Inertial Sensor Binding Position and Combination in Badminton Action Recognition Using Machine Learning

During the coaching of both novice and seasoned badminton players, a comprehensive analysis of their physical training regimens has gained paramount importance. To achieve the objective of recognizing badminton actions based on inertial sensors, we developed a data acquisition platform that focuses specifically on badminton actions. This paper primarily employs three recognition algorithms, namely K-nearest neighbors (KNN), decision tree C4.5, and support vector machine (SVM), to classify various badminton actions. We have conducted multiple experiments to scrutinize and comprehend the influence of recognition outcomes under different sensor quantities and diverse sensor placement positions.