, zscore is the command to obtain the Z-scores of the matrix, WBCoM is

the WB CoM matrix of the Z-scores of the momentum variables.

The analysis of the significant implication of each component (percent of variance)

was estimated using Equation 13 described as follows:

Percenti = variancesi *100 Σvariancesi

………. Equation 13

Where: Percenti is the variance percent of the “i” component, and variancesi is the

variance of “i” component.

Principal Component Analysis of the body segments

The body segment’s analysis was performed using PCA. The body segment’s

analysis aimed to provide further understanding of the individual performance

during the STS transition. The set of data of each segment was arranged into a

matrix. Equation Equation 14 shows the HAT matrix used during the analysis,


100

identical analysis matrices were used for the others segments. Every matrix

employs six variables (linear and angular momentum in their three components AP,

vertical, and transversal axes (X, Y, and Z)), at 100 points corresponding to time

cycle.

Segment no. 1 HAT

……….. Equation 14

Where: HAT PCA is the matrix of momentum variables of the HAT segment, 𝑝𝑥1

is the linear momentum on the AP axis, 𝑝𝑦1 is the linear momentum on the vertical

axis, 𝑝𝑧1 is the linear momentum on the ML axis, 𝐿𝑥1 is the angular momentum

about the AP axis, 𝐿𝑦1 is the angular momentum about the vertical axis, and 𝐿𝑧1 is

the angular momentum about the ML axis.

The result of the body segment’s analysis shows the dominant variable to each

segment, and how those components combine the momentum to produce the whole

body motion.


101

Appendix 7

Kinetic Analysis

The kinetic analysis corresponds to the study of the force exerted by the

participants to the ground (GRF) and the exerted point where the force is applied

over the device (CoP) obtained from the force-plates. This data is required to

estimate the causes of the movement [116]. The contribution that the left and right

sides of the body exerts during the STS transition is recorded by two force-plates.

The kinetic analysis was performed using MatLab software. GRF and CoP data

from the left and right side of the body were synchronized in order to obtain

normalising time and global coordinate system that corresponded to the data used

during the kinematic analysis. GRF and CoP data were filtered using a low pass

band Butterworth with a cut-off frequency of 200 hz, and a frequency of Nyquist of

500 hz. Figure 32 shows a representative image of the data synchronization to 35%

of the STS transition cycle using the same origin of the global coordinates system

where the joints, segments, and CoP positions can be appreciated.

The data analysis involved the calculation of the distances existing between the

segments CoM, joints centre, and CoP to estimate the length of radius arm where

the force is applied. The angles between segments were calculated by using the

Equation Equation 15 using the method ‘angle between two vectors’.

𝜃=cos−1𝐴𝐵1∙𝐴𝐵2||𝐴𝐵1||∙||𝐴𝐵2|| …………. Equation 15

Where 𝜃 is the angle between the two vectors, 𝐴 is the point of rotation centre (joint

centre) in three axes components (x, y, and z), 𝐵1 is the point of the result

magnitude of the first vector (segment “1” CoM), and 𝐵2 is the point of the result

magnitude of the second vector (segment “2” CoM),


102

Sagittal View Frontal View

a) b)

CoM Head Left Joint Shoulder Right CoM Feet

Joint Neck Left CoM Humerus Right CoP

CoM Trunk Left Joint Elbow Left Joint Hip

Joint Waist Left CoM Forearm Left CoM Thigh

CoM Pelvis Right Joint Hip Left Joint Knee

Right Joint Shoulder Right CoM Thigh Left CoM Shank

Right CoM Humerus Right Joint Knee Left Joint Ankle

Right Joint Elbow Right CoM Shank Left CoM Feet

Right CoM Forearm Right Joint Ankle Left CoP

Figure 32. Markers, joint, and CoP position on sagittal and frontal view at 35% of the STS transition cycle.

Then joint forces were calculated by the inverse dynamics method. The force

experienced at each joint segment was estimated individually following the free

body diagram as shown in Figure 33.


103

The forces and torque on the joints were calculated using the inverse rigid body

dynamics analysis, which employs the whole segments data and is based on the

second Newton’s Law. Figure 33 shows an example to estimate the forces of the

ankle joint, using the GRF from the force-plate, the flow diagram lead the value to

a correct sum or rest of each value that is involving on the joint on their

respectively axis.

Figure 33. Flow diagram used to obtain the ankle force.

The same method is used to estimate the forces on the remaining joints.

Figure 34 shows an example to estimate the torques of the ankle joint, using the

GRF from the force-plate and the forces from each joint, which were estimate

before, also a flow diagram lead the value to a correct sum or rest of each value that

is involving on the joint on their respectively axis.


104

Figure 34. Flow diagram to obtain the ankle torque.

Biomechanical Analysis of the Sit-to-Stand Transition 2015

Documents

Transcript of Biomechanical Analysis of the Sit-to-Stand Transition 2015