Domain Adaptation for Sensor-Based Human Activity Recognition with a Graph Convolutional Network

Jing Yang, Tianzheng Liao, Jingjing Zhao, Yan Yan, Yichun Huang, Zhijia Zhao, Jing Xiong () and Changhong Liu ()
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Jing Yang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Tianzheng Liao: Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 519041, China
Jingjing Zhao: School of Biological Science and Medical Engineering, Southeast University, Nanjing 211189, China
Yan Yan: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Yichun Huang: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528010, China
Zhijia Zhao: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Jing Xiong: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Changhong Liu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

Mathematics, 2024, vol. 12, issue 4, 1-20

Abstract: Sensor-based human activity recognition (HAR) plays a fundamental role in various mobile application scenarios, but the model performance of HAR heavily relies on the richness of the dataset and the completeness of data annotation. To address the shortage of comprehensive activity types in collected datasets, we adopt the domain adaptation technique with a graph neural network-based approach by incorporating an adaptive learning mechanism to enhance the action recognition model’s generalization ability, especially when faced with limited sample sizes. To evaluate the effectiveness of our proposed approach, we conducted experiments using three well-known datasets: MHealth, PAMAP2, and TNDA. The experimental results demonstrate the efficacy of our approach in sensor-based HAR tasks, achieving impressive average accuracies of 98.88%, 98.58%, and 97.78% based on the respective datasets. Furthermore, we conducted transfer learning experiments to address the domain adaptation problem. These experiments revealed that our proposed model exhibits exceptional transferability and distinguishing ability, even in scenarios with limited available samples. Thus, our approach offers a practical and viable solution for sensor-based HAR tasks.

Keywords: sensor-based human activity recognition (HAR); graph convolutional network; domain adaptation; transfer learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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