RESEARCH ARTICLE

Minimally assistive robot training for proprioceptionenhancement

Maura Casadio Æ Pietro Morasso ÆVittorio Sanguineti Æ Psiche Giannoni

Received: 23 August 2008 / Accepted: 4 December 2008 / Published online: 13 January 2009

� Springer-Verlag 2008

Abstract In stroke survivors, motor impairment is fre-

quently associated with degraded proprioceptive and/or

somatosensory functions. Here we address the question of

how to use robots to improve proprioception in these

patients. We used an ‘assist-as-needed’ protocol, in which

robot assistance was kept to a minimum and was conti-

nuously adjusted during exercise. To specifically train

proprioceptive functions, we alternated blocks of trials with

and without vision. A total of nine chronic stroke survivors

participated in the study, which consisted of a total of ten

1-h exercise sessions. We used a linear mixed-effects sta-

tistical model to account for the effects of exercise, vision

and the degree of assistance on the overall performance,

and to capture both the systematic effects and the indi-

vidual variations. Although there was not always a

complete recovery of autonomous movements, all subjects

exhibited an increased amount of voluntary control.

Moreover, training with closed eyes appeared to be bene-

ficial for patients with abnormal proprioception. Our results

indicate that training by alternating vision and no-vision

blocks may improve the ability to use proprioception as

well as the ability to integrate it with vision. We suggest

that the approach may be useful in the more general case of

motor skill acquisition, in which enhancing proprioception

may improve the ability to physically interact with the

external world.

Keywords Robot training � Haptic interaction �Proprioception � Stroke patients � Neuro-rehabilitation

Introduction

During the last two decades, robots have been widely used

to investigate the mechanisms underlying the acquisition of

novel motor skills. Furthermore, starting with early

experiments based on the MIT-Manus system (Aisen et al.

1997), robots have proven effective in promoting the

recovery of sensorimotor functions in persons with neu-

romotor impairments (Prange et al. 2006; Kwakkel et al.

2007).

As regards motor learning, in a seminal paper (Shad-

mehr and Mussa-Ivaldi 1994) used a manipulandum-type

robot to simulate a dynamic environment that systemati-

cally perturbed arm motion. With practice, subjects

gradually recovered their original performance, by learning

to predict the disturbance. Vision and proprioception are

known to be equally important in skill acquisition (Battig

1954), but in these experiments adaptation still occurred in

absence of visual feedback (Franklin et al. 2007), thus

suggesting a crucial role for proprioception in developing

an internal model of the disturbance.

The nervous system uses flexible strategies while inte-

grating visual and proprioceptive information (Sober and

Sabes 2005). However, when both sources of information

are available, vision tends to dominate (Wolpert et al.

1994; van Beers et al. 1996, 1998; Botvinick and Cohen

1998; Smeets et al. 2006).

M. Casadio (&) � P. Morasso � V. Sanguineti

Department of Informatics, Systems and Telematics (DIST),

University of Genoa, Via Opera Pia 13, 16145 Genoa, Italy

e-mail: maura.casadio@dist.unige.it

M. Casadio � P. Morasso

Italian Institute of Technology (IIT), Genoa, Italy

P. Giannoni

ART Education and Rehabilitation Center, Genoa, Italy

123

Exp Brain Res (2009) 194:219–231

DOI 10.1007/s00221-008-1680-6

A consequence of visual dominance is that vision may

mask proprioceptive impairments. In fact, in stroke survi-

vors motor impairment is frequently associated with

degraded proprioceptive and/or somatosensory functions

(Tyson et al. 2007), with negative consequences for func-

tional outcome (Carey et al. 1993). These subjects may

have difficulties in estimating the position of their arm in

absence of vision. In addition, they may be unable to

integrate visual and proprioceptive information. Further-

more, they may not be able to detect the presence,

magnitude and direction of external forces.

As a consequence, impaired proprioception may affect

not only motor performance, but also neuromotor recovery.

On the other hand, repeated, active exercise is known to

have a positive influence not only on motor deficits, but on

defective proprioception as well (Dechaumont-Palacin

et al. 2007).

Robot therapy exercises for neuromotor rehabilitation

usually consist of video games with a predominantly visual

component, which tends to hide the proprioceptive com-

ponent of interaction. However, robots might be potentially

beneficial for promoting the recovery of impaired

proprioception and/or visuo-proprioceptive integration in

addition to that of motor functions.

In most cases, robot therapy protocols for neuromotor

rehabilitation use a combination of different exercises

(Prange et al. 2006; Kwakkel et al. 2007)—passive, active-

assisted and/or active-resistive—and it is difficult to draw

solid conclusions on which technique is more effective.

However, the most recent studies (Takahashi and Rein-

kensmeyer 2003; Patton and Mussa-Ivaldi 2004; Patton

et al. 2006b) suggest that therapy protocols should explicitly

take the adaptive nature of the nervous system into con-

sideration. More specifically, Emken et al. (2007) suggested

that sensorimotor adaptation is driven by the optimization of

a cost function which accounts for both effort and position

error. Accordingly, the motor system would behave as a

‘greedy’ optimizer, exploiting the assistive forces generated

by the robot to reduce the degree of voluntary control (and

therefore muscle activation), while keeping the position

error small. A similar situation is likely to occur during

active-assisted exercises (and, even more, during passive

training), so that an assistive force with constant magnitude

would progressively depress voluntary control rather than

promoting its increase. To prevent this, the degree of

assistance should be continuously adjusted as training pro-

ceeds, and ideally kept to the minimum amount that is

necessary to achieve the goal. As a consequence, in robot-

assisted motor rehabilitation (as well as motor skill learning)

it is crucial to design interaction schemes that are capable of

delivering a minimum amount of assistance (Casadio et al.

