Smart Boxing Glove “RD α”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine Learning

• Published in: Applied sciences 2023-08, Vol.13 (16), p.9073
• Main Authors: Cizmic, Dea, Hoelbling, Dominik, Baranyi, René, Breiteneder, Roland, Grechenig, Thomas
• Format: Article
• Language: English
• Subjects: Boxing; Classification; Combat sports; Gloves; Kickboxing; Machine learning; Martial arts; Sensors; smart gear; Software; time-series classification; Velocity
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13169073

Emerging smart devices have gathered increasing popularity within the sports community, presenting a promising avenue for enhancing athletic performance. Among these, the Rise Dynamics Alpha (RD α) smart gloves exemplify a system designed to quantify boxing techniques. The objective of this study is to expand upon the existing RD α system by integrating machine-learning models for striking technique and target object classification, subsequently validating the outcomes through empirical analysis. For the implementation, a data-acquisition experiment is conducted based on which the most common supervised ML models are trained: decision tree, random forest, support vector machine, k-nearest neighbor, naive Bayes, perceptron, multi-layer perceptron, and logistic regression. Using model optimization and significance testing, the best-performing classifier, i.e., support vector classifier (SVC), is selected. For an independent evaluation, a final experiment is conducted with participants unknown to the developed models. The accuracy results of the data-acquisition group are 93.03% (striking technique) and 98.26% (target object) and for the independent evaluation group 89.55% (striking technique) and 75.97% (target object). Therefore, it is concluded that the system based on SVC is suitable for target object and technique classification.