DIET: A Dynamic Energy Management Approach for Wearable Health Monitoring Devices

Abstract

Wearable devices are becoming increasingly popular for health and activity monitoring applications. These devices typically include small rechargeable batteries to improve user comfort. However, the small battery capacity leads to limited operating life, requiring frequent recharging. Recent research has proposed energy harvesting using light and user motion to improve the lifetime of wearable devices. Most energy harvesting approaches assume that the placement of the energy harvesting device and sensors required for health monitoring are the same. However, this assumption does not hold for several real-world applications. For example, motion energy harvesting using piezoelectric sensors is limited to the knees and elbows, while a sensor for heart rate monitoring must be placed on the chest for optimal performance. To address this challenge, we propose a novel dynamic energy management approach referred to as DIET for wearable health applications enabled by multiple sensors and energy harvesting devices. The key idea behind DIET is to harvest energy from multiple sources and optimally allocate it to each sensor using a lightweight optimization algorithm such that the overall utility for applications is maximized. Experiments on real-world data from four users over 30 days show that the DIET approach achieves utility within 10% of an offline Oracle.