2009). This principle of interaction is often referred as

‘assist-as-needed’ (Trombly 1995; Emken et al. 2007).

Here we investigate, in a pilot study, the potential

benefit of a form of active-assisted training that emphasizes

the role of proprioception and keeps assistance to a mini-

mum, in the recovery of arm movements after stroke.

Chronic stroke survivors performed reaching movements

with their affected arm, under the influence of robot-gen-

erated assistive forces. Subjects were initially unable to

complete the task without assistance. The therapist initially

set the magnitude of the assistive force provided by the robot

so that assistance allowed patients to initiate the movements,

but in no way imposed the trajectory, the reaching time, and

the speed profile. Whenever patient performance improved,

in the subsequent blocks of trials force magnitude was

reduced; however, each session included trials with all the

previously experienced levels of assistance. In summary, we

used an adaptive, ‘assist-as-needed’ protocol.

Across sessions, trials were performed with open or

closed eyes, in alternation. In closed eyes trials, subjects

were forced to rely on proprioception alone to successfully

achieve the movement goal.

A problem of the protocols based on variable degrees of

assistance for severely impaired subjects is that the amount

of voluntary control (i.e., the performance in absence of

assistance), as well as its change due to exercise, is not

immediately observable when looking at performance in

assisted trials (Colombo et al. 2005). Moreover, if assis-

tance is tailored on individual subjects and is decreased as

subjects improve, treatment protocols tend to vary widely

across subjects, which makes comparisons difficult.

A similar problem occurs if therapy protocols include

both open and closed eyes training. The effect may be

highly variable from subject to subject, depending on the

nature of their impairment. Subjects with impaired pro-

prioception may perform better in presence of vision.

Subjects with problems in integration of proprioceptive and

visual information may perform better in absence of vision.

In these different situations, open and closed eyes training

is likely to have different effects.

To allow the exploration of this form of between-sub-

jects variability, we used a mixed-effects statistical model,

which separately accounts for the effects of exercise, vision

and degree of assistance on the overall performance. At the

same time, the model allows to analyze inter-subject

variability.

Materials and methods

Subjects

Nine stroke survivors (2 males, 7 females, age 52 ± 14)

participated in this study. Subjects were recruited among

those followed as outpatients of the ART Rehabilitation

220 Exp Brain Res (2009) 194:219–231

123

and Educational Center, Genova. All patients were treated

regularly on a weekly basis for at least 6 months before

entering the study.

The inclusion criteria were: chronic conditions (at least

1 year after stroke), stable clinical conditions for at least

1 month before entering robot therapy. The exclusion cri-

teria were inability to understand instructions about the

exercise protocol and other neuro-cognitive problems.

Preference was given to patients with a high degree of

motor impairment.

Disease duration was 34 ± 19 months (range 12–76),

with a majority of ischemic etiology (7/9). Patient

impairment was evaluated by means of the Fugl-Meyer

score, limited to the arm section (FMA) (Gladstone et al.

2002; Platz et al. 2005). Four subjects had a severe

impairment (FMA \ 10/66); three patients had an impair-

ment of intermediate level (10 \ FMA \ 20); two patients

had a mild impairment (FMA [ 20). The average FMA

score was 15 ± 13 (range 5–41). The average Ashworth

score of muscle spasticity (Bohannon and Smith 1987) was

1.9 ± 0.9 (range 1–3). Table 1 reports the demographic

and clinical data for all the subjects.

Due to the small size of the population of subjects, this

study cannot be considered a clinical trial but, rather, a

feasibility study for the proposed robot-therapy approach

(assist-as-needed and proprioceptive training) and a dem-

onstration of the application of the related analytical tools.

The research conforms to the ethical standards laid

down in the 1964 Declaration of Helsinki, which protect

research subjects, and to ethical bylaws of the International

Association of Bobath Instructors (IBITA). Each subject

signed a consent form that conforms to these guidelines.

Experimental apparatus

The robot—Braccio di Ferro (BdF)—is a planar manipu-

landum with 2 degrees of freedom (Fig. 1), which has been

described elsewhere (Casadio et al. 2006). The subjects sit

in a chair, with their chest and wrist restrained by means of

suitable holders, and grasp the handle of the manipulan-

dum. A light, soft support is connected to the forearm that

allows low-friction sliding of the hand on the horizontal

surface of a table, with no influence of gravity. The posi-

tion of the seat can be adjusted in such a way that the

farthest targets (see the next section) can only be reached

with an almost extended arm. A 1900 LCD screen is posi-

tioned right in front of the patients at a distance of about

1 m in order to display the positions of hand and target (see

below) by means of circles of different colors, with a

diameter of 2 cm. The visual scale factor is 1:1.

Training protocol

The training protocol specifically focuses on facilitating

active execution of outward movements. The task consists

of hitting a set of targets, arranged in the horizontal plane

(Fig. 1) according to three layers: inner (A, 3 targets),

middle (B, 3 targets), and outer (C, 7 targets); reaching the

outer targets requires an almost full arm extension. The

distance between adjacent layers is 10 cm; the distance

between targets on the same layer is, respectively, 6.26 cm

(layer A), 8.77 cm (layer B), 5.65 cm (layer C). A target is

reached when its distance from the hand is less than 2 cm.

