Design and Usability of a Home Telerehabilitation System to Train ...

Design and Usability of a Home Telerehabilitation System to Train 

Hand Recovery Following Stroke 

William K. Durfee 1 , Samantha A. Weinstein 1 , James R. Carey 2 , Ela Bhatt 2 , and Ashima 

Nagpal 2 

1 2 

Department of Mechanical Engineering, Program in Physical Therapy, University of 

Minnesota, Minneapolis USA 

Submitted to the Journal of Biomechanical Engineering (ASME), April 2006 

Address Correspondence to: 

William Durfee 

University of Minnesota 

111 Church ST S.E. 

Minneapolis, MN 55455 

tel: 612-625-0099 

email: wkdurfee@umn.edu 

Track-JBiomechEng.doc Page 1 of 31

ABSTRACT 

Current theories of stroke rehabilitation point towards paradigms of intense, concentrated use 

of the afflicted limb as a means for motor program reorganization and partial function 

restoration. TrackTrain is a home system for stroke rehabilitation that trains recovery of hand 

function by a treatment of concentrated movement. A wearable goniometer measured finger 

and wrist motions in both hands. An interface box transmitted sensor measurements in real- 

time to a laptop computer. Stroke patients used joint motion to control the screen cursor in a 

one-dimensional tracking task for several hours a day over the course of 10 to 14 days to 

complete a treatment of 1800 tracking trials. A tele-monitoring component enabled a therapist 

to check in with the patient by video phone to monitor progress, motivate the patient and 

upload tracking data to a central file server. The system was designed for use at home by 

patients with no computer skills. TrackTrain was placed in the homes of 24 subjects with 

chronic stroke and impaired finger motion, ranging from two to 305 miles away from the 

clinic, plus one that was a distance of 1,057 miles. Fifteen subjects installed the system at 

home themselves after instruction in the clinic, nine required a home visit to install. Three 

required follow-up visits to fix equipment. A post treatment telephone survey was conducted 

to assess ease of use and most responded that the system was easy to use. Functional 

improvements were seen in the subjects enrolled in the formal treatment study, although the 

treatment period was to short to trigger cortical reorganization. We conclude that TrackTrain 

is feasible for home use and that tracking training has promise as a treatment paradigm. 


1 INTRODUCTION 

Stroke is a major health problem. According to the American Heart Association (2005), in the 

United States, approximately 700,000 people suffer a first or recurrent stroke each year and 

stroke is the third leading cause of death after heart disease and cancer. On average, every 45 

seconds, someone in the U.S. has a stroke. Stroke is the leading cause of serious, long-term 

disability. About 4.7 million stroke survivors are alive today, and this number is increasing 

because of improved acute care. In 1999, more than 1.1 million adults reported difficulty with 

functional limitations resulting from stroke. The direct and indirect cost of stroke in 2005 was 

$56.8 billion. 

A common stroke disability is partial or complete paralysis of a limb, which leads to 

impairment of basic functional skills in 75% of people with stroke (Jorgensen et al., 1999). 

One of the most debilitating limb impairment is paralysis of the hand (Hummelsheim et al., 

1997; Nudo and Friel, 1999). Often, the individual will be able to close the fingers into a fist, 

which is part of the characteristic spastic flexion synergy, but is unable to open the fingers 

because of extremely weak finger extensor muscles and spastic tone in the antagonistic flexor 

muscles. As a result, the person cannot manipulate objects dexterously, which impairs 

feeding, dressing, handwriting, and occupational skills. 

Recent scientific findings fundamental to motor learning and neuroplasticity have challenged 

the traditional paradigms for motor rehabilitation following stroke. Functional loss in stroke 

occurs not only from cells that have died but also from reduced excitability in neurons that 

have survived the infarct (Feeney and Baron, 1986; Chu et al., 2002). The significance for 

rehabilitation is that there may be viable neurons available for recruitment in the stroke 


hemisphere, leading to the search for potent mechanisms that promote the restoration of 

voluntary recruitment of suppressed neurons. The literature is clear that movement retraining 

for stroke rehabilitation must be intense and must be prolonged and the patient must engage 

the task repeatedly and independently (Schmidt, 1988, Winstein et al., 1994). Importantly, 

self-improvement does not stem directly from the therapist coaching and guiding the activity; 

rather, improvement is thought to stem from internal information processing by the subject 

with consolidation of all the cues inherent to the task leading to a refined motor output 

(Winstein et al., 1994). Thus, subjects must be given opportunities to train themselves. The 

guiding and coaching efforts by therapists may be helpful in the short run but in the long run 

it will be the subject’s own information processing, including failed efforts, that will lead to 

the cellular and molecular changes that stimulate motor learning/relearning and a fuller 

recovery. 

