in home telerehabilitation for geriatric patients

© DIGITAL STOCK & EYEWIRE

In-Home Telerehabilitationfor Geriatric Patients

In the last decade, changes in the organization and financingof health services in Canada have reduced the length of stayin acute care hospitals, increased the number of day sur-geries, and generally reoriented the hospital-centric care

toward dispensation of health services in the community. Thedemographic imperative of an aging population creates uniqueopportunities to look at new paradigms in delivering healthcare services in the community. In this context, in-home telere-habilitation (i.e., the delivery of rehabilitation services at anindividual’s home over telecommunication networks) has beenidentified as a promising avenue. The rationale for in-home tel-erehabilitation is to expand and facilitate the delivery of reha-bilitation services to people who cannot travel to a clinicbecause of disability or travel time [1], [2]. Evidence support-ing the use of telerehabilitation as a viable alternative or com-plement to traditional in-home therapy is slowly emerging inthe literature [3], [4].

Most types of telerehabilitation services fall into two catego-ries: clinical assessment (the patient’s functional abilities in his/her environment) and clinical therapy. To provide both types ofservices remotely while interacting with the patient, the rehabil-itation professionals rely on establishing a telepresence throughbidirectional video and audio from videoconferencing equip-ment connected through a high-speed Internet connection.Telepresence [5] refers to the phenomenon whereby a humanoperator develops a sense of being physically present at aremote location through interaction with the user and the subse-quent perceptual feedback he/she receives via the appropriateteleoperation technology [6].

We investigate in this study, following the positive resultsfrom a proof-of-concept study [7], the effectiveness of provid-ing in-home telerehabilitation services as an alternative tohome care visits for physical therapy in orthopedic conditionsfollowing discharge from an acute care hospital and rehabilita-tion unit [8]. Based on the results from the initial proof-of-concept study and a user-centered design approach, atelerehabilitation platform was developed consisting of twoH264 videoconferencing codecs (Tandberg 500 MXP) withintegrated wide-angle view cameras and remotely controlledpan tilt zoom (PTZ) functions, local and remote computers with

dedicated modular software interfaces for user-friendly controlof videoconferencing connections, PTZ camera function, andexternal devices (i.e., tablet PC and sensors). An overview ofthe telerehabilitation platform and the software interface for theclinician is illustrated in Figure 1.

Iterative changes were made to the hardware and softwarecomponents to ensure transparent dynamic interactions be-tween the clinicians and the clients during a telerehabilitationsession. Special attention was given to provide a mouse-basedinterface to control intuitively from a unique screen throughpoint-and-click or area-zoom PTZ camera functions at bothsites. Results from our ongoing trial and debriefing of clinicianshave shown that telerehabilitation practices challenge conven-tional communication behaviors underlying the professionalpatient-client relationship found in face-to-face encounters inrehabilitation. Although videoconferencing can create a tele-presence experience for the clinician by providing visible andnonverbal information about the behavior of an individual inhis/her environment, it is difficult for the clinician to interpretdetailed information such as the kinematics and kinetics of theindividual’s movement and physiological responses to exer-cises in a telerehabilitation context. This is even more evidentwhen operating under suboptimal optical conditions such asthose found in the home environment. Increased telepresencecombining information from wearable sensors with audio andvideo streams might be part of the solution to complement thetraditional telerehabilitation practices [9], [10].

Wireless Body Area Sensor NetworksWireless body area sensor networks (WBANs) are well suitedto increase telepresence, as they can provide specific informa-tion about an individual’s behavior without using complexlaboratory equipment and without interfering with the person’snatural behavior [11]. WBANs are generally built aroundseveral sensing devices wirelessly linked together using nar-row-band radio communication [12]. Recent developments inthe field of wireless networks have generated many newcommercial wireless communication platforms based on dif-ferent protocols and technologies (Wi-Fi, WiMax, Bluetooth,Zigbee, UMTS, UWB) [13]. These technologies offer a widerange of characteristics in terms of speed, transmission range,power requirements, connectivity, and cost. The choice of

BY MATHIEU HAMEL,R�EJEAN FONTAINE,AND PATRICK BOISSY

GERO

NTEC

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Use of Wearable Wireless Body Area SensorNetworks for Increased Telepresence

Digital Object Identifier 10.1109/MEMB.2008.919491

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE 0739-5175/08/$25.00©2008IEEE JULY/AUGUST 2008 29

wireless network architecturefor a WBAN application iscontext and sensor depend-ent. Table 1 presents someof the existing BAN/WBANtechnologies and their wire-less networking character-istics. The use of a WBANsystem in a telerehabilitationcontext calls for a small, reli-able, low-power platform ca-pable of seamlessly integratingseveral modules.