The training movements are performed either with

open eyes (vision condition) or closed eyes (no-vision

Table 1 Subjects’ demographic and clinical data

Subjects Sex Age (years) Paretic hand Etiology Disease

duration (months)

Site of

lesion

FMA

(0–66)

Ashworth

(0–4)

S1 M 72 L PACI 28 FTP 6 3

S2 F 59 R TACI 39 FTP 5 3

S3 F 69 R PACI 25 FL-PR 12 1?

S4 M 57 L PACI 40 FTP 17 3

S5 F 34 R PACI 24 FTP 13 1?

S6 F 30 L TACI 12 FTO 6 2

S7 F 46 R ICH 26 F-PR 6 2

S8 F 53 R SAH 39 FTN 41 1

S9 F 55 L SAH 76 IPO 36 1

Mean 52.78 34.33 15.78 1.88

SD 14.19 18.08 13.56 0.92

Disease duration refers to the time of start of the robot therapy protocol. Etiology is expressed according to the classification of (Bamford et al.

1991). Arm portion of Fugl-Meyer score (FMA), and Ashworth scale of muscle spasticity, both at the beginning of robot therapy

Site of lesion: FTP, fronto-temporo-parietal; FT-PR, fronto-temporal ? pre-rolandic; FTO, fronto-temporo-occipital; FTN, fronto-temporo-

nuclear; IPO, intra-parenchimal occipital; TACI, total anterior circulation infarct; PACI, partial anterior circulation infarct; ICH, intracerebral

hemorrhagic; SAH, subarachnoid hemorrhagic

Exp Brain Res (2009) 194:219–231 221

123

condition). Target sequences are generated according to

the following scheme: A ? C ? B ? A in order to

emphasize the training of wide, outward movements with

respect to the return to the initial flexed posture. In the

vision condition, targets are presented to the subjects

simultaneously in two ways, visual and haptic: (1) visu-

ally, by means of a circle on the computer screen; (2)

haptically, by means of an assistive force field, i.e., a force

vector directed toward the current target, xT whatever the

current position of the hand. This field was designed in

order to fulfill two requirements: (1) to have a ‘gentle’

interaction with the patient, (2) to be effective even in

absence of vision, in order to focus the patient’s attention

on proprioceptive informations. For these reasons, the

assistive force is not activated abruptly at target presen-

tation but with a ramp-and-hold profile R(t): rise time of

1 s and saturation to a force magnitude, FA, which is set

by the therapist as the minimum value that evokes a

functional response, i.e., a (possibly incomplete) move-

ment in the intended direction. The force is switched off as

soon as the subject hits the target. The next target is

presented after a pause of 1 s. It should be noted that the

chosen assistive field does not emulate the behavior of an

ordinary linear spring but rather a constant-force spring. In

both cases it is possible to define a potential function with

a point attractor. In a linear spring, the potential function

grows in a quadratic way with respect to the distance from

the target whereas, in the constant-force spring, it only

grows linearly. In this application, a constant-force spring

is a better assistive mechanism because it provides a

uniform assistance throughout the whole movement, irre-

spective of the distance to the target. In contrast, a linear

spring would provide an assistive force that vanishes as

one approaches the target and is very large when far from

the target.

In addition to the assistive force component, the haptic

control of the robot includes a mild viscous force field

(viscosity coefficient: 12 Ns/m), intended to damp occa-

sional hand oscillations without significantly affecting the

voluntary reaching patterns, and a virtual ‘wall’ (stiffness:

1,000 N/m) which prevents subjects to go beyond the C

layer of targets and provides an additional feedback about

the successful achievement of the outward target.

The force field generated by the robot is summarized as

follows:

FðtÞ ¼ FA

ðxT � xHÞxT � xHj j � RðtÞ � B _xH

� KWðxW � xHÞ step ðxW � xHÞ ð1Þ

where xT is the vector that identifies the target position in

the plane; xH and _xH are, respectively, the hand position

and speed vectors; xW indicates the projection of hand

position on the wall; B is the viscous coefficient; KW is the

stiffness coefficient of the wall; step identifies a step

function, in order to allow the ‘‘wall’’ term to be one-sided.

Considering that subjects were simply instructed to

reach the targets as soon as possible, it should be noted that

the above scheme of assistance does not explicitly specify

the timing of the reaching movement and/or the trajectory

that subjects have to follow in order to reach the target,

except for the occasional ‘sliding’ movements along the

virtual wall. In other words, the robot does not ‘‘guide’’ the

subject’s hand along a fixed trajectory, and does not

enforce a fixed reaching time. This was done on purpose,

together with the choice of keeping the level of robotic

assistance as low as possible, in order to make sure that the

observed responses were mainly driven by active motor

control, not robot action.

The protocol started with a test phase, in which indi-

vidual subjects familiarized with the apparatus and the

range of assistive forces. This phase was supervised by a

physical therapist, who observed the subjects’ response to

the different force levels and selected the minimum level

Ftest capable to induce at least a hint of active response in

the direction of the target.