One example of modern therapy that is based on motor learning principles is constraint- 

induced movement therapy. CIMT forces the patient to use the paretic limb while the healthy 

limb is restrained and was designed to overcome learned nonuse of the impaired limb (Taub, 

1980). Clinical studies have shown CIMT to be effective for patients who start with some 

residual range-of-motion (Liepert et al., 2000; Page et al., 2004; Ploughman and Corbett, 

2004, Taub et al., 1993; Taub et al., 1999). CIMT requires massed practice (practice time is 

greater than rest time) for 6-7 hours daily for two weeks with continual therapist supervision 

(Taub, 2000). 

Forced-use such as CIMT, however, may not be the most potent behavioral stimulus for 

neuroplastic change. Forced-learning may be even more effective. This is a pivotal question 

in rehabilitation as it dictates the type of tasks and level of cognitive engagement that 


therapists select for training in subjects with stroke. Repetitive execution of simple motor 

tasks may not be as effective in re-training motor skills as complex motor tasks that involve 

in-depth cognitive processing (Carey et al., 2005). A joint-movement tracking training system 

to promote recovery of movement control in stroke was applied to the hand of subjects with 

stroke (Carey et al., 2002) and to the ankle in a single-subject research design (Carey et al., 

2004) with favorable results. Unlike CIMT, tracking training emphasizes motor learning 

principles espoused by Schmidt (1991) that minimize guidance and involvement by a 

therapist, thereby potentially saving treatment cost. 

Conventional physical therapy for rehabilitation requires extensive interaction with a 

therapist, often for hours a day. These labor intensive efforts are costly and contribute to the 

staggering financial impact of stroke. Consequently, practitioners and administrators are 

challenged to find alternative methods to deliver quality health care at reasonable cost. One 

example of cost containment in stroke rehabilitation is home-based rehabilitation. Evidence 

has shown that early discharge from acute hospital care followed by home-based 

rehabilitation, involving professionals visiting the home, reduced costs while preserving 

treatment outcomes that were equivalent to conventional rehabilitation methods (Young and 

Forster, 1992, Widen Holmqvist et al., 1998, Beech et al., 1999, Anderson et al., 2000). With 

home-based treatment, patients can receive treatment at an intensity (hours per day) that is 

appropriate to their state of recovery rather than directed by health insurance policy, which 

may be too much early on or too little later on. Conventionally, rehabilitation for people with 

stroke is restricted to the first 6-12 months following the stroke, however, current research 

indicates that further recovery is possible with rehabilitation years after stroke (Kraft et al., 

1992, Kunkel et al., 1999, Liepert et al., 2000a, Carey et al., 2002, Luft et al., 2004). With 


home-based treatment, the amount of time devoted to rehabilitation is only restricted by the 

patient’s level of commitment and not by the cost of a therapist. 

Interest in technology driven, home-based stroke movement therapies is evidenced by the 

emergence of pilot projects such as JavaTherapy, a web application that uses standard input 

devices such as force controlled joysticks (Reinkensmeyer et al., 2002), and UniTherapy, a 

telerehabilitation platform for computer-assisted movitvating rehabilitation that supports force 

feedback training with standard and custom PC input devices (Feng & Winters, 2005). 

Robot-aided neurorehabilitation is another emerging technology based treatment for stroke. 

Grounded or wearable powered manipulators assist or guide the paralyzed limb through a 

desired motion in much the same manner as a classic hands-on physical therapy treatment. 

Upper limb devices include the MIT Manus (Krebs et al., 1998; Fasoli et al., 2003; Fasoli et 

al., 2004), Ketic Muscles’ Hand Mentor (Koeneman et al., 2004). Lower limb devices include 

the Locomat by Hocoma (Colombo et al., 2000) among many others. Preliminary results have 

shown that upper and lower limb robot therapy can be effective. Unlike the TrackTrain 

system described here, robots can be expensive and must be carefully designed to be safe 

before being considered for home use. 

