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A multi-graph convolutional network based wearable human activity recognition method using multi-sensors

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Abstract

Wearable human activity recognition (WHAR) using multi-sensors is a promising research area in ubiquitous and wearable computing. Existing WHAR methods usually interact features learned from multi-sensor data by using convolutional neural networks or fully connected networks, which may ignore the prior relationships among multi-sensors. In this paper, we propose a novel method, called MG-WHAR, which employs graphs to model the relationships among multi-sensors. Specifically, we construct three types of graphs: a body structure based graph, a sensor modality based graph, and a data pattern based graph. In each graph, the nodes represent sensors, and the edges are set according to the relationships among sensors. MG-WHAR, utilizing a multi-graph convolutional network, conducts feature interactions by leveraging the relationships among multi-sensors. This strategy not only enhances model performance but also results in a model with fewer parameters. Compared to the state-of-the-art WHAR methods, our method increases weighted F1-score by 3.2% on Opportunity dataset, 1.9% on Realdisp dataset, and 2.6% on DSADS dataset, while maintaining lower computational complexity.

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Notes

  1. https://archive.ics.uci.edu/ml/datasets/opportunity+activity+recogni-tion

  2. https://archive.ics.uci.edu/ml/datasets/REALDISP+Activity+Recog-nition+Dataset

  3. https://archive.ics.uci.edu/ml/datasets/daily+and+sports+activities

  4. https://github.com/LuoYingSong/MG-WHAR

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China

    Ling Chen, Liangying Peng, Rong Hu, Yi Zhang & Shenghuan Miao

  2. Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies, Hangzhou, 310027, China

    Ling Chen, Yingsong Luo, Liangying Peng, Rong Hu, Yi Zhang & Shenghuan Miao

  3. School of Software Technology, Zhejiang University, Hangzhou, 310027, China

    Yingsong Luo

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  1. Ling Chen
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Correspondence to Ling Chen.

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Chen, L., Luo, Y., Peng, L. et al. A multi-graph convolutional network based wearable human activity recognition method using multi-sensors. Appl Intell 53, 28169–28185 (2023). https://doi.org/10.1007/s10489-023-04997-4

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