The Zigbee technology wasdesigned for this type of appli-cation. The IEEE 802.15.4physical radio standard oper-ates on the 2.4-GHz unli-censed band over 16 channels,and the network layer supportstopologies such as star, tree,and mesh. Depending on thepower output and environ-mental characteristics, trans-mission distances range from10–100 m [14]. Recent publi-cations [11], [15], [16] haveillustrated projects geared to-ward developing application-specific WBAN systemsbased on Zigbee technologies.Recommendations on a mul-titier architecture for WBANsystems in the context ofpatient monitoring or the typesof sensors to use and their lo-cations have been proposed[15], and different WBANsystems are currently underdevelopment. ActiS, an ac-tivity sensor developed byJovanov, is built around awireless platform that integra-tes a Zigbee-compliant radioand a microcontroller calledTelos from Moteiv [17]. Acustom sensor board con-nected to the Telos platformenables concurrent wirelessECG and accelerometer mea-surements. As a heart sensor,ActiS can be used to monitorthe heart activity and trunkposition. CodeBlue is anotherproject developing wirelessbody area networks for medi-cal care. The goal of theproject is to develop sensorsfor stroke rehabilitation patientsand to monitor vital signs tohelp in emergency response(ECG, blood pressure) [18].

Table 1. Wireless technologies and possible BAN/WBAN platforms.

Technology Transfer Rate Range BAN/WBAN

Wi-Fi 11–54 Mb/s 30–50 m DPAC Airborne, PDAsWiMax 4.5–70 Mb/s 100 m–50 km Portable computersBluetooth 57 kb/s–3 Mb/s 100 m Smart-Its, iMotesZigbee 20–250 kb/s 100 m MICAz, Telos, tMotesUMTS 50 kb/s–2 Mb/s 5–100 km MobihealthUWB 54 kb/s–48 Mb/s 1–10 m Magnet

1

2

Home Site

Clinical Site

(a)

(b)

1

3

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Fig. 1. Telerehabilitation platform. (a) Hardware components including two H264 videocon-ferencing codecs (Tandberg 500 MXP) with integrated wide-angle view cameras andremotely controlled PTZ functions. (b) Software interface for user-friendly control of video-conferencing connections, PTZ cameras function, and external devices (i.e., tablet PC andsensors).

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The wireless platform chosen for this project is the MICAzfrom Crossbow [19], which is also based on a Zigbee-compliant radio.

WBANs for Telerehabilitation

System ArchitectureFor use in telerehabilitation applications, we recently developeda Zigbee-based WBAN system with custom sensor platformsand adaptable sensing inputs capable of accommodating differ-ent sensor configurations. The system designed for telerehabi-litation applications is composed of sensor platforms withapplication-specific signal conditioning units connected to wire-less communication modules. An overview of the system archi-tecture and components is illustrated in Figure 2. The systemconsists of four eight-channel Zigbee-based wireless sensornodes with a total theoretical bandwidth of 250 kbps configuredin a star configuration to a single receiver connected to acomputer. The current sensor node configuration comprisesa custom sensor board with an embedded three-dimensionalaccelerometer (LIS3L02AQ, STMicroelectronics) [20], one one-dimensional gyroscope (ENC-03M, Murata) [21], and connectiv-ity to four external analog or digital sensors (Figure 3). Externalsensors can take many forms: we currently use load cells, respira-tory belts, and a pulse oximeter. The two respiratory belt sensors(MLT1132, ADInstruments) [22] are connected to the first sen-sor node worn on the trunk. The second and third sensor nodesare linked to custom instrumented shoes, which provide weight-bearing data during ambulatory activities. The last sensor nodeuses onboard sensors to measure acceleration and angular rate ofthe subject’s dominant hand.

In the context of telerehabilitation, sensor placement is acritical issue. While the ergonomics, usability, and design ofwearable sensors can affect the reliability of the data, the

external sensors described in this article (oximeter, respiratorybelts, and the instrumented shoes) can all be installed with noor minimal exterior help. The modules, as shown in Figure 2,have elastic bands and adjustable bracelets that enable the sub-jects to install them with relative ease. In certain cases, individ-uals with reduced mobility or dexterity (e.g., stroke) could getassistance from a third party to install the sensor module ifneeded.

The communication module is an off-the-shelf MICAzavailable from Crossbow [19]. The module consists of an

VPN Router

VPN RouterInternet

Tandberg 550 MXPClinician PC

5

Tandberg 550 MXP

PulseOximeter +Accelerometers

Respiratory Belt SensorsInstrumented Soles

4Tablet PC3WBAN Receiver2WBAN Transceivers1

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Fig. 2. Complete system used for a telerehabilitation session. The WBAN system comprises four wireless sensor nodes.A total of 32 analog signals are sampled at 100 Hz frequency and sent to the host computer. Sensors measure theheart rate, blood saturation, changes in thoracic and abdominal circumference, weight-bearing, acceleration,and angular rate. Video, audio, and sensor data are sent to a remote site using a high-speed Internet connection.

