In-Depth Recognition of Human Motion States Based on Smart Phone Perception

doi:10.13190/j.jbupt.2018-221

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2019, Vol. 42 ›› Issue (3): 43-50.doi: 10.13190/j.jbupt.2018-221

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In-Depth Recognition of Human Motion States Based on Smart Phone Perception

YIN Xiao-ling^1,2, XIA Qi-shou^1,2, CHEN Xiao-jiang¹, HE Juan¹, CHEN Feng¹

1. School of Information Science and Technology, Northwest University, Xi'an 710127, China;
2. College of Mathematics and Computer Science, Chizhou University, Chizhou 247000, China

Received:2018-09-09 Online:2019-06-28 Published:2019-06-20

Abstract

Abstract: In order to improve the accuracy of recognition of human motion states by smart phones, an in-depth recognition method based on parallel convolution neural network (PCNN) is proposed. Firstly, the sensor data input format is standardized by using 3D data matrix. Secondly, two PCNNs are used to carry out convolution and pool operation to the acceleration sensor and gyroscope data of human body motion respectively, realizing partial weight sharing. Finally, the two PCNNs are merged in the full-connected layer, and the softmax function is used to classify the human motion states. Experiments show that this method can extract the deep features of human motion states from the original data of the sensor, which can improve the recognition rate of the motion state by comparing with the traditional machine learning method.

Key words: motion states, in-depth recognition, smart phones, parallel convolution neural network

CLC Number:

YIN Xiao-ling, XIA Qi-shou, CHEN Xiao-jiang, HE Juan, CHEN Feng. In-Depth Recognition of Human Motion States Based on Smart Phone Perception[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(3): 43-50.

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In-Depth Recognition of Human Motion States Based on Smart Phone Perception

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