One block of trials included repetitions of the

A ? C ? B ? A sequence with different targets in ran-

dom order, for a total of 3 9 3 9 7 = 63 movements; 21

of them were large amplitude, outward movements and 42

movements had smaller amplitude and were directed

inward. The protocol, for each session, is then defined by

the following pseudo-code:

Fig. 1 A view from above of a subject holding the manipulandum.

The subject’s shoulders are strapped to a chair; the forearm is attached

to a sliding support; the wrist is stabilized by means of a skateboard

wrist brace and the hand grasp by means of a Velcro holder. The

targets are arranged on three layers: A, B, C. The C layer is placed in

front of a virtual wall. The basic sequence of target activation is

A ? C ? B ? A and it is repeated 3 9 7 9 3 = 63 times in a

random order. Note that the target distance in the figure is twice the

real distance for graphical reasons

222 Exp Brain Res (2009) 194:219–231

123

Session: set F = Ftest

Train: perform 1 block with assistive field intensity F,

Vision condition

if patient is fatigued or time is over then stop

otherwise perform 1 block with assistive field intensity

F, No-vision condition

if patient is fatigued or time is over then stop

else

if performance over threshold then reduce F by 10–20%

go to Train.

Fatigue was ascertained verbally, by asking the patient

at the end of each session. The ‘Time over’ threshold was

set to 1 h (±5 min). Performance was measured as the

mean speed within the session. The performance threshold

for force decrease was empirically set to 10%.

A consequence of the protocol is that, as training pro-

ceeded, the number of blocks increased and the minimum

assistance level decreased. Moreover, in each session

subjects experienced all the assistance levels used in the

previous sessions, plus some additional ones if they were

not fatigued and time was not over. In other words, the

overall pattern of variation of the assistive force during the

training procedure is nonmonotonic: at the beginning of

each session it goes back to the initial force, selected in the

test session; then it is reduced in steps down to a minimum

value that sometimes is lower than the value reached in the

previous session and sometimes is not. The rationale

is double: (1) to help consolidate the memory of the

learned patterns; (2) to adapt the assistance to the actual

performance.

If subjects reached a level of assistance with a force

below 4 N, the no-vision blocks were eliminated because

that level of force is quite close to the acknowledged per-

ceptual threshold and thus is insufficient for allowing the

subjects to perceive target direction without vision.

The robot training protocol consisted of ten sessions (1

session/week), plus the initial test session. Each session did

not last more that 1 h.

Data analysis

Hand trajectories and the forces generated by the robot

were recorded at a sampling rate of 100 Hz. Hand position

was measured from the 17-bit encoders of the motors, with

a precision better than 0.1 mm in the whole workspace.

Hand speed was estimated by using a 4th order Savitzky–

Golay smoothing filter (with an equivalent cut-off

frequency of 6 Hz).

The analysis focused on the outward movements

(A ? C). In particular, we defined four performance

indicators:

• Mean speed: average hand speed, computed from the

time of target presentation to the time at which the

subject reaches the target. This indicator is expected to

increase as training proceeds.

• Number of sub-movements, identified by the number of

peaks in the speed profile. The output of the smoothing

filter may contain a few spurious velocity peaks. We

eliminated them by means of two criteria: (1) a

threshold on the speed (0.01 m/s), (2) a threshold on

the time interval between one peak and the next one

(0.3 s). Normal reaching movements are characterized

by a single-peaked, bell-shaped speed profile (Morasso

1981) and thus this indicator should approach 1, as

training proceeds.

• T-ratio, defined as the ratio between the duration of the

first sub-movement and the total time required for

reaching the target. The corresponding sub-movement

duration is identified by two consecutive points of

minimum in the speed profile, one before and the other

after the point of peak. As training proceeds, this

indicator should go up to 1 (or 100%).

• Endpoint error, defined as the distance from the target

of the hand position at the end of the firs sub-

movement. As training proceeds, this indicator should

decrease to 0.

The first indicator is a global performance parameter.

The second and third indicators express, in different ways,

the degree of smoothness of the movements. This choice is

motivated by the fact that studies on the recovery from

neural injury suggested that smoothness is a result of a

learned, coordinative process rather than a natural conse-

quence of the structure of the neuromuscular system. The

fourth indicator is related to accuracy.

Statistical analysis

As stated in ‘‘Introduction’’, we are interested in assessing

the overall effect of the treatment (number of sessions,

degree of assistance, presence–absence of vision) on

movement performance. However, this same effect may

differ in individual subjects and, within the same subject,

for different target directions. To account for this, we used

a multilevel mixed-effects model (Laird and Ware 1982),

with three fixed factors (session, force, vision) plus an

interaction term (session 9 vision) and two (nested) ran-

dom factors (subject and target). This allows to properly

account for the correlations among repeated measures from

the same subject and within the same target (Murdoch et al.

1998), and at the same time to analyze inter-subject vari-

ability, i.e., how performance and the effect of treatment

varies in different subjects and, for the same subject, in

different movement directions.

Exp Brain Res (2009) 194:219–231 223

123

For each indicator, the model is defined as:

indicatorijk ¼ B0ij þ B1ij � Sijk þ B2ij � Fijk þ B3ij � Fijk

þ B4ij � ðSijk � VijkÞ þ eijk ð2Þ

where Sijk (session) is the session number (from 0 to 9), Fijk

(force) is the intensity of the assistive force (in N), and Vijk

(vision) is the absence (0) or presence (1) of vision,

respectively for the ith subject (i = 1,…,9), the jth target

(j = 1,…,7) and the kth outward trial (k = 1,…,3). The

residual eijk is the portion of the indicator that is explained

by neither of the above factors.

