Gait Recognition by Combining Recurrent Neural Network and Fully Convolutional Network

Gait recognition is one of the key technologies for exoskeleton robot control. The existing gait recognition methods cannot meet the needs of real-time control well in terms of recognition accuracy and robustness. In this paper, a gait recognition method based on the recurrent neural network and fully convolutional network (RNN-FCN) algorithm is proposed. In this paper, a human lower limb gait information acquisition device is developed, ten types of human lower limb gait data are collected, and a human gait recognition model is constructed using the RNN-FCN algorithm. The experimental results demonstrate that the RNN-FCN algorithm achieves an average recognition classification accuracy of 94.43% in the experiments, outperforming the other four algorithms.