Gait Event Detection Using Intramuscular Electromyography to Trigger Functional Electrical Stimulation in the Child with Cerebral Palsy

Under investigation is whether intramuscular (IM) electromyography can be used to trigger functional electrical stimulation (FES) to improve ambulation for the child with cerebral palsy (CP). In this study, bifilar percutaneous intramuscular (IM) recording electrodes were implanted bilaterally into a quadriceps muscle of a child with CP. Following implantation, 5 walking trials consisting of 2-4 steps were conducted during which gait events and IM EMG data were collected simultaneously. Based on the data from the first trial, a fuzzy inference system (FIS) was developed to determine 5 gait events for one leg from the 2 IM EMG signals and their derivatives. The FIS was evaluated using the remaining 4 trials. For 4 of 5 gait events detected, the average error between the actual and predicted events was 3% of the gait cycle or less suggesting that it may be feasible to accurately detect gait events using IM EMG signals. Introduction/Background In order to coordinate the stimulation of specific weak or paralyzed lower extremity muscles to provide or improve upright mobility, a reliable, accurate and clinically reasonable method of detecting critical gait events is required. Thus gait event detection has received considerable attention using various signal sources including foot switches [1], tilt sensors [2], accelerometers [3], and sensory nerve activity [4] among others, employing various state control techniques. Gait event detection using electromyography (EMG) signals from voluntarily controlled muscles has received less attention presumably since FES research has focussed on mobility for individuals with complete thoracic spinal injuries for whom lower limb control is completely absent. Graupe [5] used surface EMG signals from upper trunk muscles to predict intended lower extremity movements for reciprocal walking or postural corrections for static standing. At our institution we are investigating functional electrical stimulation (FES) as a means of improving walking ability for the child with cerebral palsy (CP) [6]. For this population, voluntary lower extremity muscle activity is present so that EMG triggering of stimulation during gait is conceivable. The EMG signal is attractive for gait event detection for several reasons. The lower extremity muscle activity occurs in a repeatable way with respect to the gait cycle. The target population has numerous potential sources of EMG activity. The technology exists to use completely implanted recording electrodes with an implanted stimulator [7] so that the sensor system itself could be easily implanted in the future avoiding sensor maintenance and donning. Finally, if unique EMG signatures can be extracted for other walking conditions such as on non-level surfaces, steps or ramps then EMG control could be extended to these situations as well. The goal of this study was to determine the feasibility of using electromyographic (EMG) signals from voluntarily controlled lower extremity muscles to detect gait events (transitions between gait phases) using a fuzzy inference system (FIS). The long-term goal is to eventually trigger stimulation of ill-timed or weak lower extremity muscles in the child with CP based on a real time EMG-based FIS system.