A Review of Approaches to Mobility Telemonitoring of the Elderly in Their Living Environment

A Review of Approaches to Mobility Telemonitoring of the Elderlyin Their Living Environment

CLIODHNA NI SCANAILL,1 SHEILA CAREW,2 PIERRE BARRALON,3 NORBERT NOURY,3

DECLAN LYONS,2 and GERARD M. LYONS1

1Biomedical Electronics Laboratory, Department of Electronic and Computer Engineering, University of Limerick,National Technological Park, Limerick, Ireland; 2Clinical Age Assessment Unit, Mid Western Regional Hospital,

Limerick, Ireland; and 3Laboratoire TIMC-IMAG, Faculte de Medecine, 38706, La Tronche Cedex, France

(Received 10 May 2005; accepted 8 December 2005; published online: 21 March 2006)

Abstract—Rapid technological advances have prompted the de-velopment of a wide range of telemonitoring systems to enablethe prevention, early diagnosis and management, of chronic con-ditions. Remote monitoring can reduce the amount of recurringadmissions to hospital, facilitate more efficient clinical visits withobjective results, and may reduce the length of a hospital stay forindividuals who are living at home. Telemonitoring can also beapplied on a long-term basis to elderly persons to detect gradualdeterioration in their health status, which may imply a reductionin their ability to live independently. Mobility is a good indicatorof health status and thus by monitoring mobility, clinicians mayassess the health status of elderly persons. This article reviewsthe architecture of health smart home, wearable, and combina-tion systems for the remote monitoring of the mobility of elderlypersons as a mechanism of assessing the health status of elderlypersons while in their own living environment.

Keywords—Activity, Remote, Review, Health smart home,Wearable, Telemedicine.

ABBREVIATIONS

ANN Artificial Neural NetworkBP Blood PressureBUS Binary Unit SystemCAN Controller Area NetworkECG ElectrocardiogramGPRS General Packet Radio ServiceGSM Global System for Mobile communicationsIR InfraredPIR Passive InfraRedISDN Integrated Services Digital NetworkLAN Local Area NetworkPDA Personal Digital AssistantPOTS Plain Old Telephone SystemPSTN Public Switched Telephone Network

Address correspondence to Cliodhna Nı Scanaill, Biomedical Elec-tronics Laboratory, Department of Electronic and Computer Engineering,University of Limerick, National Technological Park, Limerick, Ireland.Electronic mail: Cliodhna.NiScanaill@ul.ie

RF Radio FrequencySMS Short Message ServiceWLAN Wireless Local Area NetworkWPAN Wireless Personal Area Network

INTRODUCTION

The western world is experiencing a so-called “greyingpopulation” (Fig. 1).49 In 2001, 17% of the European Union(EU) was over 65 and it is estimated that by the year 2035this figure will have reached 33%. This demographic trendis already posing many social and economic problems asthe care ratio (the ratio of the number of persons agedbetween 16 and 65 to those aged 65 and over) is in decline.This trend suggests that there will be less people to care forelderly, as well as a decreased ratio of tax paying workers(who fund the health services) to elderly people (using thehealth services). This problem is compounded further by thefact that elderly place proportionally greater demands onhealth services than any other population grouping, outsideof newborn babies (Fig. 2).49 Healthcare delivery meth-ods will need to be adapted to meet the challenges posedby this aging population and to care for this group whileconstrained by limited resources, but maintaining the samehigh standards. It is generally expected that the use of tech-nology will be required to create an efficient healthcaredelivery system.9

One such technology, telemonitoring, can be used tomonitor elderly and chronically ill patients in their owncommunity, which has been shown to be their preferred set-ting.29 Telemonitoring can lead to a significant reduction inhealthcare costs by avoiding unnecessary hospitalization,and ensuring that those who need urgent care receive itin a more timely fashion. Long-term telemonitoring pro-vides clinically useful trend data that can allow physiciansto make informed decisions, to monitor deterioration inchronic conditions, or to assess the response of a patient to atreatment. Telemonitoring has the potential to provide safe,

548 NI SCANAILL et al.

FIGURE 1. Growth of the UK population as a percentage of the total UK population. (Office of Health Economics, 2006, reproducedwith permission.)

effective, patient-centered, timely, efficient, and location-independent monitoring; thus, fulfilling the six key aimsfor improvement of healthcare as proposed by the Instituteof Medicine, Washington, DC.9

Telemonitoring has become increasingly popular in re-cent years due to rapid advances in both sensor and telecom-munication technology. Low-cost, unobtrusive, telemoni-toring systems have been made possible by a reductionin the size and cost of monitoring sensors and record-ing/transmitting hardware. These hardware developmentscoupled with the many wired (PSTN, LAN, and ISDN) andwireless (RF, WLAN, and GSM) telecommunications op-tions now available, has lead to the development of a varietyof telemonitoring applications. Korhonen et al.19 classifiedtelemonitoring applications into two models—the wellness& disease management model and the independent living& remote monitoring model. Applications covered by thewellness & disease management model are those in whichthe user actively participates in the measurement and mon-itoring of their condition and the medical personnel playa supporting role. An example of this model is a diabetesmanagement system, in which the user is responsible formeasuring and uploading their blood sugar levels to a cen-tral monitoring center. This model is best suited to thosewho are willing and technologically able to measure theirhealth status and respond to any feedback received. The in-dependent living & remote monitoring model does not placeany such technological demands on the user. In this model,it is the medical personnel who monitors the patient’s con-dition and receives the necessary feedback. Health smart

home systems and many wearable systems are examples ofthis model.

The relationship between health status and mobilityis well recognized. Increased mobility improves staminaand muscle strength, and can improve psychologicalwell-being and quality of life by increasing the person’sability to perform a greater range of activities of dailyliving.36 Mobility levels are sensitive to changes in healthand psychological status.4 A person’s mobility refers to theamount of time he/she is involved in dynamic activities,such as walking or running, as well as the amount of timespent in the static activities of sitting, standing and lying.Objective mobility data can be used to monitor health,to assess the relevance of certain medical treatments andto determine the quality of life of a patient. The need forexpensive residential care (estimated at€100 per patient perday), home visits (estimated at €74 per patient per day), orprolonged stays in hospital (estimated at €820 per patientper day) could be decreased if monitoring techniques, suchas home telemedicine (estimated at €30 per patient perday), were employed by the health services.51 Existingmethods for mobility measurement include observation,clinical tests, physiological measurements, diaries andquestionnaires, and sensor-based measurements. Diariesand questionnaires require a high level of user complianceand are retrospective and subjective. Observational andclinometric measurements are usually carried out overshort periods of time in artificial clinical environments,rely heavily on the administrator’s subjectivity and maybe prone to the “white coat” phenomenon. Physiological

A Review of Approaches to Mobility Telemonitoring 549

FIGURE 2. Estimated hospital and community health services expenditure by age group, in pound per person, in England 2002/3.(Office of Health Economics, 2006, reproduced with permission.)

techniques, though objective, have a high cost permeasurement.

