Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring Systems

• URL: http://arxiv.org/abs/2401.10794v1
• Date: Fri, 19 Jan 2024 16:26:35 GMT
• Title: Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring Systems
• Authors: Ziqiaing Ye, Yulan Gao, Yue Xiao, Zehui Xiong and Dusit Niyato
• Abstract summary: Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data. We propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency.
• Score: 69.41229290253605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In smart healthcare, health monitoring utilizes diverse tools and technologies to analyze patients' real-time biosignal data, enabling immediate actions and interventions. Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently, tailored to their designated functional scope. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data due to monitoring irrelevant health metrics. In this context, we propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency, a novel framework based on Deep Reinforcement Learning (DRL) and SlowFast Model to ensure precise monitoring based on users' activities. Specifically, with the SlowFast Model, DActAHM efficiently identifies individual activities and captures these results for enhanced processing. Subsequently, DActAHM refines health metric monitoring in response to the identified activity by incorporating a DRL framework. Extensive experiments comparing DActAHM against three state-of-the-art approaches demonstrate it achieves 27.3% higher gain than the best-performing baseline that fixes monitoring actions over timeline.

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