Energy Harvesting Modeling and Prediction during Walking Gait for a Sliding Shoe

Abstract

Harvesting energy during walking is a promising potential power source to decrease size or even eliminate batteries for wearable devices. While sliding shoes may offer a method for harvesting energy during gait, it is important to know their influence on the expected energy harvesting rate and metabolic cost rate. In this paper, we develop two multivariate linear regression models based on subject height, weight, and walking speed to predict energy harvesting rate and metabolic cost rate for walking with custom energy harvesting sliding shoes. Eight healthy subjects performed 200 meter overground walking trials at normal and fast speeds while wearing the custom sliding shoes to harvest energy and a portable gas analysis system to measure metabolic cost. The metabolic cost rate model performed well with only 6.9% error, while the energy harvesting rate model was less accurate with 29.9% error. Future research should focus on improving the models by adding additional features such as step frequency, speed of sliding and length of sliding to capture more of the variance. These findings could help to serve as a foundation to facilitate widespread adoption of wearable devices by reducing the required amount of onboard energy storage.