Sensor BoardLi-Ion

Battery

WirelessModule

2.4 GHzRadio

PCSensors

ReceiverFlash

Memory

ProcessorAnalog I/ODigital I/O

2.4 GHzRadio

TransmitterFlash

Memory

ProcessorAnalog I/ODigital I/O

Fig. 3. WBAN and sensors. Wireless sensor network comprisesup to four sensor nodes configured with the star topology.Wireless modules include a custom sensor board and aMICAz communication module from Crossbow Technology.

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE JULY/AUGUST 2008 31

ATmega128 microcontroller with eight 10-b analog-to-digitalconverters (ADCs), flash memory, and a Chipcon 2.4-GHzradio transmitter/receiver. Modules can be programmed asreceivers, transmitters, or both using an event-driven, highlymodular operating system called TinyOS [23]. This operatingsystem is based on a library of components that can be easilyconnected using well-defined interfaces. Custom components,written with the NesC language [24], can directly interact withcomponents from the TinyOS library with minimal useof resources. The network is formed by assigning a uniqueaddress to each wireless module individually. The mainreceiver module acts as a coordinator by sending start and stopcommands to transmitters, enabling synchronized data acquisi-tion. Small 580-mAh Li-ion batteries (UBP363450/PCM)power both the sensor boards and the communication modulesand are embedded in bracelets that can be attached to the body.

The WBAN is configured with four wireless sensor nodes.A tablet PC served as the WBAN receiver at the home site andwas connected wirelessly (802.11 b) to a router [Linksys withvirtual private network (VPN)] connected to a digitalsubscriber line (DSL) modem for Internet access. The WBANwas connected to the computer via a USB interface board(MIB520). The router also provided wireless Internet access

to the videoconferencing equipment (Tandberg 550 MXP,H.264 codec). A secured VPN communication channel wasestablished between the two sites using a second identicalrouter at the clinical site. Raw signals provided by the wirelesssensors (Figure 4) can be directly visualized at the clinical siteand further be processed through an algorithm that interpretsin real time the variables such as body angles, weight-bearing,respiration, and heart rates [Figure 1(b)].

To assess the feasibility of using the proposed WBAN sys-tem with the existing telerehabilitation platform, we evaluatedits radio communication performance, operational range, andfunctionality under telerehabilitation conditions. More specif-ically, the objectives of the system’s evaluation were to 1)assess the impact of the number of sensor nodes used, thenumber of sensor inputs per node used, and the sampling rateused on the reliability of the radio communication; 2) charac-terize the performance of this system during continuous use ina home environment; and 3) assess the performance in con-junction with a videoconference link over the Internet.

System EvaluationAlthough the Zigbee-based WBAN systems described inthe literature are quite innovative and their development is

A

Weight BearingRight Foot

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Fig. 4. Signals output from the WBAN system during a walking activity. The A and B cycle shows the appliedvertical forces on the insoles and the leg movements during a normal walk cycle. Activity levels can be cal-culated by combining heart rate, respiratory data, and a sum vector of accelerometer signals.


ongoing, there are little or no published data concerning theperformance and limitations of these systems in terms of radiocommunication, operational range, and functionality underunconstrained conditions in a home environment when wornby an individual. Indeed, proximity issues regarding the place-ment of several modules on the body may lead to severe inter-ference problems, and the reliability of continuously streaminghigh volumes of data to a receiver at a determined rate over along period is untested.

Wireless platforms such as Moteiv, MicaZ, and other Zigbee-compliant devices were mainly developed for commercial andindustrial practices. The goal of the following experiments wasto establish the performance of a typical WBAN in real condi-tions to provide guidelines for future WBAN development andimplementation. First, a reliability experiment was conducted todetermine the performance of several WBAN configurations inan ideal laboratory environment. Second, a similar experimentwas done in a home environment to evaluate the effect of thisenvironment on the WBAN system. Finally, the last experimentconsisted of streaming data from the WBAN system in the con-text of in-home telerehabilitation (i.e., shared bandwidth be-tween videoconferencing equipment and the WBAN systemover a DSL Internet connection).

