Wearing transfemoral prosthesis is the only way to complete daily physical activity for amputees. Motion pattern recognition is important for the control of prosthesis, especially in the recognizing swing phase and stance phase. In this paper, it is reported that surface electromyography (sEMG) signal is used in swing and stance phase recognition. sEMG signal of related muscles was sampled by Infiniti of a Canadian company. The sEMG signal was then filtered by weighted filtering window and analyzed by height permitted window. The starting time of stance phase and swing phase is determined through analyzing special muscles. The sEMG signal of rectus femoris was used in stance phase recognition and sEMG signal of tibialis anterior is used in swing phase recognition. In a certain tolerating range, the double windows theory, including weighted filtering window and height permitted window, can reach a high accuracy rate. Through experiments, the real walking consciousness of the people was reflected by sEMG signal of related muscles. Using related muscles to recognize swing and stance phase is reachable. The theory used in this paper is useful for analyzing sEMG signal and actual prosthesis control.
Human motion control system has a high degree of nonlinear characteristics. Through quantitative evaluation of the nonlinear coupling strength between surface electromyogram (sEMG) signals, we can get the functional state of the muscles related to the movement, and then explore the mechanism of human motion control. In this paper, wavelet packet decomposition and n:m coherence analysis are combined to construct an intermuscular cross-frequency coupling analysis model based on wavelet packet-n:m coherence. In the elbow flexion and extension state with 30% maximum voluntary contraction force (MVC), sEMG signals of 20 healthy adults were collected. Firstly, the subband components were obtained based on wavelet packet decomposition, and then the n:m coherence of subband signals was calculated to analyze the coupling characteristics between muscles. The results show that the linear coupling strength (frequency ratio 1:1) of the cooperative and antagonistic pairs is higher than that of the nonlinear coupling (frequency ratio 1:2, 2:1 and 1:3, 3:1) under the elbow flexion motion of 30% MVC; the coupling strength decreases with the increase of frequency ratio for the intermuscular nonlinear coupling, and there is no significant difference between the frequency ratio n:m and m:n. The intermuscular coupling in beta and gamma bands is mainly reflected in the linear coupling (1:1), nonlinear coupling of low frequency ratio (1:2, 2:1) between synergetic pair and the linear coupling between antagonistic pairs. The results show that the wavelet packet-n:m coherence method can qualitatively describe the nonlinear coupling strength between muscles, which provides a theoretical reference for further revealing the mechanism of human motion control and the rehabilitation evaluation of patients with motor dysfunction.