Utilizing AI and IoT technologies for identifying risk factors in sports

• Published in: Heliyon Vol. 10; no. 11; p. e32477
• Main Authors: Chen, Zhiling, Dai, Xinghong
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15-06-2024; Elsevier
• Subjects: Athlete safety; IoT; Performance optimization; Risk factor identification; Scaled conjugate gradient algorithm; Sports analytics; Athlete safety; Risk factor identification; AI; Sports analytics; Performance optimization; Scaled conjugate gradient algorithm; IoT
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e32477

A dynamic cooperation is poised to redefine the limits of athlete safety and performance optimization in the dynamic field of sports science. A new age in sports analysis is promised by the combination of artificial intelligence (AI) and the internet of things (IoT), one in which data-driven insights not only improve our comprehension of athletic performance but also aid to reduce hazards. This academic work explores the complex interactions between AI and IoT in the context of sports. The IoT and AI integration appear to be a strong mix that has the potential to redefine the standards for athlete safety and performance improvement. This study explores the complex interactions between AI and IoT in the field of sports, emphasizing their combined potential for identifying risk factors in a variety of fields. There is a chance to proactively solve sports-related difficulties by utilizing the data-driven capabilities of IoT and the analytical power of AI, opening the door for better informed tactics and decision-making. Through an exploration of this symbiotic relationship, this paper seeks to underline the transformative potential of these technologies in fostering a safer and more performance-oriented sports environment.