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2022, Vol. 26 ›› Issue (12): 1805-1811Recognition of human lower limb movement intention based on surface electromyography of lower limb corresponding to five gaitsZhao Yiming, Wang Jie, Zhang Gaowei, Sun Jianjun, Yang Peng
Abstract: BACKGROUND: The lower limb exoskeleton robot is used to recover the basic walking ability of patients. To realize the high coordination between humans and robots and to design robot control strategies, it is necessary to recognize the movement intention of the lower limb which includes gait. OBJECTIVE: To propose a new classifier to recognize gait using surface electromyography. METHODS: Firstly, surface electromyography data of lower limbs in five gait patterns were collected. Secondly, the collected signals were denoised and the features were extracted to obtain the data sets of five gait patterns. After that, two of them were combined and 10 groups of data sets were built. Thirdly, the probabilistic neural networks were trained with the AdaBoost algorithm based on each group of data sets. Fourthly, ten strong classifiers were obtained by ensemble of trained probabilistic neural networks. Finally, the testing samples were input into 10 strong classifiers and the categories were judged by voting. RESULTS AND CONCLUSION: An average recognition rate of 90.2% was achieved in the experiment by improved AdaBoost algorithm to recognize the five gait patterns. Compared with separate probabilistic neural network, the average recognition rate of separate probabilistic neural network was 68.2%, which was lower than that of improved AdaBoost algorithm. When the weak classifier was replaced by support vector machine, BP neural network or decision tree was used to construct strong classifiers for comparison. When a weak classifier was utilized, the average recognition rate of separate support vector machine was higher than that of separate probabilistic neural network. However, but the average recognition rate of strong classifier based on probabilistic neural network was higher than that 84.3% based on support vector machine with improved AdaBoost algorithm. Key words: gait recognition, surface electromyogram, butterworth filter, adaptive filtering, feature extraction, probabilistic neural network, AdaBoost algorithm, multi-classification problem |