Rehabilitation following a neurological

Transcription

1 C l i n i c a l E l e c t r o n i c s Rapid Prototyping for Functional Electrical Stimulation Control he Clinical Set-up ool integrates fast parameter selection and a user-friendly interface to help electrical muscle stimulators more efficiently treat patients with neurological injuries. Philip A. resadern University of Liverpool and University of Salford Sibylle B. hies, Laurence P.J. Kenney, and David Howard Centre for Rehabilitation and Human Performance Research, University of Salford John Y. Goulermas University of Liverpool Rehabilitation following a neurological injury, such as a stroke or spinal cord injury, aims to help patients accomplish daily activities. Functional Electrical Stimulation (FES) is a rehabilitation method that uses electrodes (either on the skin or implanted in underlying tissue) to stimulate motor neurons with a pulsing current (see the sidebar). his can promote recovery either implicitly (via motor relearning) or explicitly (by eliciting functionally useful muscle contractions). 1 However, a key challenge in increasing FES systems clinical acceptance is facilitating or automating parameter selection, optimization, and programming to make the underlying engineering transparent to the user. o this end, we present the Clinical Set-up ool (CS), a finite-state-machine-based controller that integrates accurate, automatic parameter optimization in an intuitive user interface. Unlike other approaches, 2 we employ a numerical algorithm that uses real-life data and well-defined criteria to rapidly optimize parameter values. By letting users experiment with different stimulation patterns, the CS makes FES technology accessible to clinicians (and potentially patients or their caregivers). Furthermore, the CS is easy to use and has considerable potential for implementation in a portable computing device (such as a mobile phone), making it well suited for ubiquitous applications. System overview We demonstrate the CS (see figure 1) using a drinking task, but you can also use the same flexible framework for other applications (for example, lower-limb stimulation). For this drinking task, we measure acceleration from the forearm of a stroke patient with reasonable shoulder and elbow movement. his impairment level, not uncommon in the stroke population, is well suited to acceleration-based FES triggering. On the basis of the acceleration input, we use FES to extend the patient s wrist and open his fingers, thus restoring his limited hand function by assisting in grasping and releasing an object. Focusing solely on the hand maintains clinical relevance while limiting stimulation complexity at this stage in the CS development. Currently, clinicians fit the stimulator that is, they locate electrodes and set pulse amplitude to achieve the desired response then use the CS to design state machines, collect data, and program the stimulator. However, a clinician could train the patient to use the CS to collect data and program the stimulator himself. his would increase the CS s scope and the patient s interaction with the device, motivating the patient to use and accept FES treatment. Hardware For this application, we strap a stimulator to the patient s affected forearm with surface electrodes over the nerves supplying the muscles that open 62 PERVASIVE computing Published by the IEEE CS n /8/$ IEEE

2 Figure 1. he Clinical Set-up ool software. he tree-view component (outlined in red) displays the finite-state machine for an example stimulation sequence. the fingers and thumb and extend the wrist (figure 2). he stimulator, designed by Salisbury District Hospital, was based on a Microchip PIC18F452 microprocessor and equipped with two output stimulation channels and a gravitysensitive, internal 2-axis accelerometer. he system has a basic hardware interface (an LCD screen and push buttons on the stimulator itself) for low-level, manual programming by a medical engineer; the CS aims to replace this interface so that a clinician can program the stimulator. hree signals are available for stimulation triggering: x-acceleration (X), y- acceleration (Y), and time (). A twoway USB connection lets you control stimulation from a PC while collecting data from the accelerometer. hese three input signals are sufficient for the drinking task. Furthermore, the CS supports multiple devices such that you can increase the number of inputs for complex or multiple tasks. he accelerometer (Analog Devices ADXL22) has a full-scale range of ±2 meters per second squared, a resolution of ~.4 m/s 2 and a bandwidth of 1 Hz, and is sampled at 4 Hz. herefore, the stimulator s response time is approximately 25 milliseconds during stand-alone operation well below a suggested upper limit of 2 ms for functional tasks. 