基于注意力模型的多传感器人类活动识别

doi:10.3969/j.issn.1000-1158.2019.06.04

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Abstract The deep recurrent neural network was suitable for processing time series data. However, the feature extraction ability of traditional recurrent neural network was poor, and the time dependency mining was insufficient. In response to the mentioned problem, three models of attention mechanism and long-term short-term memory (LSTM) neural network were proposed for human activity recognition application problems. The effects of these three mechanisms on the accuracy of the models were studied separately and in combination with different datasets. In the UCI_HAR data set, the accuracy rates of the three attention LSTM models were 94.13%,95.15% and 94.81%, respectively, which were higher than the identification accuracy rate of LSTM model (93.2%). In addition, for the label characteristics of sensor time series data for human activity recognition, it was proposed to convert the time segment classification task into a segmentation task. Therefore, two attention-based gate recurrent unit(GRU) models based on segmentation tasks were designed. The accuracy of Bahdanau attention GRU model were 84.61% and 89.54% in the Skoda data set and the opportunity data set which were higher than the benchmark UNet model’s 70.40% and 88.51%.

Key words： metrology human activity recognition long short time memory neural network attention mechanism;time segment classification segmentation task

Received: 07 January 2019 Published: 10 October 2019

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TB973

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	Articles by authors
	WANG Jin-jia
	ZHOU Ya-qian
	HAO Zhi

Cite this article:

WANG Jin-jia,ZHOU Ya-qian,HAO Zhi. Multi-sensor Human Activity Recognition Based on Attention Model[J]. Acta Metrologica Sinica, 2019, 40(6): 958-969.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.06.04 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I6/958

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