On Pattern Classification of EMG Signals for Walking Motions

On Pattern Classification of EMG Signals for Walking Motions

  In this paper, we present a method to classify electromyogram (EMG) signals which are utilized to control signals for 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 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.