Our group conducted a pilot study that used a finger movement tracking program where 

subjects with stroke received 18-20 one-hour rehabilitation training sessions using their 

paretic index finger to track target waveforms displayed on a computer screen under variable 

conditions (Carey et al., 2002). The subjects came to the clinic for treatment sessions with a 

physical therapist supervising. Pre and post training treatment functional magnetic resonance 

imaging (fMRI) was used to determine which parts of the brain were activated when the 

subject was performing the finger tracking activity. The results showed improved ability to 


grasp and release small objects, and a change in cortical activation from predominantly 

ipsilateral control (non-stroke hemisphere active) to contralateral control (stroke hemisphere 

active) during movement of the paretic finger. The difficulty with the pilot study was that 

subjects were required to come to the clinic for training sessions. This was not only 

inconvenient, but physically difficult and time demanding of the subjects. Further, in-clinic 

training sessions could last no more than 60 minutes while current massed practice 

rehabilitation theory points to the benefit of longer training sessions. 

In light of the promising results of the pilot study, our group has developed TrackTrain, a 

system that enables tracking training sessions in the subjects’ homes without a therapist 

present. With home-based training the subject only has to come to the clinic for pre and post- 

treatment evaluations. TrackTrain attracted more subjects to the study than clinic-based 

training because many eligible subjects are not interested in coming to the clinic daily, or live 

too far from the clinic for convenient daily travel. In addition, if home-based therapy proves 

to be effective than it could lead to new clinical treatment paradigms to serve larger numbers 

of patients with stroke at a lower cost. 

The main requirements driving the design of TrackTrain were first to create a system that 

required the patient to move his or her hand and wrist in a tracking task that required 

concentration to accomplish motor relearning, and second to create an easy-to-use system that 

could be operated autonomously at home without requiring technical expertise. In addition, 

the system needed a telecommunication mechanism for periodic real time interaction with a 

remote physical therapist to monitor, assist, and motivate the patient’s progress. The basics of 

the tracking task were established in the pilot study. Thus the challenge in this project was to 

design system that allowed patients to train at home without a therapist present. 


2 SYSTEM DESCRIPTION 

2.1 Overview 

The TrackTrain home tracking system has two custom bilateral electrogoniometers that sense 

wrist and finger flexion and extension motions, a microcontroller-based interface box, a 

laptop computer with tracking software, a cell phone, a web cam, and a land line telephone 

switch box (Figure 1). The system is designed for self installation in the patient’s home, or for 

installation by the therapist. While the system has many individual components, it is still 

relatively compact and fits easily on a desktop or table without being an overly obtrusive 

presence in the patient’s home. 

2.2 Hand Sensors 

 

The hand sensor has three platforms connected by rotating links, all made of polycarbonate. 

The sensor measures flexion and extension of the wrist and first MCP joints (Figure 2). The 

links form two four-bar mechanisms that transfer finger and wrist joint motion to two 0.5 

inch, 1.0K potentiometers (ETI Systems, SP12S-1K) located on the platforms. This design 

was chosen over potentiometers axially aligned with the joint because it afforded higher 

tolerance to placement location. Because the linkage straddles the joint, the two platforms at 

either end need only be on opposite sides of the joint and do not require precise placement. 

This is particularly important for a sensor that was designed to be self-donned by the patient. 

The links are designed to rotate relative to one another in the plane of the platforms which 

allows the sensor to fit multiple hands as the angle between the axis of the wrist and the axis 

of the forefinger differs between people. The sensor dimensions were determined by sampling 


male and female hands and consulting anthropometric data (Tilley, 2001). The relation 

between the joint angle and potentiometer angle is sufficiently linear over the typical range of 

motions seen that a linearization algorithm was not incorporated into the software. The sensor 

had small green LEDs on the two proximal platforms which flashed as a cue to the subject for 

which joint was active. 

