Real-time implementation of an intent recognition system for artificial legs

This paper presents a real-time implementation of an intent recognition system on one transfemoral (TF) amputee. Surface Electromyographic (EMG) signals recorded from residual thigh muscles and the ground reaction forces/moments collected from the prosthetic pylon were fused to identify three locomotion modes (level-ground walking, stair ascent, and stair descent) and tasks such as sitting and standing. The designed system based on neuromuscular-mechanical fusion can accurately identify the performing tasks and predict intended task transitions of the patient with a TF amputation in real-time. The overall recognition accuracy in static states (i.e. the states when subjects continuously performed the same task) was 98.36%. All task transitions were correctly recognized 80–323 ms before the defined critical timing for safe switch of prosthesis control mode. These promising results indicate the potential of designed intent recognition system for neural control of computerized, powered prosthetic legs.