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arXiv · 2203.07910

Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition

Yan Yan·Tianzheng Liao·Jinjin Zhao·Jiahong Wang·Liang Ma·Wei Lv·Jing Xiong·Lei Wang
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Abstract

The sensor-based human activity recognition (HAR) in mobile application scenarios is often confronted with sensor modalities variation and annotated data deficiency. Given this observation, we devised a graph-inspired deep learning approach toward the sensor-based HAR tasks, which was further used to build a deep transfer learning model toward giving a tentative solution for these two challenging problems. Specifically, we present a multi-layer residual structure involved graph convolutional neural network (ResGCNN) toward the sensor-based HAR tasks, namely the HAR-ResGCNN approach. Experimental results on the PAMAP2 and mHealth data sets demonstrate that our ResGCNN is effective at capturing the characteristics of actions with comparable results compared to other sensor-based HAR models (with an average accuracy of 98.18% and 99.07%, respectively). More importantly, the deep transfer learning experiments using the ResGCNN model show excellent transferability and few-shot learning performance. The graph-based framework shows good meta-learning ability and is supposed to be a promising solution in sensor-based HAR tasks.

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Yan Yan, Tianzheng Liao, Jinjin Zhao, Jiahong Wang, Liang Ma, Wei Lv, Jing Xiong, Lei Wang. 2022-03-14. Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition. https://doi.org/10.1109/bibm55620.2022.9995660

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