A Method Combining Multi-Feature Fusion and Optimized Deep Belief Network for EMG-Based Human Gait Classification

Abstract

In this paper, a gait classification method based on the deep belief network (DBN) optimized by the sparrow search algorithm (SSA) is proposed. The multiple features obtained based on surface electromyography (sEMG) are fused. These functions are used to train the model. First, the sample features, such as the time domain and frequency domain features of the denoised sEMG are extracted and then the fused features are obtained by feature combination. Second, the SSA is utilized to optimize the architecture of DBN and its weight parameters. Finally, the optimized DBN classifier is trained and used for gait recognition. The classification results are obtained by varying different factors and the recognition rate is compared with the previous classification algorithms. The results show that the recognition rate of SSA-DBN is higher than other classifiers, and the recognition accuracy is improved by about 2% compared with the unoptimized DBN. This indicates that for the application in gait recognition, SSA can optimize the network performance of DBN, thus improving the classification accuracy.