3 Under static conditions, the accelerometer s output varies by one to two quantization levels (.4.8 m/s 2 ). Compared with natural movement variation (especially following neurological injury), this level of noise is unlikely to affect triggering accuracy enough to reduce confidence in the system s detection of state transitions. he stimulator delivers a current to the nerve via two surface electrodes Figure 2. A USB-enabled surface stimulator. Worn on the forearm, the stimulator helps the patient drink from a glass. stuck to the skin with an adhesive, conductive layer of hydrogel. We place the electrodes on the upper forearm so that stimulating the radial and other forearm nerves extends the wrist and opens the fingers. We apply stimulation pulses at 4 Hz with a pulse width of up to 2 µs and set the current amplitude (limited to 8 ma) during stimulator fitting to elicit the desired muscular response. Although we demonstrate the CS using surface electrodes, the APRIL JUNE 28 PERVASIVE computing 63

3 Clinical Electronics Functional Electrical Stimulation Determining when to apply stimulation remains a major challenge. 1 In simple cases, a system can trigger stimulation using an external keypad (for functions performed only occasionally), a timer (to exercise affected muscles), or position sensors attached to exercise equipment (for more functional exercise). More complex applications to the upper limb let patients control Functional Electrical Stimulation (FES) via button presses, 2 head movements, 3 voice commands, 3 contralateral shoulder position, 4 and specific trigger thoughts detected via electroencephalography. 5 In contrast, for daily activities (where timed or manual control is often impractical), it s desirable to reduce the amount of conscious control required of the patient so that less-able patients can also benefit. One successful example uses FES, controlled by a switch located under the patient s heel, to lift the paretic foot during the gait s swing phase. 6 his increases walking speed and energy efficiency and reduces the likelihood of tripping. Several applications have attempted to emulate this success for the upper limb by using wrist position sensors, 7 electromyography, 8 and limb acceleration. 9 However, upper-limb motion is considerably more complex than lower-limb motion because it s nonperiodic and unconstrained. In particular, motion of the impaired upper limb varies enormously over time, across tasks, and across patients, depending on the injury s severity. herefore, the control system must be highly individualized to each patient s needs. 1 Designing a natural control algorithm is also complicated because desired stimulation onset and termination don t correlate with salient events (such as foot-ground contact), and simple binary input devices (such as footswitches) rarely apply. So, systems must employ sensors that provide a continuous output. o cope with this increased complexity, a typical control algorithm requires many parameters to be appropriately specified for accurate stimulation control. However, having a medical engineer specify parameters by hand quickly becomes impractical because the engineer must rely on experience and intuition to set up each patient s controller. Furthermore, you need to update parameters regularly to account for changes in sensor output, both long-term (as a result of patient recovery) and short-term (due to the patient donning and doffing the sensor). In practice, therefore, parameter optimization must be quick and straightforward for the user. Machine learning methods, with their automatically optimized parameters, can help address this problem, but their complexity means that they rarely have a clinical interpretation and are ill suited to implementation on a low-power, embedded processor. Furthermore, although others have used these methods in lower-limb studies, 11,12 such approaches have had limited success in clinical trials of upper-limb FES and have largely been confined to a research setting. In contrast, an approach with widespread application in clinical systems is to break down the motion into a sequence of states and progress through the sequence via state transitions based on input control signals. Effectively, each state has an associated classifier that assigns the input at that instant to a state the system might transition to; crossing between states triggers the corresponding state transition. Crucially, segmenting input data into smaller subsequences simplifies classification such that you can often detect state transitions using less-complex methods that are well suited to an embedded implementation (such as threshold crossings). Applying stimulation during selected states assists the patient at the