Lower limb motion pattern recognition based on IWOA-SVM

This paper presents a lower limb motion pattern recognition algorithm based on SVM model optimized by improved whale algorithm (IWOA-SVM). The purpose is to overcome the problems of low accuracy and low reaction speed of existing lower limb rehabilitation pattern recognition algorithms, nonlinear adjustment factor is combined with WOA to enhance the search ability and search speed of the algorithm. The collected surface EMG signals are used as the input of the motion pattern recognition system, and the motion pattern recognition is realized by combining the IWOA-SVM model. Simulation experiments on six test functions are carried out and compared with WOA. The results indicated that the search convergence speed and optimization accuracy of IWOA are improved. Experiments on the collected signal data suggested that the recognition accuracy is 94.12%, compared with WOA-SVM algorithm, PSO-SVM and GA-SVM. The results indicated that the recognition accuracy of this algorithm is improved by 4.02%, 8.82% and 6.64% respectively.