Long-term, sensor-based measurements taken in a per-son’s natural home environment provide a clearer picture ofthe person’s mobility than a short period of monitoring inan unnatural clinical setting. By monitoring and recordinga patients’ health over long periods, telemonitoring has thepotential to allow an elderly person to live independentlyin their own home, make more efficient use of a carer’stime, and produce objective data on a patient’s status forclinicians.

REMOTE MOBILITY MONITORINGOF THE ELDERLY

Health Smart Homes

Smart homes are developed to monitor the interactionbetween users and their home environment. This is achievedby distributing a number of ambient sensors throughoutthe subject’s living environment. The data gathered by thesmart home sensors can be used to intelligently adapt theenvironment in the home for its inhabitants27 or can bestudied for the purposes of health monitoring. In HealthSmart Homes,34 the acquired data is used to build a pro-file of the functional health status of the inhabitant. Themonitored person’s behavior is then checked for deviationsfrom their “normal” behavior, which can indicate deterio-

ration in the patient’s health. Smart home systems passivelymonitor their occupants all day everyday, thus requiring noaction on the part of the user to operate. A large numberof parameters can be monitored in a health smart home,by employing a variety of sensors and the processing ca-pabilities of a local PC. Health smart home sensors, placedthroughout the house, have fewer restrictions (size, weight,and power) than wearable sensors (which are placed on theperson) thus simplifying overall system design. However,health smart homes cannot monitor a subject outside of thehome setting, and have difficulties distinguishing betweenthe monitored subject and other people in the home.

Health smart homes provide a complete picture of asubject’s health status, by monitoring the subject’s mobil-ity and their interactions with their environment. However,health smart home systems often have little or no access tothe subject’s biomechanical parameters, and must thereforemeasure mobility and/or location indirectly using environ-mental sensors (Table 1). These methods range from simplydetecting the subject’s location and recording the time spentthere, to measuring the time of travel from one place toanother by the subject.

Early activity monitoring systems in health smart homesused pressure sensors to identify location. The EMMA (En-vironmental Monitor/Movement Alarm) system, describedby Clark8 in 1979, detected movement using pressure mats(Fig. 3(a))50 under the carpets and a vibration detector onthe bed. These passive sensors raised an alert unless the

TABLE 1. Sensors employed in health smart homes.

Sensor type Sensor description

Pressure sensors50 An unobtrusive pad placedunder a mattress or chair todetect if the bed or chair is inuse

Pressure mat26,50 An unobtrusive pad placedunder a mat to detectmovement

Smart tiles37 Footstep detection tiles, whichcan identify a subject and thedirection in which they arewalking

Passive infraredsensors3,4,34,42,54–56

Detects movement byresponding at any heatvariations. Can be used inbroad mode to detectpresence in a room or innarrow mode to detectpresence in an area. Butthere is a possibility of falsealarms due to heat sources orwind blowing curtains

Sound sensors54 Sensors used to determineactivity type

Magnetic switches4,42,54–56 Switches used in doorframes,cupboard and fridges todetect movement or activitytype

Active infrared sensors7 Sensors, consisting of aninfrared emitter and receptorand placed in a doorway toestimate size and directionthrough doorway

Optical/ultrasonic system3 Measure gait speed anddirection as subject passesthrough doorway

user reset a clock device. Edinburgh District Council26

also employed both pressure mats and infrared sensors(Fig. 3(b))50 to monitor activity in their sheltered housingscheme, thus saving their wardens time and effort.

The first telemonitoring health smart home to measuremobility was presented by Celler et al. in 1994.4 This sys-tem determined a subject’s absence/presence in a room byrecording the movements between each room using mag-netic switches placed in the doors, infrared (IR) sensorsidentified the specific area of the room in which the sub-ject was present, and generic sound sensors detected theactivity type. Data from the sensors were collected usingpower-line communication and automatically transmitted,via the telephone network, to a monitoring and supervisorycanter.

The British Telecom/Anchor Trust42,47 health smarthome (Fig. 4)42 also used passive IR sensors and magneticswitches to monitor activity. Radio transmission was usedto transfer data between the sensors and the system controlbox, thus reducing the amount of cabling in the house and

FIGURE 3. Smart home sensors (a) pressure mats and (b) pas-sive infrared sensors. (Tunstall Group Ltd., 2006, reproducedwith permission.)

making the system easier to install and remove. The datawere time-stamped and stored on the system control boxand then forwarded to the BT Laboratories every 30 minusing the PSTN. All data were processed at the BT Labora-tories. If an alarming situation was detected, an automatedcall was made to the monitored home. The monitored sub-ject could indicate that there was no problem by answeringthe call and pressing the number “1”. If they pressed thenumber “2” or didn’t answer the call a nominated contactwas notified.

This system monitored 11 males and 11 females, agedbetween 60 and 84, and gathered 5,000 days of lifestyledata during trials. The system generated 60 alert calls, andalthough according to Sixsmith47 the majority of alertsraised were false positives, 76% of the subjects thought

FIGURE 4. Layout of house monitored by Anchor Trust\BTLifestyle monitoring system. (Porteus and Brownsell, 2006, re-produced with permission.)

the sensitivity was just right. Two subjects fell during thetrial but both these subjects used their community alarmsbefore the system had sufficient time to recognize thesituation.

There were several implementation issues in this system.BT had to develop a control box due to the unavailabilityof a suitable commercial product. It was also necessary toadd an additional telephone line to each dwelling solelyfor the control box. The authors raised the topic of PIRconflicts, noting that it is possible for two or more PIRsensors to be active at the same time. It was also noticedthat curtains blowing in the wind caused PIR conflicts. Theauthors found the development of an algorithm, to distin-guish between an alarming situation and a minor deviationwas more difficult than they had originally expected butthis distinction became easier to make as more lifestyledata were collected.

