AI

Spatiotemporal Assignment of Energy Harvesters on a Self-Sustaining Medical Shoe

Abstract

We present a new method for spatiotemporal assignment and scheduling of energy harvesters on a medical shoe tasked with measuring gait diagnostics. While prior work exists on the application of dielectric elastomers (DEs) for energy scavenging on shoes, current literature does not address the issues of placement and timing of these harvesters, nor does it address integration into existing sensing systems. We solve these issues and present a self-sustaining medical shoe that harvests energy from human ambulation while simultaneously measuring gait characteristics most relevant to medical diagnosis.