appropriate time and restores functionality to the impaired limb. his finite-state machine (FSM) structure can generate complex control commands with clinically interpretable results. underlying principles apply equally to implanted FES systems. Example task he FSM we use for this application is part of a complex state machine (figure 3). he subject begins in a neutral position (Neutral) before raising his forearm and reaching toward the glass. At this point, stimulation switches on to open the hand for a short time (Open) before switching off again, letting the hand close. he subject then lifts the glass to his mouth (Lift) before placing the glass on the table (Place). Stimulation switches on again to help the patient release the glass (Release) before switching off again after a short time, as the subject returns his arm to the starting position (Neutral). his example exploits a simplified, cyclic structure where each state can progress to only one other state. ransitions from Neutral to Open, Lift to Place, and Place to Release result from changes in sensor output (X or Y) because timed transitions aren t appropriate where the patient might not complete the whole motion at exactly the same speed. Conversely, to ensure that stimulation doesn t remain on indefinitely, a time-out triggers the Open to Lift and Release to Neutral transitions. You can implement several movements (including more complex structures such as voluntary trigger movements that could reduce false triggers) using a single state machine (figure 3). Currently, the patient uses the keypad to select the task before commencing, thus limiting the device s usability. If the system were to support many tasks, se- 64 PERVASIVE computing

4 However, as with the other systems we ve described, appropriately specifying the classifier parameters (that is, which inputs must satisfy what conditions for a transition to occur) is critical to any finite-state approach s success. he Crest (Clinical Rehabilitation using Electrical Stimulation via elematics) project connected a relatively untrained clinician to a highly trained engineer via teleconsultation to provide technical support while optimizing patient equipment. 13 In related work, Milos Popovic and hierry Keller developed a GUI that simplified stimulator programming by letting users drag and drop events (for example, input triggering conditions or output stimulation patterns), thus creating a FSM that generated the desired stimulation output. 14 However, neither system helped users select appropriate control parameter values. Instead, a medical engineer selects values using intuition and experience. hese systems are the state of the art in a clinical setting. References 1..R.D. Scott and M. Haugland, Command and Control Interfaces for Advanced Neuroprosthetic Applications, Neuromodulation, vol. 4, no. 4, 21, pp G.J. Snoek et al., Use of the NESS Handmaster to Restore Hand Function in etraplegia: Clinical Experiences in en Patients, Spinal Cord, vol. 38, no. 4, 2, pp Y. Handa et al., Functional Electrical Stimulation (FES) Systems for Restoration of Motor Function of Paralyzed Muscles Versatile Systems and a Portable System, Frontiers of Medical & Biological Eng., vol. 4, no. 4, 1992, pp M.W. Johnson and P.H. Peckham, Evaluation of Shoulder Movement as a Command Control Source, IEEE rans. Biomedical Eng., vol. 37, no. 9, 199, pp G.R. Muller-Putz et al., EEG-Based Neuroprosthesis Control: A Step owards Clinical Practice, Neuroscience Letters, vol. 382, nos. 1 2, 25, pp W.. Liberson et al., Functional Electrotherapy: Stimulation of the Peroneal Nerve Synchronized with the Swing Phase of the Gait of Hemiplegic Patients, Archives of Physical Medicine and Rehabilitation, vol. 42, 25, pp A. Prochazka et al., he Bionic Glove: An Electrical Stimulator Garment hat Provides Controlled Grasp and Hand Opening in Quadriplegia, Archives of Physical Medicine and Rehabilitation, vol. 78, no. 6, 1997, pp S. Saxena, S. Nikolic, and D. Popovic, An EMG-Controlled Grasping System for etraplegics, J. Rehabilitation Research and Development, vol. 32, no. 1, 1995, pp P.E. Crago et al., An Elbow Extension Neuroprosthesis for Individuals with etraplegia, IEEE rans. Rehabilitation Eng., vol. 6, no. 1, 1998, pp M.R. Popovic et al., Neuroprosthesis for Retraining Reaching and Grasping Functions in Severe Hemiplegic Patients, Neuromodulation, vol. 8, no. 1, 25, pp Chau, A Review of Analytical echniques for Gait Data, Part 1: Fuzzy, Statistical and Fractal Methods, Gait and Posture, vol. 13, no. 1, 21, pp Chau, A Review of Analytical echniques for Gait Data, Part 2: Neural Network and Wavelet Methods, Gait and Posture, vol. 13, no. 2, 21, pp M.H. Granat et al., Clinical Rehabilitation using Electrical Stimulation via elematics (Crest), Proc. Int