Model coefficients may be interpreted as follows. The

intercept, B0ij, is the estimated baseline performance level,

i.e., the performance at the initial session, with zero as-

sistive force; this can be interpreted as the initial degree of

voluntary control. Parameter B1ij is the between-session

rate of improvement. Parameter B2ij is a ‘compliance’

component, measuring the dependence of performance on

the assistance level. Parameter B3ij is the ‘vision’ compo-

nent, which denotes the contribution to the performance

provided by presence of vision. Finally, parameter B4ij is

the ‘session 9 vision’ component, which accounts for the

differences in the session effect that are due to vision. In

other words, B4ij accounts for the different behaviors, in

terms of between-session improvement, of the vision and

no-vision trials.

The presence of random factors implies that each of the

above parameters can be seen as having a fixed component

(the same for all subjects and targets), and a random

component (different for each subject and, within each

subject, for each target). For instance:

B0ij ¼ b0 þ b0i þ b0i;j ð3Þ

where b0 is the ‘fixed’ portion of the parameter, common to

all subjects and targets; b0i is the portion of the parameter

which relates to the ith subject, and b0i,j is the portion of the

parameter related to the jth target within the ith subject.

These components can be estimated separately. Testing

the significance of the ‘fixed’ components (e.g., b0) cor-

respond to hypothesis testing as in ANOVA. For instance,

asking like whether the therapy produces a significant

improvement would correspond to testing the significance

of the session effect, i.e., b1.

Moreover, if we look at the random parameters (e.g., b0i

and b0i,j) we can analyze inter-subject (and possibly inter-

target) variability. For instance, for the ith subject we may

look at the relationship between the baseline performance

(b0i) and the subsequent improvement (b1i) in the no-vision

condition. Or, we may look at the difference of baseline

performance between vision and no-vision trials (b3i) and

the corresponding difference in improvement between the

same trials (b4i). For each indicator, we fitted the model to

the data by using a restricted maximum-likelihood proce-

dure, which provided estimates of the coefficients that

account for both the fixed and the random components, as

well as the corresponding significance scores. For all sta-

tistical calculations, we used the R statistical package and

specifically the ‘lme’ function library implementation of

mixed-effect models (Bates and Pinheiro 1998).

Results

Overall evolution of the training sessions

In the early sessions, the outward reaching movements

were typically segmented into a sequence of sub-move-

ments. The first sub-movement only covered part of the

total distance, possibly with a directional error, thus

requiring the subjects to make subsequent corrections. In

contrast, inward movements were usually characterized by

a single, higher peak in the speed profile. As a conse-

quence, outward movements tend to have a greater duration

than inward ones. Figure 2 shows, for all the subjects, the

evolution of the speed profile from the initial to the final

session in the two experimental conditions, vision and no

vision, respectively. In general, all the subjects were

characterized by a trend to quicker and smoother reaching

movements in spite of the fact that the level of the assis-

tance force was progressively reduced.

The evolution of the assistance level is summarized in

Table 2. Table 2 shows that the initial assistance level,

which matched the subjects’ degree of impairment, ranged

between 5 and 25 N; this range was reduced to 0–10 N in

the final session.

It should be noted that the less severe patients (S8, S9)

could carry out the task without assistance. The corre-

sponding number of blocks of trials was increased from an

initial range of 3–8 to a final range of 8–12, while keeping

the same total duration of the therapy sessions.

The evolution of the four performance indicators, from

the initial to the final session, in the two experimental

conditions (vision vs. no vision), is reported in Tables 3

(mean speed), 4 (number of sub-movements), 5 (T-ratio),

and 6 (end point error after the first sub-movement).

Fixed effects

Movement performance is affected by repeated training

(the session factor), by the magnitude of assistive force (the

force factor) and by the experimental condition (vision vs.

no vision). In the same session we used different levels of

assistive force, and for a specific subject the same assistive

force was applied throughout the training protocol in a

variable number of blocks.

224 Exp Brain Res (2009) 194:219–231

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Force

As regards the level of assistance, we found a significant

effect of force—b2 coefficient—on peak speed

(P \ 0.0001), number of peaks (P = 0.0026), endpoint

error (P = 0.0003) and T-ratio (P \ 0.0001). This is no

surprise, as it is the mere confirmation that assistance has a

beneficial effect on performance.

Session

We found highly significant effects of session for the mean

speed (P = 0.0076), the number of sub-movements

(P \ 0.0001), the T-ratio (P = 0.0001), and the endpoint

error (P = 0.0308).

The b1 coefficient—session, systematic part—is positive

for the two indicators (mean speed, T-ratio) for which the

improvement corresponds to an increase of the indicator

(0.335 ± 0.096 cm/s per session and 2.70 ± 0.659% per

session, respectively), and negative for the two indicators

(number of sub-movements and endpoint error) for which

improvement is denoted by a decrement (-0.369 ± 0.098

peaks per session and -0.316 ± 0.088 cm per session,

respectively). This means that the significant effects of

session in fact correspond to improved performance.

Vision

As regards the effect of vision, in no indicator we found

significant vision and session 9 vision effects. This means

that the presence of vision did not have a systematic effect.

