Case study shows 77% sensitivity and 90% specificity in predicting injuries using wearable data, highlighting potential for early warning systems.
Wu et al. introduce a novel approach to sports injury prediction by combining survival modeling with Hidden Markov Models (HMMs). The authors present a well‐executed case study using longitudinal wearable data from an Australian Football League (AFL) club, achieving an impressive AUC of 0.93, with 77% sensitivity and 90% specificity. These results highlight the practical utility of their approach for early warning systems and performance management in both professional and community sport settings.
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Rhythm Grover (2025) studied this question.
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