WBAN Reliability in a Laboratory EnvironmentThe purpose of the reliability experiment was to determine thebandwidth limitations of this kind of system in a controlledlaboratory environment and evaluate the possible problemsrelated to interference and body movement. Several tests wereconducted while varying the number of active modules andthe sampling frequency of the ADCs. The system reliabilitywas evaluated by assuming that communication errors wouldhappen independently of the algorithm programmed in themicrocontrollers. It is also possible to avoid transmissionserrors by programming a more robust error detection algorithmthat would send back bad or missing data packets. A simpleralgorithm that associates a message number and origin of each

data packet was used for these experiments. Transmissionerrors are either a missing message from one of the transmittersor a message not received in the right order. During the experi-ments, precautions were taken to make sure that the batteriesof each module were properly charged and that the moduleswere worn correctly (two bracelets on the wrists and two on theleg shank). The distance from the receiver (DFR) was alsostandardized between tests, making sure it would not exceed50 ft between the receiver and the person. A total of 25 trials of30 min each were performed while varying the number of mod-ules from one to five and the sampling frequency from 50 to100, 200, 400, or 800 Hz. During each trial, tasks related tooffice work were done (walking, typing on the computer, etc.).Results illustrated in both graphs of Figure 5 summarize theperformances of several WBAN setups and suggest a typicalnetwork comprising four active modules, which minimizes theprobability of communication errors and optimizes the numberof active modules and their sampling rates.

Real Home EnvironmentSpecial precautions have to be taken considering that theWBAN system would be used in a home environment.Although laboratory experiments give an idea of systemperformance, it is essential to evaluate the system in a typicalhome with interwall and interfloor communication, possiblesources of signal reflections and noise. The purpose of thisexperiment was to determine whether or not it is viable to usethis system in an in-home telerehabilitation context. Assump-tions were made concerning the communication algorithmand bandwidth requirements, as these parameters could beoptimized. The system configuration used during these testswas taken from previous results derived from the laboratoryexperiments (four modules, 100 Hz). A typical multilevelhouse was chosen as the testing environment (Figure 6).

The receiver module and host computer were situated onthe second floor of the house. Two parameters were eval-uated during the trials: the percentage of communication

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400 Hz 800 Hz 50 Hz 100 Hz 200 Hz 400 Hz 800 Hz

Fig. 5. WBAN reliability experiment results. (a) Total time required over a 30-min experiment before losing communicationbetween the WBAN transmitters and the receiver for multiple setups. (b) Percentage of transmission errors during these experi-ments while varying the number of modules and the sampling frequency of the eight analog inputs.


errors as described earlier in this article and the number ofcommunication losses. This last parameter was evaluated bycounting the number of times the system completely losttrack of the wireless network during the 1-h trials. The vari-ous activities performed during these trials included bothstatic and dynamic movements. More precisely, activities inRoom 1 (office) included writing in a sitting position, vac-uuming, and tidying the closet. In Room 2 (second-floor bed-room), vacuuming and office work were done. Cookinglunch, washing the dishes, and cleaning were done in Room3 (kitchen). Activities in Room 4 (dining room) includedvacuuming and computer work (sitting at the dinner table).Room 5 (bathroom) included some laundry, vacuuming, andscrubbing. Finally, some vacuuming and reading (lying onthe bed) were done in Room 7 (first-floor bedroom). Themean linear distances between the receiver and the WBANtransmitters (DFR) were computed using the mid-point(length, width, height) of each room and the receiver locationon the second floor. The WBAN performances obtained ineach room in terms of communication errors and loss of com-munication did not differ from the performances obtained inthe laboratory environment. The effect of walls, electrical

appliances, and body movements did not prevent the systemfrom working properly.

WBAN in a Telerehabilitation ContextA last experiment was conducted to evaluate the performanceand impact of the WBAN system when used in conjunctionwith a videoconferencing system (i.e., shared bandwidth). A60-min telerehabilitation session took place at the home site,and data from the WBAN were sent to the host computer (clini-cal site) in real time via a high-speed Internet connection. Fig-ure 2 shows the system used during this experiment. A DSLhigh-speed Internet access at the two sites provides a theoreti-cal bandwidth of 3 Mb/s in download and 800 kb/s in upload.From this available bandwidth, 384 kb/s was dedicated to thevideoconferencing equipment to establish a quality audio andvideo link (320 kb/s for video data and 64 kb/s for audio data).Bandwidth allocation was estimated experimentally duringthe telerehabilitation session using communication statistics(upload and download transfer speed) computed by the routerand the videoconferencing equipment located at the clinicalsite. Communication statistics were retrieved from both devi-ces at 5-s intervals. Bandwidth allocation for the WBAN was

calculated as the bandwidthstatistics recorded on therouter minus the bandwidthstatistics provided by thevideoconferencing unit.Continuously polling the sta-tistics also requires part ofthe total bandwidth for bothupload and download. It wasincluded in the WBAN band-width for simplicity. Resultsfrom this experiment are il-lustrated in Figure 7.

