Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health
  Monitoring Systems
Ziqiaing Ye Yulan Gao Yue Xiao Zehui Xiong Dusit Niyato
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.