Motion recognition based on EMG signals of residual limb in transfemoral amputee

Objective: To study the method of lower limb movement pattern recognition using the electromyographic (EMG) signals of the residual thigh muscles, and explore the possibility of lower limb prosthesis control based on the EMG signals. Methods: Fifteen transfemoral amputees were selected as subjects, and the subjects were required to complete 5 kinds of motion, including level walking, stair ascent, stair descent, standing up and sitting down. The surface EMG signals from 6 muscles of the thigh stump were collected from each subject, including rectus femoris, vastus lateralis, tensor fascia lata, biceps femoris, semitendinosus and gluteus maximus. Six kinds of time-domain and frequency domain features of the EMG signals were extracted, and 5 kinds of motion patterns were recognized by the support vector machine. Results: Five kinds of motion patterns could be recognized online by EMG signals of the residual thigh muscles. By single experimental data from one subject, the recognition rate was 94%; for the same subject, by the data mixed from two experiments, the recognition rate was 85%; for different subjects, the recognition rate was 74%. By feature optimization, using only two EMG features of 3 muscles, the recognition rate could reach 92% by single experimental data from one subject. For 3 kinds of motion patterns (level walking, stair ascent, stair descent), the recognition rate respectively was 100% using single experimental data from one subject, 98.33% using the data mixed from two experiments for the same subject, and 93.33% using the data from different subjects. Conclusions: Simply using the thigh stump EMG signals to recognize movement intention is proved to be feasible. For each patient, by several times of training before using the EMG signals, the recognition rate is expected to reach an ideal state. The present work will lay a foundation for lower limb prosthesis control based on the EMG signals.