Evaluation System for Martial Arts Demonstration from Smartphone Sensor Data Using Deep Neural Networks on Noisy Labels

Smartphones and smartwatches are widespread. Devices have with sensors. Research activities to obtain data from sensors to recognize sports motion and find modifications are on the rise. These studies make it possible to provide movement feedback without a coach. However, a few studies have focused on the correctness of the movement, or which parts of the movement are correct. Herein, we propose a method to evaluate the movements of a martial arts demonstration based on sensor data obtained from a smartphone. In the demonstration, several movements, such as punching and kicking, are performed. The coach considers each movement and evaluates the overall movement comprehensively. Regarding the coach evaluation, we evaluate sensor data by dividing the data and using machine learning. This method was applied to the acceleration data of taekwondo demonstrations. It was shown to reproduce the instructor's score with high accuracy.