Artificial intelligence-integrated wearable technology in sports medicine injury prevention and rehabilitation: A narrative review

Abstract

Wearable technology has the potential to support customized training, injury prevention, rehabilitation, and gait analysis, enabling real-time monitoring of biomechanical, physiological, and performance-related data in sports medicine. This narrative review of the literature used Primary research data. PubMed, Google Scholar, and Web of Science were among the databases and search engines used to locate publications. This review focuses on the literature from the past 10 years on wearable sensor technologies combined with artificial intelligence (AI) and machine learning (ML) in sports medicine, particularly for injury prevention and rehabilitation. The results demonstrate the effectiveness of these technologies for injury risk assessment, rehabilitation, workload management, and gait training. Wearable technology enabled the creation of individualized training plans and the prevention of injuries by precisely monitoring heart rate, sleep quality, gait patterns, and training load. Automated feedback systems and predictive analytics can be improved through AI/ML integration. However, problems with sensor reliability, the lack of established protocols, variability in accuracy, and constraints on user comfort persisted. Wearable sensors have the potential to enable information-based, customized athlete care through improved monitoring and predictive capabilities, particularly when paired with AI/ML. Future research should focus on improving sensor accuracy, standardizing data-collection protocols, and fostering interdisciplinary collaboration to realize the benefits fully.

Ähnliche Arbeiten