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

Abstract

Miniaturized wearable or implantable medical sensors (or actuators) are important components of the Internet of Things (IoT) in healthcare applications. However, their limited source of power is becoming a bottleneck for pervasive use of these devices, specially, 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 the human motion. This paper proposes three methods, namely, linear estimation approach, multi-armed bandit, 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.