Realtime EMG analysis for transcutaneous electrical stimulation assisted gait training in stroke patients*. Issue 32 (2016)

• Record Type: Journal Article
• Authors: Schauer, T.; Seel, T.; Bunt, N.D.; Müller, P.; Moreno, J.C.
• Is Part Of: IFAC-PapersOnLine. Volume 49:Issue 32(2016)
• Page Start: 183
• Page End: 187
• Publication Date: 2016
• Subjects: Signal Processing -- Biofeedback -- Optimization -- Gait -- Rehabilitation -- Functional Electrical Stimulation -- Filter Design; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2016.12.211
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This contribution describes a method for realtime analysis of muscle activity during application of Functional Electrical Stimulation (FES) to the assessed muscles. Inertial sensors at the foot are used for realtime gait phase detection in order to synchronize the stimulation with the gait. After detecting and muting stimulation artifacts and after extraction of Inputer-Pulse Intervals (IPIs), a non-causal high-pass filter is applied to a section of the IPI to extract the voluntary EMG activity. The filter suppresses FES-evoked EMG activity (M-wave) and electrode discharging artifacts. The initial filter states are chosen by an optimization procedure to minimize undesired filter transients. The obtained filtered EMG signal is then rectified and averaged to produce a scalar measure of the volitional EMG activity over the last IPI. The volitional EMG activity during four different detected gait phases is calculated after every completed step and displayed to the stroke patients for biofeedback or to the therapist in order to adjust the FES. The system has been initially evaluated with healthy subjects walking on a treadmill. It was demonstrated that different walking styles of an individual can be distinguished by the EMG analysis also during active FES support.