 

The patient must don and doff the sensor independently. The platforms balance on top of the 

arm with relative stability. Hence, most of the difficulty in this task is associated with 

fastening and adjusting the straps. Two slap bracelets (fabric covered, metal strips that lie flat 

when bent in one direction and self-assemble into a circle when a small amount of pressure is 

applied in the opposite direction) were used for anchoring the proximal sensor platform to the 

forearm. A Velcro strap with a D-ring at one end was used to anchor the hand platform. The 

patient slides his hand into the looped strap and then pulls it back around on itself to tighten 

and secure the Velcro. The finger strap is flexible fabric ring that the patient slides his finger 

into. A spring loaded clip is used to pull up the slack in the ring and secure it at the correct 

diameter. Because the clip is difficult to operate with limited dexterity and strength, the ring 

size is adjusted by the therapist at the initial fitting session. Because the finger is conical, the 

patient can don the sensor by sliding his forefinger into the ring until it is snug. The links can 

be used as handles for device removal. 

2.3 Interface box 

The sensor interface box converts the potentiometer reading into a digital signal that is 

transmitted to the computer through its serial port. The interface box contains analog signal 


conditioning circuitry including programmable gain and offset, a 20 Hz, two-pole low pass 

filter, and a programmable multiplexer that selects which channel to digitize (Figure 3). 

Signals are digitized at 100 Hz with a 10-bit analog-to-digital converter. Software on a 

PIC16F873 microcontroller controls the peripherals, creates the real-time sampling loop, and 

sends the signal to the PC serial port. The host program running on the PC communicates 

with the microcontroller to enable and inhibit sampling, and to set current channel, gain and 

offset. A four stage running average filter implemented on the PC performed additional 

smoothing of the sampled signal. 

 

The sensor interface box has a large red button on the top cover, a small black button on the 

back, and two color coded jacks for the left and right hand sensor cables on the front . The red 

button is the only control the user needs to operate the program. Through prompts on the 

computer display, the red button is used to start the treatment, pause the treatment, calibrate 

the hand sensors, and end the treatment. The black button is used for unusual circumstances to 

abort the program and immediately shutdown the computer. 

2.4 Computer 

The system control unit is a Dell Latitude D600 laptop computer running the Windows XP. 

The computer is treated as an appliance with the patient never having to use the keyboard or 

mouse. The laptop mouse was not connected and the keyboard was covered with a screen. 

The keyboard and mouse were not used for several reasons. Keys are close together which 

increases the likelihood that a patient with limited dexterity might press the wrong key. Using 

a mouse requires fine motor control which is lacking in the users, and also requires familiarity 


with computers. Access to the keyboard and mouse could afford technologically savvy 

patients a means of getting into the inner workings of the system, which is not desired. The 

red button on the interface box and the laptop’s power button were the only two controls 

needed by the subject to operate the system. On power-up the computer immediately boots 

into the tracking program and from there, all interaction by the user was done with the single 

red button on the interface box. 

2.5 Software 

The tracking application on the PC was created using Microsoft Visual Basic 6.0. On power- 

up, the PC automatically started the application. With both sensors mounted, the subject went 

through a prompted calibration sequence with the display instructing the subject to open and 

close their hand and flex and extend their wrist as far as possible which automatically 

communicated the subject’s active range of motion to the application (Figure 4). The red 

button was pressed to sequence through the joints. If the measured range of motion was less 

than a threshold, it was likely that the sensor had been misapplied and the subject was advised 

through on-screen instructions to telephone the therapist for advice. The sensors must be 

calibrated each time the program is started as there is no guarantee that the sensor was placed 

on the hand the same way each time. At the completion of calibration, the subject pushed the 

red button to commence the day’s therapeutic tracking training session. 

 

The first screen of a trial contained four key pieces of information for the subject (Figure 4). 

At the top of the screen, the number of blocks left in the session and the number of trials left 

in the block are shown. Instructions on how to pause the program by pressing the red button 


are given in the center of the screen. Text and pictorial indicators of the hand, joint, and arm 

position to be used for the next tracking task are shown at the bottom of the screen. There 

were two LEDs on the hand sensors, one located at the wrist and the other at the finger. The 

LED on the hand and joint to be used in the next trial blinked throughout the first and second 

trial screens indicating which joint would be used for the next tracking trial. A countdown 

clock displayed the time remaining to the start of the next tracking task. 