Perry et al.40 described a third generation15 telecaresystem, The Millennium Home, which has built on thework of the second generation Anchor Trust/BT telecareproject. Like it’s predecessor, the Millennium Home wasdesigned to support “a cognitively fit and able-bodied user”and detect any deviations from their normal healthy circa-dian activities using health smart home sensors. However,the Millennium home provides the resident with the op-portunity to communicate with the Millennium Home sys-tem using a variety of home–human (computer-activatedtelephone, loudspeakers, television/monitor screen) andhuman–home (telephone, remote-control device with a tele-vision/monitor, limited voice recognition) context-sensitiveinterfaces, which were not available in the Anchor Trust/BThome. These interfaces provide a quick and easy method forthe user to cancel false alarms, or to raise an alarm quickly,thus improving on the preceding system.

Chan et al.7 developed a system, which not only detecteda subject’s absence/presence in a particular room, but alsomeasured their mobility in kilometers. Active IR detectorsand magnetic switches were placed in each doorframe todetermine the subject’s direction through the doors and toestimate their size for identification purposes. Passive IRsensors mounted on the ceiling formed circles of diameter2.2 m on the floor and detected any heat variations causedby human movement within and between these circles. Abinary unit system (BUS) linked the sensors and the localPC. An artificial neural network (ANN) monitored the sub-ject’s mobility data for deviations from their usual pattern.This system was based on the assumptions that the moni-tored subject lived alone and had repetitive and identifiablehabits. Chan et al. also used this approach in a later system,6

where IR movement detectors measured the night activitiesof elderly subjects suffering from Alzheimer’s disease. Thissystem was tested for short term (16 subjects monitored foran average of 4 nights) and long term durations (1 subjectmonitored for 13 consecutive nights) and good agreementwas found between the system and observations made by

the nursing staff. However, the authors had difficulties withthe IR sensors and noted that they could not detect fastmovement or more than a single person in the room. Theimprecise boundaries of the IR sensors was also an issue inthis system, as the possibility of two or more sensors beingactive at the same time made the timing of certain events,such as going to bed, difficult.

Cameron et al.3 designed a health smart home that mea-sured mobility and gait speed along with other parameters,to determine the risk of falling in elderly patients. PIR sen-sors were also used in this system to quantify motion withineach room. The authors developed an optical/ultrasonicsystem to measure gait speed and direction as the sub-ject passed through each doorway. In the next evolutionof this system Doughty and Cameron,14 recognizing theimportance of accurate mobility and fall data in fall riskcalculation, replaced the ambient fall detection sensors withwearable sensors.

Noury et al.33 designed the Health Integrated healthSmart Home Information System (HIS2) (Fig. 5),34 de-scribed by Virone et al.,54–56 to monitor the activity phaseswithin a patient’s home environment using location sen-sors. Data from magnetic switches and IR sensors placedin doorframes were transmitted via a CAN network to thelocal PC, where the number of minutes spent in each roomper hour was calculated. Measured data were compared tostatistically expected data each hour. The CAN networkrequires only a single telephone cable to transfer data frommultiple sensors to the local PC, thus reducing the amountof cabling required for a health smart home. CAN networkshave sophisticated error detection and the ability to operateeven when a network node is defective. In the absence ofa clinical evaluation, a simulator was developed to simu-late 70 days of data and test the ability of the system tostore large amounts of data and to manipulate these data toproduce results.55

The HIS2 health smart home initially communicatedwith a local server using an Ethernet link. In the next evo-lution of the system a PSTN line was used to transfer datato a remote server. However, this method proved costly asthe link was continually running. The HIS2 health smarthome now collects the data locally and emails this data, asan attachment, to the remote server every day. This methodis also used to alert the remote server in emergency cases.

The Tunstall Group,50 in the UK, provides commercialhealth smart home solutions for the remote monitoring ofelderly patients by using PIRs, door-, bed-, and chair-usagesensors (Figs. 3(a) and 3(b)), among others, to determine theactivity level and type of the monitored subject. A gatewayunit, placed in the person’s house, stores information fromthese sensors and downloads it via a telephone line to acentral database and an alert is generated if an alarmingtrend is detected. The carer can review the patient’s datausing the Internet and determine what action, if any, isrequired. Tunstall also have a facility for the carer to request

a current status report for the client by SMS messaging, inorder to provide the carer with peace of mind.

Wearable Systems

Overview

Wearable systems are designed to be worn during nor-mal daily activity to continually measure biomechanicaland physiological data regardless of subject location. Wear-able sensors can be integrated into clothing10,32,38 andjewelry,1,46 or worn as wearable devices in their ownright.5,22,23,25,30,45 Wearable sensors are attached to thesubject they are monitoring and can therefore measurephysiological/biomechanical parameters which may not bemeasurable using ambient sensors. However, the designof wearables is complicated by size, weight, and powerconsumption requirements.19

Wearable systems can be classified by their data col-lection methods—data processing, data logging, and dataforwarding. Data processing wearable systems include aprocessing element such as a PDA10,19 or microcontrollerdevice. Data logging and data forwarding systems are those,which simply acquire data from the sensors and log these foroffline analysis or forward these directly to a local analysisstation. These systems are best suited to cases where theincreased processing power of a PC is required to completecomplex analysis.

Wearables designed for telemonitoring applicationsmust have the capability to transfer their data, for long-

term storage and analysis, to a remote monitoring center.Data can be transmitted directly from the wearable to themonitoring center using the GSM network,30,32 or indirectlyvia a base station, using POTS or the GSM network,21,46 Aportable GSM modem consumes more energy than a localtransmission unit but it allows “anytime anywhere” locationindependent monitoring of a patient. Indirect methods placea range restriction on the monitored subject, as the subjecthas to be near the base station for the recorded data to betransmitted to the remote monitoring center via the POTSor GSM network.

