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

Yamin, Nuzhat; Bhat, Ganapati; Doppa, Janardhan Rao

doi:10.23919/date54114.2022.9774553

Cited by 7 publications

(1 citation statement)

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“…a day) is equal to the energy harvested in that period. Recent work has also used dynamic programming and control-theoretic methods to achieve energy neutral operation (Vigorito et al 2007;Bhat et al 2017;Geissdoerfer et al 2019;Yamin, Bhat, and Doppa 2022).…”

Section: Related Workmentioning

confidence: 99%

Adaptive Energy Management for Self-Sustainable Wearables in Mobile Health

Hussein

Bhat

Doppa

2022

AAAI

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Wearable devices that integrate multiple sensors, processors, and communication technologies have the potential to transform mobile health for remote monitoring of health parameters. However, the small form factor of the wearable devices limits the battery size and operating lifetime. As a result, the devices require frequent recharging, which has limited their widespread adoption. Energy harvesting has emerged as an effective method towards sustainable operation of wearable devices. Unfortunately, energy harvesting alone is not sufficient to fulfill the energy requirements of wearable devices. This paper studies the novel problem of adaptive energy management towards the goal of self-sustainable wearables by using harvested energy to supplement the battery energy and to reduce manual recharging by users. To solve this problem, we propose a principled algorithm referred as AdaEM. There are two key ideas behind AdaEM. First, it uses machine learning (ML) methods to learn predictive models of user activity and energy usage patterns. These models allow us to estimate the potential of energy harvesting in a day as a function of the user activities. Second, it reasons about the uncertainty in predictions and estimations from the ML models to optimize the energy management decisions using a dynamic robust optimization (DyRO) formulation. We propose a light-weight solution for DyRO to meet the practical needs of deployment. We validate the AdaEM approach on a wearable device prototype consisting of solar and motion energy harvesting using real-world data of user activities. Experiments show that AdaEM achieves solutions that are within 5% of the optimal with less than 0.005% execution time and energy overhead.

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Section: Related Workmentioning

confidence: 99%