The WBAN setup used forthe experiment accounted forapproximately, on averageover the 60-min session,209 kb/s of the total band-width allocated during the ses-sion. The bandwidth neededto stream the data from theWBAN system in real timeover the Internet did not affectthe overall quality of the audioand video signals receivedfrom the videoconferencingequipment. During the experi-ment, the WBAN encounteredcommunication errors andwas restarted three times. TheTandberg unit recorded 192(download) and 95 (upload)missing data packets through-out the session.

Power ConsumptionA custom sensor boardwas built for the telerehabilita-tion application. As describedin the ‘‘System Architecture’’

Second Floor

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Area: 15.68 m2

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LOC0

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Area: Total Area of the Room (m2)

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LOC: Number of Loss of Communication

%ERR: Percentage of Communication Errors

7

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Area: 6.39 m2

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Area: 12.94 m2

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Fig. 6. In-home communication reliability results. Percentage of transmission errors and numberof communication losses during a 1-h continuous transmission from all rooms in a typical houseusing four modules at a 100-Hz sampling rate (eight channels).


section, it contains a tree axial accelerometer, a gyroscope,and four amplifiers for external circuitry. The theoreticalpower consumption is presented in Table 2. The total batterylife was tested experimentally during the real home environ-ment experiment. Continuous transmission of the WBANlasted until the first module had no power left. A total batterylife of 24 h was expected based on the theoretical power con-sumption (Table 2). Operating current from onboard sensors(accelerometers, gyroscopes, and amplifiers) was added to thepower consumption of the MICAz module in continuoustransmit mode. Experimental results suggested a total batterylife of approximately 15.45 h.

Discussion

Performance and Limitations of In-Home WBANDuring the reliability experiments, the system’s farthest limitsof radio communication were tested in terms of sampling fre-quencies and number of active sensor nodes. From the resultsshown in Figures 5 and 6, it is possible to determine some sortof comfort zone where the proposed WBAN system workswell and minimizes the probability of errors. This informationgives a starting point for using a WBAN system during telere-habilitation sessions. An optimal configuration consisting offour active sensor nodes, each capable of accommodatingeight sensor inputs and a sampling rate of 100 Hz, was foundto offer the most reliability. It should be noted that this particu-lar setup is a compromise solution between the number ofactive modules and the bandwidth requirement of the bodysensors. Results also showed the possibility of using fiveactive modules by using a 50-Hz sampling frequency or bytaking only four of the eight available analog inputs. The sys-tem was found to work correctly up to 800 Hz by using a sin-gle active sensor node with eight sensor inputs. However, theaddition of another active sensor node at this samplingfrequency resulted in an immediate loss of communicationwith the receiver.

Onboard data processing can be achieved to substantiallyreduce the overall dataflow by transmitting the already ana-lyzed data and warnings to the clinician, as suggested in otherstudies [11], [12], [15]. Event management, as described byOtto, would considerably reduce the overall transmit rate byrecognizing characteristic features of raw sensor data. How-ever, onboard data processing also has a great impact on powerconsumption and signal latency. Compared with long-timemonitoring scenarios, telerehabilitation sessions are relativelyshort (1–2 h) and require real-time data transfer for quick accessby clinicians. A compromise solution between the amount ofcomputing done and overall bandwidth usage should be consid-ered. No consensus has yet been reached in regard to choosingthe right sensors and data format relevant to clinicians. Themultitude of applications makes it difficult to obtain a uniquestandard. During experiments, signals from onboard sensorswere transmitted in their raw format, as this gives importantinformation about the limitations of the system.

Unlike the results obtained by Ylisaukko-oja [25], theexperiments conducted in the home environment showedpromising results as the system behaved the same way it didduring laboratory experiments. The presence of walls, floors,and possible sources of noise (home appliances) did notincrease the overall number of transmission errors. In fact,

Table 2. Theoretical power consumption of WBANsensor nodes.

Active Items Operating Current

Wireless module (Crossbow) 17 mA* (Tx mode)Accelerometer (STElectronics) 1.5 mAGyroscope (Murata) 5 mAAmplifiers (Analog Devices) 0.250 mATotal 23.75 mABattery life (Li-Ion at 580 mAh) 24.42 h

0 6030

100

200

300

400

600

700

500

Time (min)(a)

0

Video (315 kb/s)

WBAN (209 kb/s)

Audio (64 kb/s)

Total BW (588 kb/s)

Ban

dwid

th (

kb/s

)

0 6030

100

200

300

400

500

Time (min)(b)

0

Audio (64 kb/s)

Total BW (407 kb/s)

Ban

dwid

th (

kb/s

)

Video (228 kb/s)

Download Upload

Fig. 7. WBAN performances and bandwidth allocation during a 60-min telerehabilitation session. (a) Bandwidth allocationduring download composed of video, audio (Tandberg 550 MXP), and sensor data from the WBAN. (b) Bandwidth allocationduring upload composed of audio and video only.


