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

• URL: http://arxiv.org/abs/2203.07910v1
• Date: Mon, 14 Mar 2022 07:57:32 GMT
• Title: Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition
• Authors: Yan Yan, Tianzheng Liao, Jinjin Zhao, Jiahong Wang, Liang Ma, Wei Lv, Jing Xiong, and Lei Wang
• Abstract summary: We devised a graph-inspired deep learning approach toward the sensor-based HAR tasks. We present a multi-layer residual structure involved graph convolutional neural network (ResGCNN) toward the sensor-based HAR tasks. Experimental results on the PAMAP2 and mHealth data sets demonstrate that our ResGCNN is effective at capturing the characteristics of actions.
• Score: 12.51766929898714
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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