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Adaptive Maximization of the Harvested Power for Wearable or Implantable Sensors with Coulomb Force Parametric Generators

Published

Author(s)

Masoud Roudneshin, Kamran Sayrafian, Amir Aghdam

Abstract

Miniaturized wearable or implantable medical sensors (or actuators) are often used in the Internet of Things (IoT) technologies in healthcare applications. However, their limited source of power is becoming a bottleneck for the pervasive use of these devices, especially, as their functionality increases. Kinetic-based micro-energy harvesters can generate power through the natural human body motion. Therefore, they can be an attractive solution to supplement the source of power in medical wearables or implants. The architecture based on the Coulomb force parametric generator (CFPG) is the most viable micro-harvester solution for generating power from human motion. This article proposes three methods: a linear estimation approach, a multi-armed bandit algorithm, and a min–max-based approach to adaptively estimate the desirable electrostatic force in a CFPG using the input acceleration waveform. Through extensive simulations, the performance of the proposed methods in maximizing the output power of the micro-harvester is evaluated.
Citation
IEEE Internet of Things Journal
Volume
10
Issue
19

Keywords

Internet of Things in Healthcare, Micro energy harvesting, Coulomb force parametric generator, Low power wearable sensors, Online optimization

Citation

Roudneshin, M. , Sayrafian, K. and Aghdam, A. (2023), Adaptive Maximization of the Harvested Power for Wearable or Implantable Sensors with Coulomb Force Parametric Generators, IEEE Internet of Things Journal, [online], https://doi.org/10.1109/JIOT.2023.3269953, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936349 (Accessed April 19, 2024)
Created April 25, 2023, Updated March 28, 2024