Phase-Based Quantification of Sports Performance Metrics Using a Smart IoT Sensor

Abstract

Sports performance is often judged based on the results of a series of motions rather than observing and analyzing the detailed sequential motions that lead to the results. Hence, subjective feedback from the coaches is often ineffective in improving player performance. In this work, we custom-built a smart Internet-of-Things wristband motion sensor to implement data-based sports performance evaluation. A phase-based feature selection method is also proposed to assess the athletes sequential detailed motion for selected sport activities. To demonstrate the merits of this technology, we quantified the quality of the sequential motions of a specific type of volleyball-serve by analyzing 183 samples of motion data obtained from a total of 18 players. The general skill levels (i.e., elite, sub-elite, and amateur) of the players were identified by machine learning algorithms, with accuracies of up to 95%. Moreover, we adopted biomechanical principles to extract 11 motion-related performance metrics from various phases of the players serve motion. We identified the distributions of these metrics across different skill levels and found 8 key metrics that were highly correlated to the skill level of a players. We suggest that these metric distributions can be used as a reference for providing feedback to the coaches and players, to improve a players skill in the future. This phased-based analysis method can potentially be applied across many sports to increase the effectiveness of athletes training.