l Functional Electrical Stimulation Society, Ifess, 1997, pp M.R. Popovic and. Keller, Modular ranscutaneous Functional Electrical Stimulation System, Medical Eng. & Physics, vol. 27, no. 1, 25, pp lecting the tasks by hand would be even more tiresome for the patient, and automatic task switching would be highly desirable. However, this automatic task switching would require more inputs to differentiate between tasks in the early stages of movement, making it even more impractical to specify parameters by hand. As a result, no clinical system has attempted to control multiple tasks automatically. he CS might be a good candidate for attacking this problem because it s flexible, supports multiple devices (possibly of different sensing modalities), rapidly searches very many inputs for optimal parameters, and automatically programs the hardware even for a complex state machine. However, automatic task switching is beyond this article s scope. Control algorithm As the arm moves, changes in the accelerometer output owing to inertia and gravitational forces trigger the sequence of state transitions that produce an appropriate stimulation response. In particular, each transition occurs when a selected input (X, Y, or ) rises above or falls below a specified threshold. he corresponding control algorithm (running at 4 Hz on the embedded microprocessor within the stimulator) reads the accelerometer values, computes whether a state transition occurred, updates the system s internal state, and generates an appropriately sized electrical pulse. Without the CS, a medical engineer must specify control parameters using intuition and experience. As a result, the stimulation system has several limitations: APRIL JUNE 28 PERVASIVE computing 65

5 Clinical Electronics Figure 3. An example state machine Neutral Release Open ArmUp Release2 Replace Lift ArmDown Place2 Open2 Lift2 diagram. Arrows indicate state transitions, labeled by the inputs that can trigger the transition x-acceleration (X), y-acceleration (Y), and time (). he system applies stimulation only during Open, Release, Open2, and Release2. he upper subsequence (indicated by a dotted box) corresponds to the example drinking task. he lower subsequence corresponds to a less natural but more distinctive movement, whereby the patient raises and lowers his arm within a fixed time limit to voluntarily trigger stimulation. he engineer must use qualitative feedback (such as visual observation) rather than a quantitatively measurable performance indicator to assess the effect of changing parameter values. ime is limited during stimulator fitting, so you can only test a few sets of parameter values, making suboptimal results likely. Programming the stimulator using trial and error makes it difficult to check whether performance improved because of better parameters or because the patient moved differently potentially lowering confidence in the updated parameter values. Programming the stimulator via its LCD screen and three buttons is time consuming and tedious. he CS addresses these problems by offering automatic parameter optimization and an intuitive user interface. Parameter optimization he CS s first major contribution is automatically selecting optimal parameter values. Broadly speaking, we specify an ideal stimulation output via a sequence of button presses. hen the software looks for algorithm parameter values that result in similar stimulation timing. More specifically, for each state transition, the CS searches all possible combinations of parameter values to find optimal values that minimize a quantitative error measure that s based on real-life data. So, the clinician or even the patient could program the stimulator instead of the medical engineer, because substantially less skill is required. his would lead to increased clinical acceptance and more widespread use of the device. Furthermore, because the system selects parameter values based on accurately labeled state transition timings with respect to input signals, we expect the resulting stimulation timing to be more accurate, leading to increased functionality and patient satisfaction. he first step is collecting input data as the patient performs the task. By repeating the task several times, we average out sensor noise, movement variation, and other artifacts (such as tremors) that the neurological injury might have caused. For each repetition, the clinician clicks a button to indicate instants in time where state transitions should occur, thus labeling the data according to its corresponding state. For each state transition (such as Neutral to Open), the CS then collates all collected data corresponding to the previous state (Neutral) and next state (Open). he system then looks for parameter values that correctly