The second screen in the trial sequence was the tracking task (Figure 5). At the start of the 

trial, a green ball was displayed on the far left of the box with the joint motion controlling the 

vertical motion of the ball. The patient had one second at the start of the trial to move the ball 

up and down to get a feel for the range of motion through which he would be working. After 

the one second delay, a beep sounded and the ball began to move horizontally across the 

screen at a fixed rate determined by the waveform duration. The task of the subject was to 

move the green ball follow the blue target by moving the specified joint up and down. The 

ball left a red trail for visual feedback to the patient. When the ball reached the right end of 

the box, a double beep indicated the end of the trial. The third trial screen showed a total 

tracking score to indicate the subject's performance (Figure 5). 

 

Eleven tracking performance scores (Carey, 1990; Schmidt and Lee, 2005) were computed 

and stored for each trial. The scores and associated computation algorithms are shown in 

Table 1. The subject is shown 1000*AI during the rest period after each trial as their 

performance score on the trial. The score ranges from a large negative number if the tracking 

is anti-phase to 1000 for perfect tracking. 


Following some trials, the screen also displayed text providing automated feedback on 

performance. The feedback alerted the subject to areas of weak performance, such as wrist or 

finger extension, or alerted the subject to excessive or insufficient frequency of movement. 

The feedback was generated based on the 11 tracking performance metrics that were 

calculated after every trial. This more descriptive information is known as “knowledge of 

performance” (KP). KP feedback was shown to subjects in a faded schedule after certain 

blocks of trials being shown every third block on day one and gradually declining to every 

20th block by day 10. The purpose of the faded feedback is to reduce reliance on external 

guidance and instead promote internal error-detection capability (Schmidt, 1991). The KP 

information, when it appeared, was based only on the immediately preceding trial and was 

always complemented with an encouraging comment to motivate continuing effort. An 

example is, "You are not reaching the tops of the upper blue waves. Try to reach higher and 

touch the peaks," and "Mrs. Jones, your performance is great! Keep it up!" 

The treatment plan was 180 tracking trials per day for 10 days, a total treatment of 1800 trials. 

The trials were organized into 60 blocks with three identical trials per block. The blocks were 

randomly selected from a set of 100 test conditions which varied in target wave shape, 

frequency, amplitude, duration, joint used, hand used, hand posture and visual feedback 

(Table 2). The variations were needed to keep the patient concentrated on the task. The paretic 

hand was used for 90% of the blocks and the nonparetic hand for 10%. Tracking with the 

nonparetic hand was included to take advantage of cross education. Cross education is the 

principle of training in which effort unilaterally has beneficial effects bilaterally (Greenough 


et al., 1985; Imamizu et al., 1998; Shields et al., 1999). Hand posture (palm up/down), where 

for the up case, cursor movement is opposite the joint movement, was included to case greater 

complexity and depth of cognitive processing for the task (Carey et al., 1994, 1995, 1998). In 

some blocks, the ball and red trail were replaced by a vertical hairline cursor that sweeps 

across to show timing but provides no visual feedback on joint location. Every trial was 

followed by a rest period of the same duration of the preceding trial, constituting distributed, 

rather than massed, practice (Dail, 2004; Lee, 1989). 

 

Once the training session started, other than tracking, the only action required by the subject 

was to press the red button whenever a pause was desired. If the subject did not press the 

pause, the computer continued until all 180 trials were done that day. Most subjects choose to 

pause periodically. If the pause was less than five minutes, pressing the red button to resume 

brought the subject immediately back to the previous training point. For longer pauses, the 

program assumed the subject had removed the sensors and pressing the red button to resume 

went back to the calibration screen. The application informed the subject when the day’s 

session was complete and automatically shut down the computer. The automatic shutdown 

regulated the rate of rehabilitation to the prescribed regimen of one session per day. The 

application had additional branch points to detect and exit gracefully if the sensor was still 

(meaning the subject may have left the room), the sensor was disconnected from the interface 

box, the computer was shut down, or power was removed from the sensor box. 

2.6 Telecommunication 


Telecommunication took place between therapist and patient every few days. The periodic, 

real-time, face-to-face interaction with a therapist was for monitoring, motivating and 

assistance, and is essential for patient compliance with the rigorous protocol. We assumed 

every subject had a land-line telephone and cellular phone coverage, but not broad-band 

internet. Based on pilot work, we assumed that high quality audio was essential for 

meaningful communication, but low quality video was adequate. Voice communication was 

by cell phone (TracPhone). Two-way video and data transmission was by web cameras 

(Logitech QuickCam 4000) and dial-up internet access, resulting in low quality (color, 

128x96, 3 frames per sec) video imaging. 

