Paradoxical Tensions Related to AI-Powered Evaluation Systems in Competitive Sports

Mazurova, Elena; Standaert, Willem; Penttinen, Esko; Tan, Felix Ter Chian

doi:10.1007/s10796-021-10215-8

Cited by 14 publications

(5 citation statements)

References 51 publications

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“…The results of this article suggest that the current CPR quality evaluation system should not depend only on specialist scoring, and that differences in competition scenes will significantly affect the specialist scoring. Human judgments are susceptible to error due to a host of factors, from fatigue to various biases of judges [26]. Specialist scoring can be used as a supplement to the simulator scoring, and indicators that cannot be quantified and monitored, such as whether the practice posture is standardized, whether the arm is completely perpendicular to the ground when pressing, etc.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of cardiopulmonary resuscitation quality during the pandemic of COVID-19

Liu

Wang

et al. 2022

BMC Emerg Med

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Background Cardiopulmonary resuscitation (CPR) is an important technique of first aid. It is necessary to be popularized. Large-scale offline training has been affected after the outbreak of Coronavirus disease 2019 (COVID-19). Online training will be the future trend, but the quality of online assessment is unclear. This study aims to compare online and offline evaluations of CPR quality using digital simulator and specialist scoring methods. Methods Forty-eight out of 108 contestants who participated in the second Chinese National CPR Skill Competition held in 2020 were included in this study. The competition comprised two stages. In the preliminary online competition, the contestants practiced on the digital simulator while the specialist teams scored live videos. The final competition was held offline, and consisted of live simulator scoring and specialist scoring. The grades of the simulator and specialists in different stages were compared. Results There was no statistical significance for simulator grades between online and offline competition(37.7 ± 2.0 vs. 36.4 ± 3.4, p = 0.169). For specialists’ grades, the video scores were lower than live scores (55.0 ± 1.4 vs. 57.2 ± 1.7, p < 0.001). Conclusion Simulator scoring provided better reliability than specialist scoring in the online evaluation of CPR quality. However, the simulator could only collect quantified data. Specialist scoring is necessary in conjunction with online tests to provide a comprehensive evaluation. A complete and standardized CPR quality evaluation system can be established by combining simulator and specialist contributions.

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Section: Discussionmentioning

confidence: 99%

Evaluation of cardiopulmonary resuscitation quality during the pandemic of COVID-19

Liu

Wang

et al. 2022

BMC Emerg Med

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“…Because of advancements in information and communication technologies, AI is increasingly used in sports [16][17][18][19][20]. There is a need to improve the precision of deep learning for use in diagnosing sports injuries.…”

Section: Application Of Ai In Sportsmentioning

confidence: 99%

“…As can be seen from equation (17), 𝑐 of the output state of the current node is weighted by the state of the previous node and the internal information of the current node. As long as 𝑓 of the forget gate is not 0, LSTM can memorize the information of the previous node.…”

Section: 𝑖 = 𝜎(𝑊 𝑥 + 𝑊 ℎ + 𝑏 )mentioning

confidence: 99%

Cloud IoT-Oriented Neural Network-Based Taekwondo Teaching Scheme

2024

J Multimed Inf Syst

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As Taekwondo is widely accepted, there is a rapid increase in Taekwondo learners. Traditional Taekwondo teaching mode is challenging to meet the increasing educational needs. In Taekwondo teaching, in addition to teaching the basic skills of combat, Taekwondo Poomsae teaching is also required. The quality of Taekwondo Poomsae learning is critical to the overall strength of Taekwondo. For a long time, the judgment of the quality of Poomsae action has relied on the manual evaluation of teachers, which could be more conducive to forming an objective and accurate quality score. Therefore, it is necessary to introduce in-depth learning to find new solutions to these problems. First, a multi-sensor data fusion method is proposed to collect Taekwondo Poomsae action. Then, a Taekwondo Poomsae expertise integrated multiview feature extraction method is proposed. Finally, Convolutional Neural Network (CNN)-Mogrifier Long Short-Term Memory (LSTM) is proposed to train the generated Taekwondo Poomsae action scoring model, identifying whether Taekwondo Poomsae action meets the standard and improve Taekwondo teaching. The effect test of Taekwondo Poomsae action intelligent evaluation shows that the results of the proposed method follow the evaluation given by the teachers, and the discrimination of the scores is moderate, which indicates that the proposed method has good Taekwondo Poomsae action quality evaluation ability. Furthermore, the experimental results show that CNN-Mogrifier LSTM achieves high recognition accuracy of Taekwondo Poomsae, gives comprehensive evaluation and improvement suggestions for action completion quality, breaks away from the restrictions of teachers and venues, and realizes automatic and smart Taekwondo teaching.

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“…While traditional assessment methods often rely on the personal experience and intuition of coaches or experts, the introduction of AI adds science and objectivity to the process. By analyzing and comparing a large amount of data, the AI system is able to give more comprehensive and accurate assessment results, so that an athlete's performance is no longer a vague concept, but a specific indicator that can be quantified and compared (Mazurova et al, 2022). In addition, the intervention of AI has also greatly improved the efficiency of assessment.…”

Section: Case 2: Artificial Intelligence-based Analysis and Evaluatio...mentioning

confidence: 99%

Review Study Of Integrating Ai Technology Into Sports Training System

Su,

Ge,

et al. 2024

Kuey

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This article mainly reviews the application of artificial intelligence technology in the field of physical education and its future development trend. The article analyses how AI technology can enhance the quality and efficiency of physical education, providing students with better teaching experience and training effects through intelligent teaching aids, virtual reality technology and big data analysis. The article also highlights the important role of AI technology in promoting the personalized and interdisciplinary development of physical education, demonstrating the potential and prospects for its widespread application in the field of physical education. At the same time, the article also discusses the technical, ethical and privacy challenges facing the application of AI technologies in physical education, and puts forward suggestions for strengthening regulation and norms to ensure the healthy development of the technologies and maximize the benefits to society. In looking at the future development trend, the article points out that AI technology will continue to drive innovation and change in the field of physical education, injecting new vigor into the development of physical education globally.

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Paradoxical Tensions Related to AI-Powered Evaluation Systems in Competitive Sports

Cited by 14 publications

References 51 publications

Evaluation of cardiopulmonary resuscitation quality during the pandemic of COVID-19

Evaluation of cardiopulmonary resuscitation quality during the pandemic of COVID-19

Cloud IoT-Oriented Neural Network-Based Taekwondo Teaching Scheme

Review Study Of Integrating Ai Technology Into Sports Training System

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