Again, this is hardly surprising, as subjects are likely to

differ widely in their sensory impairment as regards its

relationship with the underlying motor impairment: some

subjects strongly rely on vision because proprioception is

poor; in other subjects proprioception is good enough to

take the place of vision; in some case there may even be a

conflict between vision and proprioception.

Random effects

The model of Eq. 2 can be used to identify the individual

variations—subjects by subject and target by target—in the

contributions of each factor (session, force, vision) to each

indicator. Here we only discuss the variability across

subjects.

Fig. 2 Speed profile of the typical movement in the first and the last

session, for each subject and for the same movement (from the central

position of the A layer to the central position of the C layer). The plots

refer to the vision (open eyes, black) and non vision (closed eyes,

gray) conditions, respectively

c

Exp Brain Res (2009) 194:219–231 225

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Table 2 Evolution of the minimum level of assistive force (N) and (in parentheses) the corresponding number of blocks, for each subject (S1–

S9) and for each session (test session ? therapy sessions)

Subjects Sessions

Test 1 2 3 4 5 6 7 8 9 10

S1 25 25 (3) 18 (7) 18 (8) 15 (10) 15 (10) 15 (10) 15 (10) 13 (10) 13 (11) 10 (12)

S2 22 20 (6) 18 (7) 16 (7) 16 (9) 14 (9) 12 (11) 12 (11) 12 (13) 12 (13) 8 (13)

S3 15 13 (5) 12 (9) 12 (9) 10 (10) 9 (11) 9 (11) 6 (12) 6 (14) 6 (15) 6 (16)

S4 13 10 (4) 10 (4) 9 (5) 8 (6) 8 (6) 8 (6) 7 (8) 6 (8) 5 (8) 4 (8)

S5 13 8 (4) 8 (4) 8 (4) 7 (5) 7 (6) 9 (4) 6 (7) 5 (7) 4 (8) 4 (8)

S6 9 6 (6) 6 (6) 6 (6) 5 (6) 5 (7) 6 (7) 5 (8) 4 (8) 2 (9) 2 (9)

S7 9 5 (8) 4 (8) 5 (10) 3 (10) 3 (10) 3 (11) 3 (10) 3 (11) 3 (11) 2 (12)

S8 5 3 (7) 2 (8) 1 (9) 0 (10) 0 (11) 0 (12) 0 (10) 0 (12) 0 (12) 0 (12)

S9 5 2 (8) 1 (8) 0 (10) 0 (10) 0 (10) 0 (12) 0 (13) 0 (12) 0 (12) 0 (11)

Table 3 Mean speed (cm/s): overall modification from the initial to

the final session in the two experimental conditions (vision and no

vision)

Subjects Vision No vision

Initial Final Initial Final

S1 0.9 ± 0.1 10.7 ± 0.8 1.4 ± 0.8 14.5 ± 0.9

S2 8.1 ± 9.4 5.6 ± 0.8 3.0 ± 0.7 6.9 ± 0.8

S3 4.4 ± 1.4 12.4 ± 1.0 8.9 ± 1.9 11.4 ± 1.9

S4 2.4 ± 0.3 8.5 ± 1.2 3.8 ± 0.7 7.5 ± 0.3

S5 4.5 ± 1.1 7.0 ± 1.0 4.9 ± 0.5 7.2 ± 0.9

S6 3.4 ± 0.9 10.6 ± 0.6 2.4 ± 0.9 9.6 ± 1.7

S7 8.7 ± 0.6 10.6 ± 0.5 7.8 ± 0.2 9.8 ± 1.0

S8 13.2 ± 1.3 16.5 ± 1.6 6.0 ± 0.5 7.8 ± 0.5

S9 11.7 ± 1.6 12.7 ± 1.7 7.4 ± 0.2 9.3 ± 0.3

Table 4 Number of sub-movements: overall modification from the

initial to the final session in the two experimental conditions (vision

and no vision)

Subjects Vision No vision

Initial Final Initial Final

S1 10.1 ± 3.8 1.7 ± 0.2 10.3 ± 6.6 1.1 ± 0.1

S2 1.9 ± 0.4 2.3 ± 0.6 5.4 ± 3.2 1.8 ± 0.2

S3 4.5 ± 0.9 1.2 ± 0.1 2.8 ± 1.1 1.6 ± 0.4

S4 14.6 ± 7.3 1.6 ± 0.5 13.2 ± 4.7 3.2 ± 0.7

S5 6.1 ± 2.3 2.5 ± 0.3 8.0 ± 0.6 3.7 ± 0.6

S6 7.2 ± 4.2 1.2 ± 0.1 10.8 ± 5.2 1.5 ± 0.7

S7 1.9 ± 0.2 1.1 ± 0.0 2.8 ± 0.3 1.5 ± 0.2

S8 1.6 ± 0.4 1.2 ± 0.2 2.9 ± 0.4 1.7 ± 0.2

S9 1.4 ± 0.3 1.1 ± 0.1 5.9 ± 2.6 1.2 ± 0.2

Table 5 T-ratio: overall modification from the initial to the final

session in the two experimental conditions (vision and no vision)