To initiate a tele-session, the therapist called the subject on the cell phone (after first alerting 

the subject with a landline phone call as the subject is not used to answering the cell phone). 

The cell phone was set for auto-answer on any button push so that motor-impaired subjects 

did not have to manipulate small buttons. A cell phone speaker phone accessory was used so 

that the subject could communicate hands free. The therapist instructed the subject to flip the 

switch on the telephone interface box, which connected the land line to the PC modem, then 

power up the computer. On boot up, the PC automatically detected the dial tone, dialed the 

ISP, connected to the host computer, and from there the therapist could download trial data 

and remotely start the web cam. After connecting, the subject could see the therapist’s face 

on the computer display, and the therapist could see the subject or could ask the subject to 

hold his or her hand in front of the camera to see whether the electrogoniometer was attached 

properly. During the communication the therapist answered questions and provided helpful 

comments and motivation. At the end, the therapist could remotely shut down the subject's 

computer. The session ended with the therapist instructing the subject to toggle the switch 


setting on the telephone interface box so that the next time the subject computer powered up, 

it would boot into tracking treatment mode. 

3 EVALUATION STUDY 

3.1 OBJECTIVES 

The tracking training evaluation study had two objectives. First, to determine if home-based, 

self-administered tracking training with only occasional teleconferencing with a remote 

therapist is feasible, and second whether tracking training was more beneficial for cortical 

reorganization and improved motor control than movement training. While both questions 

were examined in the study, this paper is concerned with the first objective. A companion 

paper (Carey et al., 2006) contains the complete results for the second objective. 

3.2 METHODS 

The tracking training system was placed in the homes of 24 subjects ranging from 2 to 307 

miles from the University (median = 18 miles), plus one 1057 miles away. Subject inclusion 

criteria were post-stroke duration of at least 12 months; satisfactory corrected vision to detect 

the position of a cursor on the computer screen as being slightly above, below or exactly on a 

target line; and at least 10 degrees of active extension-flexion movement at the index finger 

metacarpophalangeal (MP) joint of the paretic hand. All subjects had sufficient cognition to 

perform the training and testing with Mini-Mental State Examination (Folstein, 1975) scores 

of 25 or higher except for one subject with a score of 21, who was still deemed appropriate to 

participate. Exclusion criteria were indwelling metals or medical implants incompatible with 

functional magnetic resonance imaging (fMRI), pregnancy and claustrophobia. The mean age 

(± SD) of the subjects was 66.7 ± 9.6 years. The mean stroke duration (time from stroke onset 


to entrance into study) was 39.1 ± 24.8 months. The study was approved by the University of 

Minnesota IRB. 

Twenty of the 24 subjects were in the controlled training study with 10 in the tracking training 

group and 10 in the movement group. Subjects in the movement group performed the same 

number of movement trials, as directed by the computer display, but neither target waveform 

nor response line was displayed and thus the subject was not required to problem-solve nor 

concentrate to perform the task. 

During the pre-treatment evaluation session at the University, subjects were trained in use of 

the equipment, and watched a video clip that showed the steps for setting up the equipment at 

home. The video was stored on the tracking laptop for later review at home. The subject and 

family member practiced the equipment setup and performed six tracking trials under 

supervision of the therapist. The subject was then sent home with the equipment. Subjects 

completed the 1800 trials in 10 to 14 days. The therapist communicated with the subject about 

5 times using the teleconferencing system. The therapist was also available by pager. 

Pre and post treatment evaluations included Box and Block (Mathiowetz, 1985), Jebsen- 

Taylor Hand Function (Jebsen, 1969), finger range of motion, finger movement tracking 

accuracy, and fMRI voxel count, intensity and laterality index. The fMRI measures were 

included to evaluate cortical reorganization. Details of these evaluations are in Carey et al. 

(2006). A telephone usability survey was conducted following the completion of the 

treatment. The survey contained six questions with a five point Likert response scale. 


3.3 USABILITY RESULTS 

Fifteen of the 24 subjects were able to install the system by themselves while nine required a 

home visit for installation. An additional four home visits were required to correct a computer 

or equipment malfunction or equipment. One subject dropped out because they were unable to 

independently don and doff the sensor and had no companion to help. Teleconferencing 

sessions were conducted with 22 of 24 subjects. All 20 subjects in the controlled study 

completed the 1800 trial treatment. Nineteen of the 24 subjects completed the usability survey 

whose results are shown in Table 3. Two of the 19 had no teleconferencing sessions and did 

not answer Question 6. 

