Details
| Original language | English |
|---|---|
| Pages (from-to) | 183-187 |
| Number of pages | 5 |
| Journal | IFAC-PapersOnLine |
| Volume | 49 |
| Issue number | 32 |
| Publication status | Published - 2016 |
| Externally published | Yes |
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.
Keywords
- Biofeedback, Filter Design, Functional Electrical Stimulation, Gait, Optimization, Rehabilitation, Signal Processing
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
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In: IFAC-PapersOnLine, Vol. 49, No. 32, 2016, p. 183-187.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Realtime EMG analysis for transcutaneous electrical stimulation assisted gait training in stroke patients
AU - Schauer, T.
AU - Seel, T.
AU - Bunt, N. D.
AU - Müller, P.
AU - Moreno, J. C.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Biofeedback
KW - Filter Design
KW - Functional Electrical Stimulation
KW - Gait
KW - Optimization
KW - Rehabilitation
KW - Signal Processing
UR - http://www.scopus.com/inward/record.url?scp=85009827432&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2016.12.211
DO - 10.1016/j.ifacol.2016.12.211
M3 - Article
AN - SCOPUS:85009827432
VL - 49
SP - 183
EP - 187
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 32
ER -