Subjects Vision No vision

Initial Final Initial Final

S1 3.8 ± 1.2 72.8 ± 4.6 8.3 ± 4.1 88.3 ± 2.7

S2 41.6 ± 31.2 48.2 ± 11.7 29.2 ± 2.6 57.5 ± 11.0

S3 31.1 ± 7.1 96.3 ± 4.6 76.6 ± 4.0 89.0 ± 7.8

S4 21.9 ± 7.7 82.9 ± 11.7 29.5 ± 10.5 68.5 ± 2.8

S5 30.6 ± 9.3 62.8 ± 6.3 33.2 ± 9.5 67.6 ± 9.3

S6 41.7 ± 14.2 95.7 ± 1.2 26.1 ± 8.3 90.8 ± 13.3

S7 80.9 ± 0.4 97.8 ± 2.5 74.1 ± 6.1 96.7 ± 4.3

S8 91.2 ± 5.5 98.7 ± 2.2 70.9 ± 4.4 93.6 ± 2.6

S9 96.7 ± 4.4 100 ± 0 82.9 ± 8.3 98.7 ± 1.2

Table 6 End-point error after the first sub-movement (cm): overall

modification from the initial to the final session in the two experi-

mental conditions (vision and no vision)

Subjects Vision No vision

Initial Final Initial Final

S1 6.0 ± 0.7 1.3 ± 0.3 8.0 ± 2.2 0.4 ± 0.2

S2 3.0 ± 1.4 1.9 ± 0.7 4.9 ± 1.3 1.5 ± 0.8

S3 3.1 ± 0.3 0.1 ± 0.1 1.1 ± 0.1 0.6 ± 0.4

S4 10.2 ± 2.3 1.3 ± 1.0 14.1 ± 2.8 3.3 ± 0.2

S5 9.2 ± 2.0 2.9 ± 0.5 11.3 ± 0.3 2.7 ± 0.8

S6 5.8 ± 0.8 0.3 ± 0.1 6.3 ± 0.7 0.6 ± 0.8

S7 0.8 ± 0.2 0.2 ± 0.2 2.1 ± 1.2 0.3 ± 0.4

S8 0.6 ± 0.4 0.0 ± 0.0 3.6 ± 0.5 0.4 ± 0.1

S9 0.3 ± 0.4 0.0 ± 0.0 1.8 ± 0.3 0.1 ± 0.1

226 Exp Brain Res (2009) 194:219–231

123

Session

To look at the session effect on each individual subject, for

each indicator we compared the model coefficients (both

the systematic and the random part), namely b0 ? b0i

(baseline performance) and b1 ? b1i (session effect, i.e.,

the change over sessions), for each individual subject. This

allowed to assess, across subjects, the relationship between

initial performance and the magnitude of the session effect

(i.e., the change in performance due to training).

These results are displayed in Fig. 3. All indicators

display a strong negative correlation between the baseline

performance, b0 ? b0i and the change over sessions,

b1 ? b1i: r = -0.89 for the mean speed; r = -0.75 for the

number of peaks; r = -0.68 for the T-ratio; r = -0.95 for

the endpoint error. This indicates that subjects with poorer

initial performance tend to improve more. Although this

result can be partly attributed to a ‘ceiling’ effect—in less

severe subjects the initial values of the indicators are closer

to the ‘ceiling’—it also suggests that, irrespective of the

initial conditions, all subjects have a potential for

improvement that is enhanced by the assist-as-needed

protocol. Figure 3 specifically refers to the session effect in

the ‘no-vision’ case. Similar results were found in the

‘vision’ case.

Vision

The model also allows to assess the effect of vision on the

individual subjects. A crucial question is how the different

subjects compare in terms of their initial performance with

open or closed eyes. Another question, similar to the one

we asked before for the ‘session’ effect, is whether there is

any systematic relationship between the difference in the

vision and no-vision baseline behavior and the differential

change in vision and no-vision trials.

The former question can be addressed by comparing, for

each subject, the baseline performance without vision

(b0 ? b0i) and with vision (b0 ? b0i ? b3 ? b3i). For all

four indicators, the comparison is shown in Fig. 4.

The figure indicates that some subjects (namely, S1 and

S3) have a better initial performance with closed eyes (data

points below the diagonal line in the speed and T-ratio

plots). In contrast, other subjects (S8, S9) have a better

performance with open eyes (data points above the diag-

onal). The remaining subjects have similar performances

Fig. 3 Relationship between

the baseline performance (the

b0 ? b0i parameter in the

model) and the change over

sessions (the b1 ? b1i parameter

in the model), relative to the no

vision (closed eyes) case. All

plots display a strong negative

correlation, indicating that

improvement is greater in

subjects with poorer initial

performance. Negative values of

the baseline parameters denote

that these subjects are unable to

move without assistance. For

clarity, the ‘change’ parameter

has been projected over the set

of sessions (i.e., what is shown

is b1 ? b1i multiplied by 9)

Exp Brain Res (2009) 194:219–231 227

123

under both conditions. Moreover, the same figure suggests

that, whatever the initial state of each subject as regards the

relationship between vision and no-vision performance, the

final performance in both conditions improves for all

subjects and all indicators.

Figure 5 complements the above picture by displaying

how the initial performance compares with the change over

sessions. More specifically, it depicts, for each subject, the

relationship between the difference in the baseline perfor-

mance with and without vision (i.e., parameter b3 ? b3i)

and the difference in the performance change over sessions

between trials with and without vision (i.e., parameter

b4 ? b4i).

Figure 5 indicates a strong negative correlation among

these parameters. For speed, number of peaks, T-ratio and

endpoint error, the correlation coefficients are -0.36,

-0.96, -0.88, and -0.81, respectively. This means that

subjects with an initial more severe impairment with closed

eyes (negative b3 ? b3i) result in a greater improvement in

closed eyes trials (negative b4 ? b4i), and vice versa.