0.167% of transmission errors occurred during the reliabilitytest and a mean value of 0.125% was obtained throughout thehouse. A link can be made between the DFR and the number ofcommunication losses. As expected, the further the WBAN islocated from the receiver module, the greater the likelihood oflosing contact with the base station. Telerehabilitation sessionsusually take place in just one room where the camera, screen,and microphone are installed. Although not always the case,this study shows that the receiver unit could be located inanother room for wiring convenience. The embedded algo-rithms used for the experiments did not include any error man-agement functions. As explained previously, a missing datapacket retransmission function could be embedded in the pro-gram. Loss of communication also implies data loss. Every timelosses occur, the system must be restarted by consecutivelysending a ‘‘stop’’ and a ‘‘start’’ command. This process takes afew seconds and could be decreased to about 500 ms by auto-mating the process. Although not desirable, occasionally miss-ing data packets is not critical during telerehabilitation sessionsas long as the packets are correctly identified as missing.

During the telerehabilitation session, sensors were wired to theWBAN, and data were sent over the Internet to a remote site shar-ing the available bandwidth with a duplex audio and video signalfrom a Tandberg system (Tandberg 550 MXP). The WBANworked as expected, but some adjustments were made to transferthe data throughput from the sensors via the Internet to the clinicalsite. Data reduction had to be done in order for the custom TCP/IPLabview application to work correctly and keep the connectionactive. Data were filtered and downsampled three times beforesending them to a remote computer, resulting in a bandwidth of209 kb/s (Figure 7). This bandwidth also includes polling therouter for statistics. Better results should be possible by allowingmore lag in the communication and by establishing an error-managing algorithm for missing data packets. Overall, the telere-habilitation session was not affected by the presence of theWBAN system. Both clinical and home sites recorded great videoand sound performances throughout the session. Data from thesensors appeared on the clinician’s computer almost synchronizedwith the video signals. From a telerehabilitation viewpoint, thebattery life of the WBAN modules showed satisfactory results, asthey can be used extensively through the day and recharged atnight using a docking station for charging batteries and remoteprogramming. Despite the manufacturer’s warnings about notusing the transceivers within 1 m of each other, the system per-formed well in all dynamic tasks done by the subject. Special caremust be taken with the whip antenna that projects from the brace-let casing. These antennas will be replaced by smaller helix anten-nas embedded directly in the bracelets.

Experimental results obtained from laboratory and in-hometesting of the proposed WBAN system can be synthesized asthe following elements:

1) The WBAN comprising four continuously streamingmodules (100 Hz, eight channels per module) is the opti-mal configuration in terms of the number of active mod-ules and communication errors.

2) The WBAN when worn by an individual in a multilevelhouse during daily activities provides comparable per-formances and reliability as when in use under controlledlaboratory conditions.

3) Data from the WBAN can be streamed over the Internetwithout interfering with the performances of a videocon-ference link.

Future Applications and ChallengesRehabilitation of patients with hip and knee replacementsusually involves the presence of a clinician for the assessmentof parameters such as joint range of motion. Remote assess-ment of this parameter is possible using WBANs and acceler-ometers. Wireless modules located at the patient’s ankle,knee, and hip could serve as a goniometer providing angles foreach segment using gravitational acceleration as a reference[10]. These measurements could help clinicians to betterassess their patients remotely. Combined signals from the sen-sors, such as respiratory belts, pulse oximeter, and accelerom-eters, provide important information about a patient’s activitylevel during rehabilitation. The WBAN could remotelyprovide real-time data relating to patients’ exercise load andfatigue. This information could also be used to monitorchanges in patients’ health from one telerehabilitation sessionto the next. Wireless weight-bearing is possible using instru-mented insoles wired to the WBAN. These sensors could beused during telerehabilitation to evaluate gait and postureparameters and provide real-time feedback for the patient aswell as the clinician. Difficulties encountered during the hometelerehabilitation experiments provided important informationregarding design considerations for the next generation ofwireless platforms. The bracelets should be robust, comforta-ble, and easy for the patients to put on themselves [26]. Thedocking station used for battery charging and remote program-ming should be simple enough for the patients to clearly seethat the modules are correctly docked. Overall, the study dem-onstrated that remote monitoring from multiple sensor nodesis technically feasible using a WBAN system and videocon-ferencing system together. Future research will focus on datareduction, choice of relevant sensors for remote assessment,and software interfaces that will meet the emerging technicalguidelines for telehealth applications [27].