classify as many points as possible based on a quantitative error metric. Specifically, the system uses an error metric that, for each repetition, finds the first sample occurring immediately after the transition occurred (as defined by the parameter values being tested). It then labels all previous samples (within the same repetition) as previous and all subsequent samples as next, regardless of their value: the error for those particular parameter values is the total number of misclassified points over all repetitions. Although this error function is complex (to respect the temporal ordering of samples in time), for a state transition with d inputs and N collected samples, the parameter combinations are limited to 2dN (because the transition might have been triggered by an input rising above or falling below the threshold). In practice, this results in only a few thousand possible combinations, such that a brute-force exhaustive search takes less than a second, eliminating the trial-and-error approach to parameter specification. We illustrate parameter optimization using data collected from a stroke patient during the drinking task. As the patient performed the task, the clinician collected input accelerations (figure 4a shows one repetition) and indicated state transitions (and therefore desired stimulation) via the PC s keyboard. Using data from several segmented repetitions, the optimization algorithm searched all possible parameter combinations to find values that minimized the error function (figure 4b). Applying the triggering algorithm with optimized parameters to previously unseen data (figure 4c), the resulting state transitions and stimulation output corresponded closely to those the clini- 66 PERVASIVE computing

6 Acceleration (m/s 2 ) , 1,2 1,4 ime (no. of frames) Y 1 5 Lift Place (error =.17) Stimulation output Lift Place (a) , 1,2 1,4 ime (no. of frames) (b) X Acceleration (m/s 2 ) , 1,2 1,4 ime (no. of frames) Stimulation output (c) , 1,2 1,4 ime (no. of frames) Figure 4. Parameter optimization. (a) A hand-labeled input acceleration profile (X in blue, Y in red) for a stroke patient with desired stimulation output below. (b) Segmented data (Lift in blue, Place in red) for Lift to Place over 11 trials with a transition boundary determined by the optimization algorithm. he misclassification rate is 1.7 percent. (c) An automatically labeled, previously unseen input acceleration profile with corresponding stimulation output below. cian indicated (such that the algorithm would have assisted the patient at the correct times to complete the task). We also observed this outcome under realtime test conditions. Currently, stimulation systems require either that the system transmit the data to a remote expert for offline analysis and parameter selection 2 or that the medical engineer manually select parameters. 4 So, the CS presents a considerable contribution toward increasing FES systems usability (and potential clinical acceptance). Also, although three inputs are sufficient to generate correct stimulation patterns for this task, more complex motions could require more inputs (possibly from different input sources) to detect state transitions. User interface he CS s second major contribution is integrating automatic parameter optimization and stimulator programming in an easy to use interface to produce a more accessible system. Without the CS, a medical engineer must program the stimulator via an LCD screen and buttons integrated into the hardware. However, this process is time consuming and inaccurate, which could limit its clinical acceptance. In contrast, programming the stimulator using the CS comprises the following five steps, which a clinician can complete using the interface. FSM definition Using the CS, you create inputs and at- APRIL JUNE 28 PERVASIVE computing 67

7 Clinical Electronics tach them via a drop-down list to physical devices (such as accelerometers and stimulators) connected to the PC. So, if necessary, you could use multiple sensors (possibly with a mixture of sensing modalities such as goniometers and electromyography) in designing and testing a control algorithm. he FSM uses a familiar Explorerstyle tree (see figure 1) whose levels (from root to leaf) correspond to the FSM, its states, and their transitions. You add a state to the FSM via a button click and modify it using the corresponding property tab, where you can specify a name, a color (for display during data collection), and whether stimulation should be on during this state. When specifying a transition from a particular state, you simply pick the next state from a list. Similarly, you specify the inputs that can trigger a given state transition by picking them from a list of those available (the actual input used is