Building A Prototype of a Pair of Weighing Shoes Based on Machine Learning Techniques

Abstract

Changes in the human body weight may be caused by diseases, overeating, lack of exercise, or other health problems. In this paper, we built a prototype of a pair of weighing shoes to enable people to constantly monitor their weight changes so that they can detect and treat their health problems earlier. We placed several thin and flexible pressure sensors under the shoes and used machine learning techniques to learn the relationship between their values and the actual weight of the human body being measured. The trained model then was used to predict the weight of a human body. In our experiments, we varied the number of used pressure sensors to study its impact on the prediction error. We found that the prediction error of the trained model could be controlled within 5.8% when 8 or more pressure sensors were used for the current prototype.