On Pattern Classification of EMG Signals for Walking Motions
On Pattern Classification of EMG Signals for Walking Motions
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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.
Abstract<BR>1. INTRODUCTION<BR>2. EMG SIGNAL PROCESSING<BR>3. EXPERIMENT<BR>4. CONCLUSIONS<BR>5. REFERENCES<BR>
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