Wearable Sensors

Wearable sensors have the ability to measure mobilitydirectly. Pedometers, foot-switches and heart rate measure-ments (calculated by R-R interval counters) can measure aperson’s level of dynamic activity and energy expenditurehowever they do not provide information on the person’sstatic activities. Accelerometer and gyroscope-based wear-ables can be used to distinguish between individual staticpostures and dynamic activity. Magnetometers have alsobeen used in combination with accelerometers to assess thegiratory movements.31

Accelerometry is low-cost, flexible, and accurate methodfor the analysis of posture and movement,24 with applica-tions in fall detection, gait analysis, and monitoring of avariety of pathological conditions, such as COPD (ChronicObstructive Pulmonary Disease).5,25 Accelerometer-basedsystems have been shown to accurately measure both

dynamic and static activities in both long11,22 and short-term situations.30 Accelerometers operate by measuringacceleration along each axis of the device and can thereforedetect static postures by measuring the acceleration due togravity, and detect motion by measuring the correspondingdynamic acceleration. Gyroscopes measure the Coriolis ac-celeration from rotational angular velocity. They can there-fore measure transitions between postures and are oftenused to compliment accelerometers in mobility monitoringsystems.28,45 For this reason most mobility, gait, and posturewearable applications are accelerometer and/or gyroscopebased. However, there is little consensus as to the optimalplacement and amount of sensors required to obtain suffi-cient results; with some authors preferring a single sensorunit worn at the waist,12,22,23,25,59 sacrum43 or chest28,31 tomultiple sensors distributed on the body.11,20,30,53

Data Logging Wearables

Data logging systems have the advantage of being ableto monitor the subject regardless of their location. The dis-advantage of data logging systems is that the subject’s mo-bility patterns cannot be analyzed between uploads. If analarming trend occurs between uploads it will not be dis-covered until that data is uploaded and analyzed on the pc.This problem will become more significant as improvingmemory technology increases the time between uploads.Non-telemonitoring data logging systems,11,20,53 typicallyused in a clinical setting, require a skilled user to uploadthe data and perform complex offline analysis. Telemon-itoring data logging systems,2,32,57 used by elderly sub-jects in their own homes, include simplified data uploadmechanisms and automated data analysis and transmis-sion to increase their suitability for non-technically-mindedusers.

The BodyMedia SenseWear (Fig. 6)2 is such a telemon-itoring data logging system. It is worn on the upper armand is capable of storing up to 14 days of continuous datafrom its dual-axis accelerometer, galvanic skin responsesensor and heat sensors. The SenseWear can form a BodyArea Network (BAN) with other commercial physiologicalmonitors, such as heart rate monitors, to supplement itsanalysis. The data can be uploaded to the local PC using aUSB cable or can be uploaded wirelessly using the wirelesscommunicator module. The associated desktop application,InnerView, retrieves lifestyle data, including energy expen-diture, physical activity, and number of steps, from theSenseWear unit. Data from the SenseWear unit can trans-mitted, via an Internet server, to a health or fitness expertfor remote monitoring of the subject’s health status. A carercan be notified by SMS message if an alarming trend hasbeen detected. The SenseWear unit can also operate as adata forwarding device, which wirelessly streams data tothe local PC for immediate analysis.

FIGURE 6. SenseWear armband. (BodyMedia Inc., 2005, pre-produced with permission).

Wearable systems integrated into clothing, such as theVTAMN project32 and the VivoMetrics Lifeshirt R©10,57

products, can be worn discreetly under clothing. The pro-cess of donning and doffing multiple sensors is simpli-fied by integrating these sensors into clothing. Clothing-based wearables also ensure correct sensor placement. TheLifeshirt10 is a lightweight, comfortable, washable shirtcontaining numerous embedded sensors. It measures over30 cardiopulmonary parameters, and it’s 3-axis accelerom-eter records the subject’s posture and activity level. Thesensors are attached, using secure connectors, to PDAdevice. The data is saved to a flash memory card andcan be analyzed locally using VivoLogic software or up-loaded via the Internet and processed by staff at theData Center who will generate a summary report for thesubject.

The VTAMN smart cloth (Fig. 7)32 measures severalparameters of daily living, including activity, using sen-sors incorporated into the garment. The activity-measuringmodule of the VTAMN project is based on a 3-axis ac-celerometer, worn under the subject’s armpit. The data fromthis module is processed by embedded software and candistinguish between activity, a fall, and standing, lying, andbending postures. The VTAM shirt can connect to a remotecall center using the GSM network if it detects an alarm-ing situation. Data can also be transmitted, via the GSMnetwork, from the activity-measuring module to a remotePC, where it is analyzed using further mobility-detectionalgorithms.

Data Forwarding Wearables

Data forwarding systems5,12,22,23,25,46,59 are used whenthe weight of the wearable system is a key factor, as a datastorage or a data processing unit can be replaced by a minia-ture transmitter. However data forwarding wearables, whichtypically use RF, Bluetooth, or WLAN, are range-limited,and therefore the data from the subject is not recorded whenthe subject is outside the range of the receiver. This makesdata forwarding systems suitable for housebound subjectsbut not necessarily those who are independent and have theability to move outside of the house.

Simple accelerometer-based activity monitors, knownas actigraphs, can be worn at the wrist,46 waist, or footto monitor mobility and are usually a single-axis devicesthat simply distinguish between activity and inactivity inorder to estimate energy expenditure, sleep patterns, andcircadian rhythm. While actigraphs were originally localdata logging systems that required manual uploading of datato a PC, an evolution of these devices are data forwardingsystems such as the Vivago device described by Sarela,46

which can generate an alarm in emergency cases.The Vivago R© device (Fig. 8),18 described by Sarela

et al.46 in 2003, is a wrist-worn device with a manualalarm button and inbuilt movement measurement, capa-ble of distinguishing between activity and inactivity. TheVivago system continually monitors the user’s activity pat-terns in their home by forwarding data from the wrist unitto the base station. The base station generates an automatedalarm if an alarming period of inactivity is detected. Thebase station is typically connected to the server using thePSTN, or using a GSM modem if the PSTN is not available.The gateway server then transmits the alert, as voice or text

FIGURE 8. IST Vivago wrist unit. (IST OY, 2006,19 reproducedwith permission).

messages, to the appropriate care personnel. Activity datacan be remotely monitored using specially designed soft-ware. This system was evaluated, over three months, on 83elderly people living at home or in assisted living facilities.Subjects were actively encouraged to wear the device andskin conductivity data, measured by the wrist units, showedthat the subjects were within monitoring range (20–30 m)of the base unit for 94% of the time and user compliancewas high.