ConclusionsThe use of a wireless body area network linked to embeddedand external sensors can increase the telepresence of rehabilita-tion professionals by providing important information that isotherwise difficult to obtain in that context. This article

The rationale for in-home telerehabilitation is to

extend rehabilitation services to the people in

remote locations or with disabilities.


described the capability of a Zigbee-based WBAN and its poten-tial use in telerehabilitation applications. Experimental resultsshow that a typical setup of four wireless sensor nodes with eightsensor inputs per node sampled at 100 Hz offers the most reli-able radio communication performance and reliability. Tests ina real house showed the possibility of using the wearable systemat home independently from the location of the receiver moduleand in conjunction with videoconferencing equipment.

AcknowledgmentsWe wish to acknowledge the work done by Olivier Lessard-Fontaine and Simon Briere for the preliminary softwaredevelopments on the WBAN and videoconferencing soft-ware. This project was funded by the Canadian Institutes forHealth Research, Institute of Musculoskeletal Health andArthritis (IMHA) under the Invention—Tools, Techniquesand Devices for Research and Medicine program (Grant No.200307ITM-120560).

Mathieu Hamel received his B.Eng. de-gree in 2003 from the Universite de Sher-brooke. In 2007, he completed his M.Sc.degree in electrical engineering in the fieldof signal processing at the same university.He is now working at the Research Centreon Aging, Sherbrooke, Canada, as a researchengineer. His professional interests include

wireless devices applied to rehabilitation and biomedical signalprocessing.

Rejean J. G. Fontaine received his B.Eng.degree in 1991 from the Universite de Sher-brooke. In 1999, he completed his Ph.D.degree in electrical engineering at the sameuniversity in the field of microelectronicsapplied to neurostimulation (cochlear im-plant). After a short stay in industry, hereturned to the Universite de Sherbrooke as

a professor in 2001 and initiated works in electronics applied tomedical imaging. He is now the chairman of a research groupinvolved in the design of medical electronics dedicated topositron emission tomography scanners and to biomedicalsignals.

Patrick Boissy received his B.Sc. degreein kinesiology from the Universite deSherbrooke in 1991. He graduated fromthe Universite de Montreal in 1999 with aPh.D. degree in biomedical sciences withspecialization in rehabilitation. After post-doctoral training at Boston University’sNeuromuscular Research Centre, he joined

the faculty of the Universite de Sherbrooke in 2002 inthe Kinesiology Department, where he is currently an associ-ate professor. He holds appointments as a researcher at theResearch Centre on Aging of the Health and Social ServiceCentre, University Institute of Geriatrics of Sherbrooke, andat the Center of Excellence in Information Engineering of theUniversite de Sherbrooke. His research interests includetechnological and clinical evaluation of telehealth applica-tions and the study of the dose-response relationship in

geriatric rehabilitation. He is currently funded as a ChercheurBoursier Junior II by the Fonds de la recherche en sante duQuebec.

Address for Correspondence: Patrick Boissy, ResearchCentre on Aging, University Institute of Geriatrics of Sher-brooke, Sherbrooke, Quebec, Canada. E-mail: [email protected].

