Abstract
We present a method to calssify electromyogram (EMG) signals which are utilized as control signals for a patient-responsive walker-supported system for paraplegics. Patterns of EMG signals for different walking motions are classified via adequate filtering, real EMG signal extraction, AR-modeling, and a modified self-organizing feature map (MSOFM). In particular, a data-reducing extraction algorithm is employed for real EMG signals. Moreover, MSOFM classifies and determines the results automatically using a fixed map. Finally, the experimental results are presented for validation.
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Choi, HL., Byun, HJ., Song, WG. et al. On pattern classification of EMG signals for walking motions. Artif Life Robotics 4, 193–197 (2000). https://doi.org/10.1007/BF02481174
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DOI: https://doi.org/10.1007/BF02481174