Mathie et al.,22,23,25 Wilson et al.,12,59 and Pradoet al.43,44 have each designed more complex systems, capa-ble of measuring both activity and posture, using a single bi-axial or tri-axial accelerometer-unit located at the person’scenter of gravity (i.e. waist or sacrum). Mathie et al.25 useda single, waist mounted, tri-axial accelerometer to mea-sure mobility, energy expenditure, gait and fall incidence inpatients with CHF (Congestive Heart Failure) and COPD(Chronic Obstructive Pulmonary Disease). The device wasinitially placed at the sacrum, but during testing, subjectscomplained of difficulty attaching the device and discom-fort when sitting with the device attached. It was decided toplace the device on the hipbone to improve comfort. How-ever, the authors noted that this placement was more likelyto be affected by artifact than placement at the sacrum, andthat some distortion of the output signal occurred as thedevice was not aligned symmetrically (left-right) on the pa-tient. Data were sampled at 40 Hz and forwarded over a RFlink to a PC. All parameters in the system were calculatedtwice a minute, and summarized information was uploadedto a central server each night. Like all data forwarding sys-tems, this system was unable to monitor the subject whenthey were outside of the range of the RF link. This systemimplemented telemonitoring by uploading data to a centralserver every night. At the same conference, Celler et al.5

described the “Home Telecare System” which combinedMathie’s25 wearable system, with a fixed workstation (forECG, BP and temperature measurements) and ambient sen-sors (light, temperature, humidity). Data from the wearableelement was collected by a local PC, compressed and trans-mitted during the night to a remote server. Measurements

taken using the fixed workstation were transmitted to thecentral server immediately following collection. Passwordswere used to control the level of access each user had to thepatient’s data on the server. A web interface to the serverwas provided for the clinicians to observe the patients mo-bility trends. Easy access to the server was necessary forclinicians to monitor mobility trends because automatedtrend detection and automated summary reports were notimplemented in this system. A pilot study of this system22

was carried out with six subjects, aged between 80 and86, over a period of 13 weeks. The wearable system washoused in a case (71 mm × 50 mm × 18 mm), whichcould be clipped to a belt. Healthy subjects, who werelikely to still be in their own homes at the end of trial, wereselected for this study; consequently, the health status ofthe subjects remained unchanged throughout the study. Ahigh rate of compliance (88%) was measured, which wasattributed by the authors to the simplicity of the system, itsunobtrusiveness (subjects forgot they were wearing it), andthe computer-generated reminders to wear the system. Thehigh rate of compliance and positive user feedback suggestthat the system is suitable for long-term continuous use.

The CSIRO “Hospital without Walls” project describedby Wilson et al.59 and Dadd et al.,12 monitors vital signsfrom patients in their homes using a wearable ultra low-power radio system and a base station located in the home.The wearable module contains a tri-axial accelerometer,and a rubber electrode system for detecting heartbeats, in-terfaced to an RF data acquisition unit. Sensor data canbe continuously forwarded from the wearable to the baseunit for two days before recharging the batteries on thewearable unit. Processing and storage occur predominantlyin the base station PC. Trend and summary data is generatedby database software resident on the base station PC. ThePC uploads data to a central recording facility every dayor in response to an emergency. This data can be accessedremotely by authorized medical staff using a web browser.

Data Processing Wearables

Data processing wearables consume more power thanother types of wearable systems but they can provide real-time feedback to a user and do not require large amountsof data storage, as the raw data are typically summarized inreal-time before storage or transmission. The use of sum-marized data also reduces costs by lowering the upload timeto the server.

CSIRO have developed a data processing mobility mon-itoring system, PERSiMON41 (Fig. 9),41 which measuresheart rate, respiration rate, movement and activity. The non-contact PERSiMON unit is held in the pocket of an under-garment vest. The 3 accelerometers in the unit are analyzedto measure movement, long-term activity trends and to de-tect falls. Sensor data are processed in the wearable unitin order to produce summaries, and to detect and record

FIGURE 9. CSIRO PERSiMON unit. (CSIRO, 2006, reproducedwith permission).

details of an event. A voice channel is activated in the caseof an alarm to reduce the incidence of false positives. Thedata is transmitted by Bluetooth, to a base station in thehome, from where it is uploaded to a remote monitoringcenter. If the subject carries a Bluetooth and GPRS enabledmobile phone they will be monitored, regardless of theirlocation, provided GSM coverage is available.

Veltink et al.53 demonstrated a dual sensor configuration,where uni-axial accelerometers are placed on the trunk andthigh to measure mobility. Veltink’s configuration has beenhas been adapted by Culhane et al.11,20 and validated in along-term clinical trial of elderly people. This configura-tion was found to have a detection accuracy of 96%, whencompared to the observed data. Nı Scanaill et al.30 adoptedthis accelerometer configuration, which requires only twodata channels to distinguish between different postures anddynamic activities, for a wearable telemonitoring system(Fig. 10). A wearable data acquisition unit processed thedata from the chest and thigh accelerometers every secondto determine the subject’s posture. A SMS (Short MessageService) message, summarizing the subject’s posture for theprevious hour, is sent from the data acquisition unit everyhour to a remote monitoring and analysis server. This sys-tem was tested in short-term conditions on healthy subjectsand showed an average detection accuracy of over 99%.

Prado et al.43,44 developed a WPAN-based (WirelessPersonal Area Network) system that is capable of moni-toring posture and movement of the subject 24 h a day,inside and outside of the home. This system utilizes anintelligent accelerometer unit (IAU), capable of 2 monthsof autonomous use and which is fixed to the skin at theheight of the sacrum using an impermeable patch. The IAU(diameter 50 mm, thickness 5 mm) consists of two dual-axis accelerometers, a PIC microcontroller and a 3 V Li-Ionsupply. It can reset itself and inform the WPAN server when

FIGURE 10. Remote mobility monitoring using the GSM network.

it detects hardware failure. The WPAN server includes analarm button, a display to show the state of the IAU, and anoptical/acoustic signal to confirm transmission to a remoteunit. Low power ISM-band FSK RF transmission was usedto communicate within the WPAN and a Bluetooth linkwas used to transfer data between the WPAN server andthe remote access unit (RAU). Several alternatives wereexplored for the transmission of data from the RAU to thetelecare center,44 including POTS, GSM, ISDN, and X.25protocol. The X.25 protocol was chosen for cost-efficiency,security reasons, ubiquitous access (especially in rural ar-eas), development time, and ease of use.

Combination Wearable/Health Smart Home Systems

Health smart home systems developers have recentlybeen integrating wearable sensors into their systems in or-der to make more accurate physiological and biomechanicalmeasurements. These systems combine the physiologicaland location-independent monitoring advantages of wear-ables with the less severe design constraints of a healthsmart home. Combination wearable/health smart home sys-tems are those, which used both wearable and health smarthome sensors to measure mobility. Systems, such as theHospital without Walls project,12,59 which monitors mobil-ity using a wearable, and uses ambient sensors to makenon-mobility measurements (such as weight, and bloodpressure) are not considered as combination systems forthe purposes of this review.