 

Off those subjects completing the usability survey, all agreed they "understood how to use the 

system" and that "the computer was easy to use." Fourteen could independently don and doff 

the sensors. Fifteen did not feel the system interfered with their daily routine, 15 saw the 

automated feedback as being useful and 16 found the teleconferencing sessions easy to 

manage. In self reported comments associated with the survey, almost all of the subjects 

commented on the convenience of being able to work on the treatment at their leisure, fitting 

it around their normal daily routine. About one quarter of the subjects had difficulty donning 

and doffing the sensors by themselves, which was probably correlated to severity of the 

stroke. The most consistent complaint was putting on the electrogoniometers independently, 

particularly using the paretic hand to apply the electrogoniometer to the nonparetic hand, 

which took some subjects 10-15 minutes or more. 


3.4 TREATMENT RESULTS 

Complete information on treatment results are in the companion paper (Carey et al., 2006) 

and only a summary is presented here. The Track group showed improvement in Box and 

Block, Jebsen Taylor, finger range of motion and finger tracking while the Move group 

showed improvements only in Box and Block and Jebsen Taylor. Unlike our earlier clinic 

based finger tracking training project that demonstrated brain reorganization (Carey, 2002), 

the fMRI data in this study showed no significant evidence of brain reorganization for either 

group. 

4 DISCUSSION 

The subjects were tolerant of the TrackTrain system. The concern that elderly people 

unaccustomed to complications of electronic technology might drop out of the study did not 

materialize. The subjects simply needed to be gently trained in the system operation and be 

reassured that help was only a phone call away. Subjects who were hesitant at first became 

excited by the challenge to continually do better. The evaluation project confirmed that an 

easy-to-use, simple user interface, coupled with an initial instruction session is essential for 

successful home use. 

The hand sensor could be improved to make it easier to don and doff. Next generation sensors 

should be wireless to eliminate cable interference, should not have the linkage bars above the 

wrist, and should have a simplified attachment process. Because of the theoretical value of 

cross-education (Greenough, 1985; Imamizu , 1998; Shields, 1999; Luft, 2004), the training 

effort with the nonparetic hand should be retained, increasing the challenge for a usable 

sensor design. 


The current system takes up most of a desk when all the cables, boxes and communications 

equipment is placed. Future systems must be compact and even simpler to set up. In 

particular, the number of connections must be reduced by either combining system 

components into a single appliance, or mounting components on a single board or in a single 

package. Ideally, the subject should only have to plug into a power outlet and connect to the 

telephone or broadband. 

Periodic therapist contact was essential for reassuring and motivating patients to complete the 

protocol. Visual contact by video proved important for two reasons. First, the visual presence 

of the therapist often provided sufficient motivation for the patient to continue the treatment. 

Second, if the patient was having difficulties with the sensor, the therapist could see the 

problem and provide a solution. Future systems would benefit from a pan-tilt-zoom camera 

that could be remotely controlled by the therapist. The teleconferencing technology used in 

this project demonstrated that while high quality audio is essential, low quality video is 

acceptable. Broadband connections are preferable to increase the quality of the video and 

simplify the process of automatically downloading tracking data to a central database each 

night. While the penetration of cable and DSL broadband to remote areas is still lagging, the 

recent rise of wireless broadband access through cellular phone networks promises broadband 

coverage to most areas. 

The controlled study showed that tracking training had a positive effect on finger range of 

motion, finger tracking, and functional hand dexterity, demonstrating the potential for clinical 

efficacy. We suspect that the two weeks of training was too short to differentiate between the 

Track and Move groups and too short to show the brain reorganization that was evident in our 

earlier study. Future studies will spread the treatment over a longer time period. 


