Smart Power Management for Energy-Autonomous Wearable Health Monitors

Abstract

The rise of wearable health monitors (WHMs) has opened new frontiers in personalized healthcare, enabling continuous monitoring of vital signs outside clinical settings. However, the energy demands of these devices present a significant challenge, particularly in achieving long-term, autonomous operation without frequent battery recharge. This thesis explores the potential of Thermoelectric Generators (TEGs) combined with smart power management strategies, particularly Reinforcement Learning (RL), to address this challenge. The study involved the design and simulation of a TEG-based energy harvesting system integrated with a boost converter, supercapacitor storage, and resistive loads using the MATLAB Simulink toolbox. Two TEG modules were utilized in the design, and they were connected in such a way as to switch between a parallel and a series configuration. The system's performance was evaluated under various configurations and ambient conditions, with a focus on maintaining energy-neutral operation (ENO). Simulation results indicated that the parallel configuration of TEG modules provided better performance at lower ambient temperatures, while the series configuration was more efficient at higher temperatures. The RL problem was set as a Markovian Decision Process (MDP) consisting of states, action and reward space. A neural framework consisting of six sensory inputs, eight hidden layer and four possible actions was designed for the RL solution. The sensory inputs represented harvested energy from the modelled TEG and the available charge in the supercapacitor storage. A hypothetical analysis of the RL agent's potential behaviour suggested that it would dynamically adjust the system's load states and configuration to optimize energy efficiency based on real-time sensory inputs. Although the RL algorithm has not yet been fully implemented, these findings lay the groundwork for future work aimed at creating a fully autonomous and intelligent power management system for wearable health monitors. This study contributes to the development of sustainable wearable technologies, offering a promising solution for energy-autonomous WHMs capable of achieving energy neutral operation in diverse environmental conditions.