Fall detection using only ambient sensors is compli-cated as there is no direct access to the subject who isfalling. This makes it difficult to distinguish between a

subject falling and a heavy object being dropped. If a fallis properly recognized using the ambient sensors the sys-tem has to decide if it is a recoverable fall or if an alarmmust be raised. Doughty and Costa16 developed a telemon-itoring health smart home with a wearable fall detectionelement. The wearable element consists of pressure padsin the shoes to count steps, tilt sensors to detect transfers,and shock sensors to detect falls. The health smart homeelement indirectly monitored location using sound sensors,and switches on the lights and television. The followingyear Doughty and Cameron14 incorporated a wearable falldetector into their already developed fall risk health smarthome, to improve the accuracy of their fall detection system.The combination wearable/health smart home system de-signed by Noury et al. also used a wearable sensor to detectposture and movement after a fall but used ambient sensors(magnetic switches and IR sensors) to monitor location.

Activity monitoring using wearables in a health smarthome environment provides more accurate data than mon-itoring with ambient sensors alone. Virone et al. describedan ambulatory actimetry sensor in several of the papersdescribing the HIS2 health smart home.13,33,56 The sen-sor continuously detected physical activity, posture, bodyvibrations and falls. Ambient sensors in the HIS2 homeprovided data on the patient’s circadian activity.

DISCUSSION

Smart Homes

Health smart homes, wearables, and combinationsystems monitor mobility using a variety of sensor and

volume

user input

communicationbiomechanical

ubiquity

continuity

Smart Home

volume

user input

ubiquity

continuity

Data Logging

volume

user input

ubiquity

continuity

Data Forwarding

volume

user input

ubiquity

continuity

Data Processing

volume

user input

ubiquity

continuity

Combination System

Power Volume User Input Cost Bandwidth Range Ubiquity

Bio-mechanical Continuity

3 = low 1.5 = Low

3 = No

1.5 = Low 1.5 = low

3 = outside 3 = Yes 1.5 = Yes 3 = Yes

2 = med.

1 = normal

2 = med.

1 = med. 1 = med.

2 = around

1 = indirectly

1 = high

0.5 = Bulky

1 = High

0.5 = High 0.5 = high

1 = Inside 1 = No 0.5 = No 1 = no

FIGURE 11. Graphical comparisons of the different approaches to mobility telemonitoring and the associated rating scale.

communication technologies. However, no method is betterthan the other in all respects (Fig. 11).

Health smart homes (summarized in Table 2), by theirnature monitor a wide range of factors and while there aremany examples of health smart home systems only a few

implement mobility monitoring as part of their functions.This may be because mobility is a difficult quantity to mea-sure simply using ambient sensors. Passive infrared sen-sors and switches placed in doorframes are the most com-mon sensors applied to measure mobility. These sensors

TABLE 2. Summary of health smart home systems discussed.

Author Sensor descriptionLocal data

transmissionTelemonitoring in

system?

Cameron3 IR sensor and optical/ultrasonic sensors in doorway ISM band RF NoCeller4 IR sensors, magnetic contacts Power-line Yes, PSTN to serverChan et al.6,7 Active and passive IR sensors, magnetic contact sensors BUS Yes, PSTNPorteus and Brownsell42; Perry40 Passive IR, magnetic switches RF Yes, PSTN to serverNoury54–56 IR sensors, magnetic contacts. Audio sensors CAN Yes, email to serverTunstall group50 Passive IR sensors, door-, bed-, and chair-occupancy

sensors, magnetic contactsISM band RF Yes, PSTN

measure mobility by determining the location of the sub-ject and recording their interactions in that location as wellas the time spent there.

Health smart homes are highly suitable for houseboundelderly who are living alone as the health smart home ap-proach eliminates the need for daily donning and doffingof the monitoring equipment. These systems would be thepreferred option for persons with dementia, as the userwould not need to remember to don the equipment. How-ever, health smart home systems have several disadvantagesincluding the requirement to identify the monitored subjectfrom others in the home (Biometrics signature), IR conflictsand the inability to monitor the subject outside of the homeenvironment.

Monitoring the activity of an elderly person in a smarthome is relatively simple if the person is living alone, as allthe detected activity can be attributed to that person. How-ever, the health smart home must have the ability to identifythe monitored subject, and distinguish between their loca-tion and the location of others if the monitored person isliving with others or regularly receives visitors. This canbe achieved using video recognition, audio recognition,54

height recognition,7 wearable id tags48 or footstep analy-sis.37 Video recognition and audio recognition may be seenas intrusive. Electronic identification tags, such as those inthe Elite Care nursing home, described by Stanford,48 arean effective solution but they are not suitable for those withdementia who may forget to don the tag. A solution basedon ambient sensors, such as the active IR sensors placedin the doorways in Chan’s health smart home,7 or smartfootstep identification mats used by Orr and Abowd37 inthe Georgia Tech Aware House, is preferable because it isless invasive than wearable ID tags and requires no actionon the part of the user to operate.

The topic of PIR conflicts was raised by several au-thors. Each PIR sensor should monitor a certain activity,for example if the person is in bed. If a subject is identifiedby the “in bed” PIR sensors and the “in bedroom area”PIR sensor at the same time the system will not be ableto identify the subject’s activity properly. This issue canbe overcome by careful placement of sensors or intelligentdecision-making software.35 Careful IR sensor placement isalso required to avoid false detections caused by nearby heatsources.

Health smart homes cannot monitor a person while theyare outside of the home environment. A wearable elementwould be required to measure the person’s mobility outsideof the home environment but then the system would haveto be reclassified as a combination system. Smart homesystems are therefore not suited to monitoring the mobilitylevels of active persons who are frequently and irregularly,outside of the home.

Wearables

Wearable systems (Table 3) are typically based on ac-celerometers, gyroscopes or a combination of these. Thedistinguishing features in mobility monitoring wearablesare the particular configurations of these sensors and thedata acquisition methods employed. In the past, wearablesystems were bulky and heavy and consumed excessivepower, however the size and power consumption of bothsensors and processing units have decreased significantlyin recent years, enabling the development of smaller, morediscrete systems. Wearable systems are not suited to thosewho are not mentally or physically able to operate them, asmost wearable require a limited amount of user interactionto maintain and operate them.

TABLE 3. Summary of wearable systems discussed.

