A Study on Pedaling Skill Level Classification Using Machine Learning

Abstract

Pedaling skills are one of the important factors to bring out one’s best performance in pedaling exercise, and refer to the ability to efficiently convert bicycle pedal effort into a driving force. It has been reported that pedaling skills are strongly connected with the muscle synergy of lower limbs. However, a quantitative evaluation method for this connection has not been established yet. The objective of this study is to establish a quantitative evaluation method for pedaling skills. In this study, we proposed a method to derive muscle synergy by measuring and processing the signals of surface electromyography of the lower limb during pedaling exercise, where both the temporal structure and the space structure of the muscle synergy can be visualized. In this paper, we apply a machine learning method to the visualized muscle synergy data in order to estimate the pedaling skill level of a test subject.