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Human Activity Recognition Based on Residual Network and BiLSTM

by 1 and 2,*
1
School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510006, China
2
School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510006, China
*
Author to whom correspondence should be addressed.
Academic Editor: Qammer Hussain Abbasi
Sensors 2022, 22(2), 635; https://doi.org/10.3390/s22020635
Received: 8 November 2021 / Revised: 19 December 2021 / Accepted: 12 January 2022 / Published: 14 January 2022
(This article belongs to the Special Issue Sensing Human Movement through Wearables)
Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters. View Full-Text
Keywords: human activity recognition; residual network; BiLSTM; inertial measurement unit human activity recognition; residual network; BiLSTM; inertial measurement unit
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MDPI and ACS Style

Li, Y.; Wang, L. Human Activity Recognition Based on Residual Network and BiLSTM. Sensors 2022, 22, 635. https://doi.org/10.3390/s22020635

AMA Style

Li Y, Wang L. Human Activity Recognition Based on Residual Network and BiLSTM. Sensors. 2022; 22(2):635. https://doi.org/10.3390/s22020635

Chicago/Turabian Style

Li, Yong, and Luping Wang. 2022. "Human Activity Recognition Based on Residual Network and BiLSTM" Sensors 22, no. 2: 635. https://doi.org/10.3390/s22020635

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