Application Analysis of Wearable Technology and Equipment Based on Artificial Intelligence in Volleyball

Abstract

Today, while people’s satisfaction with materials is high, the pursuit of health has begun and sports are becoming increasingly important. Volleyball is a good physical and mental exercise, which helps improve the health of the body. However, excessive exercise usually leads to muscle strain and more serious accidents. Therefore, how to effectively prevent excessive fatigue and sports injuries becomes more and more important. In the past, some methods of exercise fatigue detection were mostly self-assessment through some indicators, which lacked real-time and accuracy. With the advancement of smart technology, in order to better detect sports fatigue, smart wearable technology and equipment are used in volleyball. Firstly, surface electromyography signals (sEMG) are collected through wearable technology and equipment. Secondly, the signal is preprocessed to extract features that are conducive to exercise fatigue assessment. Finally, a motion fatigue detection algorithm is designed to identify and classify features and evaluate the motion status in real-time. The simulation results show that it is feasible to collect ECG signals and EMG signals to detect exercise fatigue. The algorithm has good recognition performance, can evaluate exercise conditions in real-time, and prevent fatigue and injury during exercise.

Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion

“…The mean recognition rate was 93.58%. Compared with the study report of Shangbin Li [ 27 ] (Reconition rate: 83.15–93.62%), the recognition rate of the model in this paper is improved. By comparison with the results based on pure sEMG and pure ECG signals, the model based on feature fusion shows better recognition precision and performance.…”

Modeling the gameplay actions of elite volleyball players and teams based on statistical match reports

“…In modern volleyball, data-based modeling is critically important for enhancing training methodologies and game strategies. Aldous et al [40] and Ntzoufras et al [41] are developing stochastic and Bayesian methods for analyzing game dynamics, while Dai et al [42] and Yuan et al [43] are exploring the role of technological innovations in analyzing volleyball techniques. Endriani et al [44] proposes a model based on Umbrella Learning, while Leng et al [45], Li et al [46], and Wang et al [47] use random matrix models for analyzing learning and gameplay actions.…”

“…A set of studies [40,41] employ stochastic and Bayesian frameworks, including Markov chains and set difference prediction models, to dissect game dynamics. The integration of cuttingedge technologies, such as artificial intelligence and wireless communication networks, in analyzing and fine-tuning volleyball techniques and strategies is the focus of research [42,43]. Endriani et al [44] put forth a lower pass model anchored in the Umbrella Learning paradigm.…”

A Bibliometric Analysis on Machine Learning in Sports Studies From 1999 to 2021

“…5 When the related literature is reviewed, it is observed that studies were conducted on numerous subtopics such as the prediction of match results using the machine learning approach, the use of artificial neural networks in sports biomechanics, the creation of algorithms through collecting electrocardiography and electromyography signals to determine exercise fatigue and the prevention of fatigue and injury during exercise, the creation of soccer teams and transfer support using the machine learning approach, making strategic decisions and building the best team, the investigation of professional soccer players' wages, the direction of coaching experience, and the enhancement of player performance. [6][7][8][9][10][11][12][13] Artificial intelligence refers to the technology that imitates human tasks and uses machine learning to identify how these tasks will be imitated. 14 The data analytics focus of machine learning involves artificial intelligence making inferences from computer experience as well as learning by itself and playing a supporting role.…”