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

Abstract

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.

Study on the Difference of Human Body Balance Stability Regulation Characteristics by Time-Frequency and Time-Domain Data Processing Methods

“…In addition, one study demonstrated that WT was appropriate for diseases connected to the identification and diagnosis of balance [ 19 ]. It has been discovered that WT can reveal the change in COP displacement that cannot be observed by conventional methods [ 29 , 30 , 31 ]. Studies have revealed that the increase in energy in the frequency band indicated that subjects were at risk of falling, which was similar to the results of this study, and the larger the WT, the worse the balance capability [ 9 ].…”

A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks

“…The fundamental Transformer architecture comprises an input layer, multi-head self-attention blocks, normalization layers, feedforward layers, and residual connection layers. Essentially, it embodies an Encoder-Decoder framework [ 26 ]. Key components within the Transformer model are the multi-head self-attention mechanism and the autoencoder.…”

Applied Computing and Artificial Intelligence

“…Lu et al [7] propose 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 endto-end structure.…”