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Recognition of locomotion patterns based on BP neural network during different walking speeds

Pattern recognition based on myoelectric speed control is critical for neural-controlled powered lower limb prostheses. We preliminarily investigated the performance of surface electromyography signals used to identify movement modes with different speeds. The pattern recognition was tested on electromyography data collected from five muscles of sixteen able-bodied subjects. The BP neural network was used to identify level-ground walking modes with three different speeds: slow speed (0.5m/s), median speed (1m/s) and fast speed (1.5m/s). The results showed that the comprehensive recognition rate reached 90.48%, which would aid the future development of neural-controlled artificial legs and assist lower limb amputees with level ground walking.