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

Jie He, Farong Gao (), Jian Wang, Qiuxuan Wu, Qizhong Zhang and Weijie Lin
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Jie He: HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China
Farong Gao: HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China
Jian Wang: HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China
Qiuxuan Wu: HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China
Qizhong Zhang: HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China
Weijie Lin: HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China

Mathematics, 2022, vol. 10, issue 22, 1-20

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.

Keywords: gait recognition; surface electromyography; feature fusion; deep belief network; sparrow search algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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