selected from this subset during parameter optimization). his lets you restrict transitions to occur only using certain inputs (for example, using only effects a time-out). At this point, you ve defined only the state machine s structure; specific transition parameter values are determined automatically by the CS during parameter optimization. You can also export the state machine structure to a text file for later retrieval. In this way, you can reuse the same structure for different patients performing similar motions, because in many cases, only the parameter values need changing, not the FSM structure itself. Data collection Once you ve defined the state sequence, you collect and label data using simple button clicks ( start, trigger transition, and stop ). he subject performs the motion while data streams to the host PC (via USB) from the accelerometer. Simultaneously, you indicate when state transitions should occur using the keyboard or mouse, thus segmenting the collected data into several sets labeled according to their state. During data collection, the system displays the received, color-coded data on the screen in real time for visualization. For cyclic FSMs (that is, those with a transition from the final state to the initial state), the movement can repeat several times without stopping to make the parameter optimization robust to variations. When data collection ends, a second Explorer-style tree shows a hierarchical representation of the collected data, broken down by state transition at the first level and individual trials at the second level. From this view, you can flag outlier data (such as repetitions that were performed or labeled incorrectly) to remove them from the training set (that is, ignore them during parameter optimization). You can then save the collected data to a time-stamped file for later retrieval (that is, an audit trail). Not only does this let you retrospectively analyze the subject s rehabilitation over a period of time, it provides accountability in case someone asks why you used particular parameter values. Although the CS uses only the raw data to optimize thresholds, methods such as filtering and the computation of higher-order features can increase robustness and improve performance. However, this could reduce clinical interpretation of the parameter values and can add to the system s complexity, complicating its implementation on an embedded processor. Parameter optimization Having collected and labeled training data, you then execute parameter optimization via a button click. In a fraction of a second, the CS optimizes parameters and overlays thresholds on the collected data for you to inspect. his is a considerable improvement on the trial-and-error process that medical engineers currently use to determine parameter values under clinical conditions. System testing After determining parameter values that result in the desired stimulation, you can test the system online using the host PC. During online testing, accelerometer outputs stream to the PC in the same way as during data collection. he system displays incoming data in real time along with the corresponding transition boundary, tests the threshold conditions at each instant to detect state transitions, and triggers stimulation via USB at the appropriate times. Stimulator programming When satisfied that the optimized parameter values are appropriate, you upload them directly to the stimulator via USB at the click of a button. You can then disconnect the stimulator from the PC to operate in stand-alone mode as a truly portable device. Mobile application here s considerable potential for integrating the CS with portable computation platforms (such as a modern mobile phone) to make the technology truly ubiquitous. In practice, however, without the benefit of a complex operating system, the interface would be reduced to a simple menu-driven system. Because this would make FSM definition impractical on a mobile handset, you could instead transmit state machines (created using the Windowsbased CS) directly to the handset from a local PC (via Bluetooth) or download them from a remote repository (via 3G). Similarly, you could transmit software updates to the phone from a central clinic, while transmitting patient performance reports and data recorded 68 PERVASIVE computing

