Good lower limb gait perception performance can help improving assistance effectiveness of exoskeleton robots.Taking plantar pressure distribution as research object,a wearable plantar pressure distribution collection device is built based on plantar biomechanical analysis,plantar pressure data of three different gaits,namely walking on flat ground,slow jogging on flat ground,and walking on slopes,are collected,respectively.Overall plantar pressures are obtained by constructing a ground reaction force prediction model based on multiple linear regression method.A method for lower limb gait perception based on the overall plantar pressure and convolutional neural network(CNN)classification algorithm is proposed.Comparative analysis are conducted on support vector machine(SVM)and back propagation(BP)neural network.The experimental results show that the proposed method achieves an average recognition rate of 98.3% on the three gaits,and has higher accuracy.The feasibility and effectiveness of using the CNN classification algorithm to identify different lower limb gaits are verified.
维普
万方数据