One might argue that training by tracking waveforms on the screen is boring and instead that 

joint motion should drive a video game. There are at least three reasons for not taking the 

video game approach. First, it is not clear what video game would be motivating for hours at a 

time to elderly subjects with stroke. While teens can become addicted to a single video game 

or to a category of video games, motivation drivers have not been studied for our target 

population. Thus, choosing any one or any collection of video games may not solve the 

motivation problem. Second, controlling a video game does not permit control over 

amplitude, frequency and type of movement. With structured waveforms, TrackTrain can 

provide targets at several frequencies and specific amplitudes scaled to the patient’s active 

range of motion, and vary parameters in predetermined or adaptive paradigms as needed for 

treatment. Third, in this study patients were sufficiently motivated by target tracking to 

complete long, daily sessions of treatment. 

5 CONCLUSION 

The study demonstrated the technical feasibility of a home based tracking training system for 

motor rehabilitation following stroke. Home-based treatment using TrackTrain resulted in 

functional improvements similar to those seen for the equivalent clinic-based system and 

subjects indicated that they preferred the home system for its convenience. The ability of 

subjects to successfully complete a treatment plan showed that elderly patients can understand 

and use the system without continual supervision intended. Improvements are needed to 

eliminate the sensor wires and to simplify telecommunication. Future treatment must be 

spread out for longer than two weeks for significant cortical reorganization. 


ACKNOWLEDGMENTS 

This work was supported by the National Institute on Disability and Rehabilitation Research 

(U.S. Department of Education #H133G020145-04) and the National Institutes of Health 

(National Center for Research Resources #M01-RR00400 and Biomedical Technology 

Research Resources #P41-R008079). 


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Figure 1: TrackTrain home tracking system. 

Figure 2: Hand sensor. 


LEFT 

SENSOR 

RIGHT 

SENSOR 

9V 

WALL 

SUPPLY 

Figure 3: Interface box. 

MCP6S26 

MUX 

MCP41100 

OFFSET 

5V 

VREG 

PWR 

2-POLE 

20 HZ 

FILTER 

Figure 4: Calibration (left) and pre-trial (right) screens. 

Figure 5: Tracking (left) and post-trial feedback (right) screens. 

Track-JBiomechEng.doc Page 29 of 31 

PIC16F873 

CONTROLLER 

MAX233 

LEVEL 

SHIFT 

BUTTO 

COMM 

PORT 

HOST PC

Table 1: Performance scores 

T = target trajectory, X = response trajectory, N = number of data points in record 

Metric and Name Computation Algorithm Interpretation 

E = Total Error = RMS Error 

1 / 2 

⎛ 1 

2 ⎞ 

⎜ ∑( X i − Ti 

) ⎟ 

⎝ N 

⎠ 

RMS error between response and 

target. 

CE = Constant Error = Average 

Error 

1 

∑( X i − Ti 

) 

N 

Average error between response 

and target. Reveals bias above or 

below target. 

VE = Variable Error = Standard 

Deviation 

1/ 

2 

⎛ 1 

2 ⎞ 

⎜ ∑( 

X i − Ti 

− CE) 

⎟ 

⎝ N 

⎠ 

Standard deviation of the 

response about the bias. Measure 

the spread of the response. 

CE/VE CE / VE 

>1 for response far from target 

but consistent, 0 means response lags target. 

Fade (First half RMS)/(Last half RMS) > 1 means response getting 

smaller as trial proceeds. 

Frequency (Zero crossings of 

Large score means subject has 

response)/(Zero crossings of 

target) 

tremor. 


Table 2: Tracking block conditions 

Parameter Units Conditions 

Wave Shape n/a triangle, left-saw, right-saw, square, pseudo-random 

Frequency Hz 0.2, 0.4, 0.8 

Duration Sec 5, 10, 15, 20 

Range % of ROM 0-50, 30-70, 50-100, 0-125 

Joint n/a wrist, finger 

Side n/a paretic, nonparetic 

Posture n/a pronated, supinated, mid 

Visual Feedback n/a on, off 

Table 3: Usability survey results 

Questions N 

Track-JBiomechEng.doc Page 31 of 31 

Strongly 

Disagree Disagree 

No 

Opinion 

Agree 

Strongly 

Agree 

1. I understood how to use the system 19 9 10 

2. The computer was easy to use 19 9 10 

3. I was able to put on and take off the hand 

sensors by myself without problems 

19 1 4 10 4 

4. The physical system setup in my home 

interfered with my daily routine 

5. The scores and comments I received after 

each trial helped me 

6. It was easy for me to communicate with 

the therapist during a tele-session 

19 9 6 4 

19 3 1 14 1 

17 1 10 6

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