Author Sensor description Classification Local data transmission Telemonitoring

Mathie and Celler5,22,23,25 Waist-mounted tri-axialaccelerometer

Data forwarding RF Internet transfer

Prado43,44 Sacrum-mounted 4-axisaccelerometer unit

Data Processing RF within WPAN andBluetooth from WPANand RAU

X.25 protocol

Wilson and Dadd12,59 3-axis accelerometer Data forwarding RF Internet transferNoury et al.32 (VTAMN) 3-axis accelerometer, worn

under armpitData processing None (direct transmission

to remote centre)GSM

Sarela46 Sensor capable ofdistinguishing betweenactivity and inactivity

Data forwarding RF PSTN or GSM

CSIRO PERSiMON41 3 accelerometers Data Processing Bluetooth PSTN or GPRSNı Scanaill30 Two uni-axial accelerometers,

placed at the chest and thighData Processing None (direct transmission

to remote centre)SMS

messagingBodyMedia SenseWear2 Dual axis accelerometers worn

on the upper armData logging (data

forwarding optional)USB or wireless upload to

PCInternet transfer

VivoMetrics LifeShirt10,57 3-axis accelerometer,integrated into vest

Data logging Flash card, manuallyuploaded to PC

Internet transfer

Telemonitoring data logging systems allow the personto be monitored regardless of their location and allow com-plex analysis to be performed off-line using the processingpower of a PC. The expanding storage capabilities of mod-ern data logging systems suggest that the period betweendata uploads will increase. An excessive period betweenuploads is to be avoided as a worrying trend which occurswithin this period may be missed. Rather, the increased datastorage capability should be used to improve the quality ofdata, by increasing the sampling frequency or by monitoringadditional relevant parameters.

Data forwarding systems, such as the Vivago system de-scribed by Sarela et al.,46 allow real-time complex analysisof mobility data on a local PC. They are typically smallerthan their data logging and data processing counterparts, asthey use a miniature transmission module instead of storageor processing modules. A range/power-consumption tradeoff is made when selecting the data transmission modulefor a data forwarding system and the technology with thelowest power consumption is usually selected at the ex-pense of a wide-ranged technology. As a result, once thesubject is out of range of the base station, the subject’s dataare not received by the base station and are therefore notanalyzed. These wireless technologies include Bluetooth,WLAN and ISM. Low-power Bluetooth (0.3 mA in standbymode and 30 mA during sustained data transmissions) hasa range of 10 m, making it ideal for Personal Area Net-works (PAN) or communicating with a base station placedcentrally in a small apartment. Higher power Bluetooth hasa range of up to 100 m. WLAN is a more mature networktechnology than Bluetooth, and has a longer range (up to300 m outdoors) however it is bulkier and does require morepower than Bluetooth to operate. However, the increasinguse of Bluetooth and WLAN in consumer electronics makesthe data forwarding systems, based on these technologies,susceptible to data “fog”. The European Telecommunica-tions Standards Institute (ETSI) has allocated the 869 MHz(ISM) frequency band for both narrow and broadband socialalarms and telecare. Telecare applications, which use thisfrequency, will be secure, reliable and free from interferencefrom non-telecare applications.

Data processing wearables are becoming more commondue to the rapid development of PDA and microproces-

sor technology. The smaller form factors of these unitsallow them to be carried for prolonged periods withoutcausing discomfort to the user. The capability of theseprocessors to deal with larger numbers of inputs and toperform more complex calculations than before has lead tothe development of more sophisticated multi-sensor mon-itoring solutions. Personal Area Networks, and its wire-less equivalent WPAN, are now emerging in the wearablesensor domain as a result. The ability of data processingwearables to interface with wireless communication mod-ules such as mobile phones or WLAN modules, to trans-mit processed data to remote servers, is also beginning tobe explored as a possibility in recent years43,44 and whencombined with the improved processing ability of PDAsand processors, may lead to the removal of the PC as anintermediate stage in future mobility monitoring systems.PAN- and WPAN-based systems, with the ability to plug-and-play new sensors or third-party devices into the exist-ing monitoring system, increase the flexibility of wearablesystems and enable easy upgrading and maintenance ofthe systems. Therefore, the advantages that once attractedpeople to health smart homes (discretion, multi-parametermeasurement, and ease-of-use) are now also available inwearable devices.

Combination Systems

General mobility is one of the four parameters noted byCeller et al.4 to be most sensitive to changes in health, and istherefore a very useful parameter to measure. Health smarthome developers appear to have recognized that simpleand accurate mobility measurement is not feasible usingambient sensors alone (Table 4) and instead, like Doughtyand Cameron, have adapted their pure health smart homesystems3 to include a wearable element for more accuratefall and mobility measurement.14 Local communication incombination systems encompasses both a wireless elementfrom the wearable, and a wired or wireless element from thehealth smart home sensors. Data from the wearable sensorsmust be forwarded wirelessly in real-time to be processedin tandem with the real-time data from the ambient sensorson a local PC. The ambient sensors may use any wired orwireless communication method appropriate for local data

TABLE 4. Summary of combination systems discussed.

Author Sensor description ClassificationLocal data

transmission Telemonitoring

Doughty and Cameron3,14 Sensors selected according to patientsneeds, including pressure pads inshoes, tilt, shock, and optical sensors

Data forwarding ISM band RF Yes, PSTN

Noury13,33,56 IR and magnetic contact switches.Wearable accelerometer and tilt sensor

Data forwarding RF Yes, email to remoteserver

Costa and Doughty16 IR sensor and optical/ultrasonic sensors indoorway. Wearable fall detector

Data forwarding ISM band RF Yes, “telecommunicationchannel”

TABLE 5. Advantages and disadvantage of different approaches to mobility telemonitoring of the elderly.

Monitoring system Advantages Disadvantages

Health smart homeSystem

1. Less severe design (power consumption, formfactor, processing power, and communicationsmeans) limitations

1. Need for user identification if multiple persons arepresent

2. Cannot monitor outside of the home environment2. Person does not have to wear electronics 3. Limited access to biomechanical parameters3. Does not require user input to operate it (suited to

persons with dementia)4. Problems with IR sensors as shown by Chan6

Wearable system 1. Direct access to biomechanical parameters 1. Design limitations in form factor, powerconsumption, processing power, communications,and durability of materials

2. Data logging and data processing wearablesmeasure mobility regardless of location

3. Technological advances leading to reduced size,weight and cost of systems

2. Bulky systems are indiscrete3. Data forwarding systems cannot monitor person

outside of range of base station4. User must control system (recharge, switch on/off,

don/doff)Combination system 1. Monitoring inside and outside of the home 1. Combines disadvantage of wearable and health

smart home systems2. Combines advantages of wearable and healthsmart home systems

transmission in a health smart home. Telemonitoring incombination systems is achieved using the same transmis-sion techniques used in health smart homes. This impliesthat telemonitoring in most combination systems is via aPSTN line. Although, the PSTN line may be replaced bywireless GSM transmission, in future health smart homeand combination systems.

