Gait Motor Function Evaluation Based on Muscle Synergy Method Considering the complexity and functionality of multimuscle synergies, involved in the human lower limb gait movement, a gait function evaluation method, based on synergy structure, is proposed. Firstly, the surface electromyography (sEMG) from selected muscles are collected and pretreated, to extract its envelope. Next, it is decoupled by the algorithm of nonnegative matrix decomposition, so that the synergy elements of the gait action can be extracted and the corresponding activation coefficients calculated, while subsequently these data are converted into normalized form. Then, the energy distribution and complexity of the synergy motion are analyzed. Finally, the function of gait motion is mapped, according to synergy structure, to determine whether the gait phases are normal or not. This study is helpful for quantitative analysis of lower limb gait and evaluation of motor function, in the framework of rehabilitation therapy.

Human Body Mixed Motion Pattern Recognition Method Based on MultiSource Feature Parameter Fusion Aiming at the requirement of rapid recognition of the wearerxe2x80x99s gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multisource feature parameters. We obtain information on human lower extremity acceleration and plantar analyze the relationship between these parameters and gait cycle studying the motion state recognition method based on feature evaluation and neural network. Based on the actual requirements of exoskeleton per use, 15 common gait patterns were determined. Using this, the studies were carried out on the time domain, frequency domain, and energy feature extraction of multisource lower extremity motion information. The distancebased feature screening method was used to extract the optimal features. Finally, based on the multilayer BP (back propagation) neural network, a nonlinear mapping model between feature quantity and motion state was established. The experimental results showed that the recognition accuracy in single motion mode can reach up to 98.28%, while the recognition accuracy of the two groups of experiments in mixed motion mode was found to be 92.7% and 97.4%, respectively. The feasibility and effectiveness of the model were verified.