References[1] L. Wakeford, P. P. Wittman, M. W. White, and M. R. Schmeler, ‘‘Telereha-bilitation position paper,’’ Am. J. Occup. Ther., vol. 59, no. 6, pp. 656–660,2005.[2] J. M. Winters and J. M. Winters, ‘‘A telehomecare model for optimizingrehabilitation outcomes,’’ Telemed. J. E. Health., vol. 10, no. 2, pp. 200–212,2004.[3] H. Hoenig, J. A. Sanford, T. Butterfield, and P. C. Griffiths, ‘‘Development ofa teletechnology protocol for in-home rehabilitation,’’ J. Rehabil. Res. Dev.,vol. 43, no. 2, pp. 287–298, 2006.[4] J. A. Sanford, P. C. Griffiths, P. Richardson, K. Hargraves, T. Butterfield, andH. Hoenig, ‘‘The effects of in-home rehabilitation on task self-efficacy in mobil-ity-impaired adults: A randomized clinical trial,’’ J. Am. Geriatr. Soc., vol. 54,no. 11, pp. 1641–1648, 2006.[5] T. Sheridan, ‘‘Musings on telepresence and virtual presence,’’ Presence: Tele-oper. Virtual Environ., vol. 1, no. 1, pp. 120–126, 1992.[6] W. Ijsselsteijn, H. de Ridder, J. Freeman, and S. Avons, ‘‘Presence: Concept,determinants and measurement,’’ presented at the International Society for OpticalEngineering: Human Vision and Electronic Imaging, San Jose, CA, 2000.[7] M. Tousignant, P. Boissy, H. Moffet, and H. Corriveau, ‘‘In home telerehabili-tation for older adults after discharge from an acute hospital or rehabilitation unit:A proof-of-concept study and costs estimation,’’ Disabil. Rehabil.: Assist. Tech-nol., vol. 1, no. 4, pp. 209–216, 2006.[8] H. Moffet, M. Tousignant, H. Corriveau, P. Boissy, S. Robitaille, F. Marquis,and E. Anctil, ‘‘In home telerehabilitation after primary knee arthroplasty: A casereport,’’ presented at the World Conf. Physical Therapy, Vancouver, Canada,2007.[9] J. M. Winters, Y. Wang, and J. M. Winters, ‘‘Wearable sensors and telereha-bilitation,’’ IEEE Eng. Med. Biol. Mag., vol. 22, no. 3, pp. 56–65, 2003.[10] H. Zheng, N. D. Black, and N. D. Harris, ‘‘Position-sensing technologies formovement analysis in stroke rehabilitation,’’ Med. Biol. Eng. Comput., vol. 43,no. 4, pp. 413–420, 2005.[11] E. Jovanov, A. Milenkovic, C. Otto, and P. Groen, ‘‘A wireless body areanetwork of intelligent motion sensors for computer assisted physical rehabilita-tion,’’ J. Neuroeng. Rehabil., vol. 2, no. 1, p. 6, 2005.[12] P. Lukowicz, T. Kirstein, and G. Troster, ‘‘Wearable systems for health careapplications,’’ Methods Inf. Med., vol. 43, no. 3, pp. 232–238, 2004.[13] J. Sakari and N. Jarkko, Wireless Technologies for Data Acquisition Systems.Dublin: Ireland: Trinity College Dublin, 2003.[14] Zigbee-Alliance. (2006). ZigBee specifications [Online]. Available: http://www.zigbee.org/en/spec_download/download_request.asp[15] C. Otto, A. Milenkovic, A. Sanders, and E. Jovanov, ‘‘System architecture ofa wireless body area sensor network for ubiquitous health monitoring,’’ J. MobileMultimedia, vol. 1, no. 4, pp. 307–326, 2006.[16] S. Victor, C. Bor-rong, L. Konrad, R. F. F. J. Thaddeus, and W. Matt, SensorNetworks for Medical Care. San Diego, CA: ACM Press, 2005.[17] Moteiv. (2006). Telos [Online]. Available: http://www.moteiv.com/[18] K. Lorincz, D. J. Malan, T. R. F. Fulford-Jones, A. Nawoj, A. Clavel,V. Shnayder, G. Mainland, M. Welsh, and S. Moulton, ‘‘Sensor networks foremergency response: Challenges and opportunities,’’ IEEE Pervasive Comput.Mag., vol. 3, no. 4, pp. 16–23, 2004.[19] MICAz. (2005). Crossbow technologies [Online]. Available: http://www.xbow.com/Products/Product_pdf_files/Wireless_pdf/MICAz_Datasheet.pdf[20] STMicroelectronics. (2004). LIS3L02AQ [Online]. Available: http://www.st.com/stonline/products/literature/ds/9321.pdf[21] Murata. (2001). ENC-03M [Online]. Available: http://lc.fie.umich.mx/�ifranco/DATASHEET/giroscopio_CI/s42e3.pdf[22] ADInstruments. (2005). MLT1133 [Online]. Available: http://www.adinstruments.com/products/hardware/corporate/product/MLT1133/[23] TinyOS. (2006). ‘‘Open-source operating system designed for wireless em-bedded sensor networks,’’ [Online]. Available: http://www.zigbee.org[24] NesC. (2003). nesC 1.1 language reference manual [Online]. Available:http://nescc.sourceforge.net/papers/nesc-ref.pdf[25] A.-A. Ylisaukko-oja, A.-E. Vildjiounaite, and A.-J. Mantyjarvi, ‘‘Five-pointacceleration sensing wireless body area network—Design and practical experien-ces,’’ presented at ISWC, 8th IEEE Int. Symp. Wearable Computers, Arlington,VA, 2004.[26] P. Bonato, ‘‘Advances in wearable technology and applications in physicalmedicine and rehabilitation,’’ J. Neuroeng. Rehabil., vol. 2, no. 1, p. 2, 2005.[27] NIFTE. (2003). National initiative for telehealth framework of guidelines,[Online]. Available: http://www.cst-sct.org/en/index.php?module=library&VV_DocumentManager_op=downloadFile&VV_File_id=54


in home telerehabilitation for geriatric patients

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