8 the Authors during reprogramming back to the clinic to inform further system development and to maintain patient records. Implementing the CS on a mobile platform would have several implications for its clinical acceptance. In particular, we could remove the integrated screen and buttons previously used to program the stimulator from the device (because the mobile phone has an integrated screen and keypad); this would reduce the device s complexity and size, making it more attractive to the patient and potentially increasing system uptake. Furthermore, the increased interaction between the patient and the device could also increase motivation when using FES treatment. Philip A. resadern is a research associate at the University of Liverpool and a visiting research fellow at the University of Salford. His research interests include machine learning, HCI, and computer vision. He received his DPhil in information engineering from the University of Oxford. Contact him at Crhpr, Brian Blatchford Bldg., Univ. of Salford, Salford M6 6PU, UK; p.tresadern@ salford.ac.uk. Sibylle B. hies is a research fellow with the Centre for Rehabilitation and Human Performance Research at the University of Salford ( ac.uk/crhpr). Her research interests include gait analysis, upper-limb biomechanics, neuropathy, accelerometry, and functional electrical stimulation for rehabilitation. She received her PhD in biomedical engineering from the University of Michigan. Contact her at Crhpr, Brian Blatchford Bldg., Univ. of Salford, Salford M6 6PU, UK; s.thies@salford.ac.uk. Laurence P.J. Kenney is a senior research fellow with the Centre for Rehabilitation and Human Performance Research at the University of Salford ( salford.ac.uk/crhpr). His research focuses on the development and evaluation of rehabilitation technologies, particularly functional electrical stimulation systems. He received his PhD in engineering design from the University of Salford. He s a member of the Institute of Physics and Engineering in Medicine. Contact him at Crhpr, Brian Blatchford Bldg., Univ. of Salford, Salford M6 6PU, UK; l.p.j. kenney@salford.ac.uk. Stimulation hardware, wearable sensors, and wireless telemetry are rapidly widening FES solutions scope. In particular, we can now tackle more complex problems (such as voluntary control of upperlimb reaching and grasping). However, emerging FES applications for rehabilitation require computational complexity that exceeds existing technology. Nonperiodic and unconstrained motions demand a way to specify highly individualized, flexible stimulation sequences, often with many control parameters. However, this additional complexity introduces new demands on the FES system designer to ensure that complex technical methods remain transparent to the clinician to maximize the technology s acceptance. Acknowledgments his work was supported by EU Framework VI contract IS ( Healthy AIMS ). We thank Salisbury District Hospital for providing the modified stimulator, Chris Smith and Julie Rigby for their invaluable help in recruiting patients and collecting data, and the stroke patients for their participation in the study. References David Howard is a professor in the University of Salford s School of Computing, Science and Engineering and the director of the university s Centre for Rehabilitation and Human Performance Research ( CRHPR). His research interests include biomedical engineering, specifically neuro-musculo-skeletal systems modeling, applying gait simulation to design assistive devices, controlling functional electrical stimulation, biomechanical modeling of the foot and lower limb, real-world monitoring of patients using assistive devices, and measuring post-stroke postural and balance deficits. He received his PhD at the University of Bath. Contact him at Crhpr, Brian Blatchford Bldg., Univ. of Salford, Salford M6 6PU, UK; d.howard@salford.ac.uk. John Y. Goulermas is a lecturer in the Department of Electrical Engineering and Electronics at the University of Liverpool. His research interests include pattern recognition, mathematical optimization, and machine vision, with applications in biomedical and clinical engineering and industrial monitoring. He received his PhD in image processing and machine vision from the Control Systems Centre at the University of Manchester Institute of Science and echnology. He s a member of the IEEE. Contact him at the Dept. of Electrical Eng. and Electronics, Univ. of Liverpool, Liverpool L69 3GJ, UK; j.y.goulermas@ liverpool.ac.uk. 1. L.R. Sheffler and J. Chae, Neuromuscular Electrical Stimulation in Neurorehabilitation, Muscle and Nerve, vol. 53, no. 5, 27, pp M.H. Granat et al., Clinical Rehabilitation using Electrical Stimulation via elematics (Crest), Proc. Int l Functional Electrical Stimulation Society, Ifess, 1997, pp R.. Lauer et al., Applications of Cortical Signals to Neuroprosthetic Control: A Critical Review, IEEE rans. Rehabilitation Eng., vol. 8, no. 2, 2, pp M.R. Popovic and. Keller, Modular ranscutaneous Functional Electrical Stimulation System, Medical Eng. & Physics, vol. 27, no. 1, 25, pp For more information on this or any other computing topic, please visit our Digital Library at APRIL JUNE 28 PERVASIVE computing 69