An Intelligent Athlete Signal Processing Methodology for Balance Control Ability Assessment with Multi-Headed Self-Attention Mechanism

In different kinds of sports, the balance control ability plays an important role for every athlete. Therefore, coaches and athletes need accurate and efficient assessments of the balance control ability to improve the athletes' training performance scientifically. With the fast growth of sport technology and training devices, intelligent and automatic assessment methods have been in high demand in the past years. This paper proposes a deep-learning-based method for a balance control ability assessment involving an analysis of the time-series signals from the athletes. The proposed method directly processes the raw data and provides the assessment results, with an end-to-end structure. This straight-forward structure facilitates its practical application. A deep learning model is employed to explore the target features with a multi-headed self-attention mechanism, which is a new approach to sports assessments. In the experiments, the real athletes' balance control ability assessment data are utilized for the validation of the proposed method. Through comparisons with different existing methods, the accuracy rate of the proposed method is shown to be more than 95% for all four tasks, which is higher than the other compared methods for tasks containing more than one athlete of each level. The results show that the proposed method works effectively and efficiently in real scenarios for athlete balance control ability evaluations. However, reducing the proposed method's calculation costs is an important task for future studies.