Annals of medicine · 2026
Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives.
СтатьяОбзорПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Dong Guozhong, Tan Xiaoxiao, Yuan Peijiang, Li Yanyan
Аннотация
BACKGROUND: Artificial intelligence (AI) and wearable sensors are increasingly reshaping sports injury risk prediction by enabling continuous, individualized, and data-driven assessment.
DISCUSSION: This review summarizes recent advances in wearable technologies - including inertial measurement units (IMUs), electromyography (EMG), physiological monitors, and flexible electronics - and their use in capturing biomechanical, physiological, and psycho-physiological indicators relevant to injury risk. Deep learning (DL) models, particularly those capable of temporal and multimodal fusion, have shown promise in predicting injuries such as anterior cruciate ligament (ACL) tears, muscle fatigue, and stress fractures. Through representative applications and mechanism-informed analysis, we highlight the growing role of interpretable AI, real-time feedback, and behavior-integrated predictive systems.
CONCLUSIONS: Key challenges remain, including data heterogeneity, limited generalizability, compliance issues, and privacy concerns. Future directions point toward personalized modeling, explainable systems, federated learning, and digital twin frameworks. Together, these advancements mark a shift toward proactive, intelligent injury prevention across athletic and clinical settings.
DISCUSSION: This review summarizes recent advances in wearable technologies - including inertial measurement units (IMUs), electromyography (EMG), physiological monitors, and flexible electronics - and their use in capturing biomechanical, physiological, and psycho-physiological indicators relevant to injury risk. Deep learning (DL) models, particularly those capable of temporal and multimodal fusion, have shown promise in predicting injuries such as anterior cruciate ligament (ACL) tears, muscle fatigue, and stress fractures. Through representative applications and mechanism-informed analysis, we highlight the growing role of interpretable AI, real-time feedback, and behavior-integrated predictive systems.
CONCLUSIONS: Key challenges remain, including data heterogeneity, limited generalizability, compliance issues, and privacy concerns. Future directions point toward personalized modeling, explainable systems, federated learning, and digital twin frameworks. Together, these advancements mark a shift toward proactive, intelligent injury prevention across athletic and clinical settings.