If a health smart home-based mobility system requiresthat some aspects of the system should also be worn, then itwould seems that a complete wearable solution would be abetter approach. Requiring the user to wear some elementsof the system and also interact with the health smart home tomeasure mobility imposes the disadvantages of both typesof systems on the user. One, they are restricted to the homewhen mobility is being measured; and two, they are requiredto remember to don the wearable unit for their mobility tobe measured (Table 5). Though, it gives the possibility tothe person to doff the wearable for a while and still bemonitored by the smart home.

Practical, Functional and Ethical Issues

Elderly people wish to remain living in their own homesfor as long as possible provided they are safe. Technology,and in particular telemedicine, has a role to play in achiev-ing this goal by reassuring the person that their conditionis being monitored. However, several practical, functional,and ethical issues need to be addressed to promote the useof telemonitoring and to ensure long-term patient compli-ance. Practical issues include ease of use, discretion, cost,and the ability to perform daily activities unimpeded. Is-sues regarding ease of use can be resolved by automatingsystem functions to reduce patient-system interaction andby clearly explaining the system to the user. Elderly people

will be encouraged to use a telemonitoring system if theyfeel the system benefits them. They have been found39 toreject indiscrete systems that indicate to others that the per-son is being monitored as they fear they label them as oldand dependent. Elderly people will also reject any systemswhich due to their size, communication methods or locationimpede their daily activities or force them into a fixed lifepattern.39 The cost of telemonitoring may dissuade manyelderly people, who only have their pension. However, inmany countries, this cost may be partially or fully funded bythe health services or social services, or private insurancecompanies. Commercial telemonitoring systems can alsobe purchased by adults with elderly relatives, to providethe purchaser and the monitored person with reassurancethat their condition is being monitored. Functionally, a sys-tem must be accurate, reliable, and have continuous ac-cess to an alarm center. An inaccurate system which raisesfalse alarms wastes valuable healthcare resources; can leadto a lack of confidence in the system’s ability; and willeventually annoy both the client and responder.14 On theother hand, a system that fails to recognize an alarmingsituation may put a person’s life in danger.58 Unreliablesystems are very problematic for two reasons—first, theyrequire constant maintenance, which will deter elderly pa-tients and second, there is an increased risk of missingalarming situations while the system is broken. Alarmingsituations can also be missed if the communication linkto the remote alarm center is not available when required.Automated detection of alarm conditions, based on individ-ually configurable alarm thresholds is necessary as a subjectmay not recognize a slow deterioration in their situationor their ability to raise an alarm may be compromised.Overall, the ability to detect a worrying trend and raisean appropriate alarm is very important to elderly people39

who fear they will remain unattended in the event of anaccident.

The increasing use of telemonitoring to support inde-pendent living inside and outside of the home inevitablyraises many ethical questions regarding privacy, cost andmotivation. A patient’s right to confidentiality must berespected in any aspect of healthcare, telemonitoring in-cluded. At the patient side, the intrusiveness of long-termanalysis of the patient in his/her own private life shouldbe minimized as much as possible. Data encryption andsecure methods should be applied to ensure confidentialityof data during transfers over the network. At the monitoringend, access to data should be restricted using a hierarchi-cal password system. Telemonitoring is a cheaper optionthan hospitalization, clinical visits, or home help,51 but thedecision to telemonitor a person should not be purely oneconomic grounds. Although a majority of elderly peoplewould prefer to remain in their own homes, the minoritywho would prefer to be cared for outside of their homeshould not be denied this opportunity, simply because thereis a cheaper telemonitoring option available. Conversely,there is a concern that telemonitoring would be availableonly to the rich, thus enforcing the “digital divide”. Themotivation of the patient and prescribing clinician to usetelemonitoring should also be questioned—is telemonitor-ing in the patient’s best interest, or would they receive bettercare in a clinical environment? Is it safe for the candidateto live independently? Is there a possibility of the candidatebecoming over-dependent on the technology to an extentthat they do not report an illness to their clinician, but waitfor the system to report the illness on their behalf? Unfor-tunately, methods for assessing socio-ethical implicationsof health technology are relatively undeveloped and evenfewer mechanisms exist to take actions based on the re-sults of such evaluations.52 The decision-making processfor selecting a telemonitoring system should be similar tothe decision-making process used when selecting a therapy.The clinician examines the advantages and disadvantagesof employing telemonitoring, and also examines the advan-tages and disadvantages of not employing telemonitoring,which is slightly different.

CONCLUSION

Mobility telemonitoring is a growing area, which en-ables the subjective monitoring of the health status of el-derly people living independently in their own homes. Itprovides the clinician with continuous quantitative data thatcan indicate an improvement or deterioration in a patient’scondition. Telemonitoring also reduces the cost of provid-ing care to elderly subjects by moving care from the tra-ditional hospital/nursing home setting into the home, thusmaking more efficient use of healthcare resources. It im-proves the quality of life of the monitored person and theircarer, as they can continue with their daily lives, reassured

that if an alarming trend occurs it will be detected and actedupon early.

There is a wide range of solutions for the telemonitoringof mobility of elderly in their living environment, vary-ing in both sensor type and communication method. Astechnology advances on both these fronts even more per-mutations will undoubtedly be developed. This variety is tobe welcomed, as each system solves a particular problem.Health smart homes are suited to housebound people whoare either unwilling or incapable of operating a monitor-ing system. Wearable data forwarding systems, the lightestwearable option, are suited to the frail and housebound asthey analyze the data in real-time and can raise immediatealerts. Data-logging wearables are suitable for monitoringmulti-parameter, long-term trends of healthy elderly sub-jects, who regularly leave their homes. However they arenot suited to real-time alarm detection because they requirethe user to upload the data to a PC before an alarming trendis detected. Automated data processing wearables requirelittle user interaction and are suited to monitoring mobilityof people who leave their houses regularly but would ben-efit from real-time alarm detection. Finally, combinationsystems are best suited to those who require the quantity ofdata provided by a health smart home but also the accuracyof physiological measurements provided by a wearable.

ACKNOWLEDGMENTS

The authors would like to acknowledge the funding fromthe Irish Research Council for Science, Engineering andTechnology under the Embark Initiative.

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A Review of Approaches to Mobility Telemonitoring of the Elderly in Their Living Environment

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