Discussion

Subjects improve their motor performance

Analysis of the results suggests that the proposed robot

therapy exercise, based on an assist-as-needed paradigm, is

capable of improving the ability to perform outward

movements in chronic stroke survivors.

Improved performance was characterized by a general

regularization of the movements. In particular, we observed

a remarkable reduction in the degree of segmentation into

sub-movements and the emergence of normal reaching

patterns—straight paths and bell-shaped velocity profiles

(Morasso 1981). As training proceeded, kinematic indica-

tors showed a trend toward movements that were faster,

smoother, and with more symmetric speed profiles. It may

be asked to what extent such improvements are also

indicative of functional recovery. Studies on stroke recov-

ery (Rohrer et al. 2002) have suggested that smoothness is a

result of a learned, coordinative process rather than a natural

Fig. 4 Differential effect of

open eyes (vision) and closed

eyes (no vision) training. For

each subject, filled circlesindicate the baseline

performance in vision and no

vision trials. Circles below thediagonal denote subjects with

better performance (or greater

error, depending on the

indicator) in absence of vision;

circles above the diagonalindicate better performance (or

greater error) in presence of

vision. The lines indicate the

direction and the magnitude of

improvement. Subjects display a

strong trend toward improving

most in the modality in which

they are initially more defective

so that the final performance is

closer to the diagonal. For

clarity, the ‘change’ parameter

(i.e., the length of the line) has

been projected over the set of

sessions

228 Exp Brain Res (2009) 194:219–231

123

consequence of the structure of the neuromuscular system.

Additionally, there is some evidence that the segmented

structure of arm movements in stroke patients can be

attributed to a deficit of inter-joint coordination (Levin

1996). Therefore, smooth movements result from an

improved coordination, which is a necessary condition for

functional recovery.

Minimally assistive training

A peculiar feature of our proposed approach is that

assistance is kept at a minimum, thus preventing move-

ments, as much as possible, from being performed

passively. In other words, movements are assisted, not

enforced by the robot therapist. In contrast, in other more

common approaches the level of assistance is increased if

subjects are unable to reach the target, up to a value that

allows to reach it in one way or another. The difference is

that in our case robot assistance focuses on movement

initiation whereas in other approaches it focuses on

movement termination.

It is interesting to note that after training, the subjects

generally reported mental, not physical tiredness. This

occurs, in particular, when subjects have great difficulties

in reaching the target, in spite of robot assistance. In this

case, they tend to stop temporarily their effort to generate

the appropriate command. To do this, they may carry out

some kind of mental simulation, in which they ‘imagine’

how to reach it. In conclusion, the proposed minimally

assistive strategy may incorporate an element of ‘mental

practice’, facilitated by the fact that movement termination

is not enforced by the robot. This speculation motivates the

design of specific future experiments aimed at the incor-

poration of mental practice in human–robot interaction.

Robot therapy is still effective in severely impaired

subjects

Our results suggest that even severely impaired patients

benefit from robot therapy, to an extent that is at least

comparable to those with mild to intermediate levels of

impairment. This is partly in contrast with other studies,

Fig. 5 Relationship between

the differential effect of training

with open eyes (vision) and

closed eyes (no vision) in the

baseline performance (the

b3 ? b3i parameter in the

model) and in the change over

sessions (the b4 ? b4i parameter

in the model). For the number of

peaks and endpoint error

indicators, subjects with an

initial more severe impairment

in the no-vision condition

(negative b4 ? b4i) result in a

greater improvement in the

same condition

Exp Brain Res (2009) 194:219–231 229

123

which suggest a greater benefit for mildly impaired sub-

jects (Fasoli et al. 2003; Colombo et al. 2005; Patton et al.

2006a). This is a contribution to an open debate, about the

extent to which physical therapy (by either human or robot

therapists) may contribute to the functional recovery of

severe, chronic patients. At least, our results provide some

evidence for a positive prognostic outcome.

The importance of proprioceptive training

Our results suggest that at least a significant part of stroke

survivors may benefit more from proprioception-enhancing

therapy sessions (the ‘no-vision’ condition) than from tra-

ditional visually guided training. In particular, subjects

who initially had problems with closed eyes tend to benefit

most from this training modality (the inverse is true for

subjects who initially displayed more problems with

movements performed in presence of vision). Although this

observation is not conclusive from a strictly clinical point

of view, it strongly motivates future controlled clinical

trials based on this working hypothesis.

In conclusion, we suggest that robots may be useful in

neuromotor rehabilitation not only because they combine

in the same device a capability of delivering interactive and

repeatable sensorimotor exercises and continuously moni-

toring the actual motor performance, but also because they

allow to create new and ‘controlled’ haptic environments in

which patients can learn to move by only using proprio-

ceptive information. We also believe that robot-controlled,

rich, haptic virtual environments may be powerful tools for

a better understanding of the role of proprioception during

acquisition of novel sensorimotor skills and, possibly, for

designing effective robot trainers which are capable to

improve subjects’ performance in skill acquisition.

Acknowledgments This work was supported by two Research

Projects of National Relevance (PRIN) grants awarded by the Italian

Ministry of University and Research to P. Morasso and V. Sanguineti.

We thank Ms. Liliana Zerbino, PT, for the help in the selection of the

patients and the evaluation of the FMA score.

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