Head-Mounted Individual Monitoring and Finding with Heterogeneous Learning Enhancement Based on the Internet of Things

In this paper, we propose the head-mounted individual monitoring and finding (HIMF) framework to achieve proactive individual safety and assistance based on the Internet of Things (IoT). HIMF is the novel framework that provides the following features: i) it collects and analyzes inertial data to monitor individual behavior, detects incidents in emergency situations, and recognizes and corrects body postures as necessary in daily use, ii) heterogeneous learning attention is explored to develop the head-mounted activity recognition model to accurately identify individual status, and iii) emergent alerts and location tracking for the target individual in hazardous situations can be actively performed in a crowdsourced sensing manner. In particular, the Android-based prototype with a head-mounted IoT device for individual monitoring and finding is implemented to verify the feasibility and performance of HIMF. Experimental results show that HIMF outperforms existing methods and can efficiently provide individual status monitoring and crowdsourced target localization. The developed heterogeneous learning model achieves peak recognition accuracies of approximately 98.2% on the HARSense dataset, 94.5% on the KU-HAR dataset, and 89.2% on the UMAFall dataset. Compared to the worst-performing baseline (CNN-LSTM) and the strongest competitor (DDC3N), the developed model yields the maximum accuracy improvement of up to 14.3% and 1.5%, respectively.