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June 8, 2026Physical Education of StudentsOpen Access

AI-integrated wearables predict sports injuries with ~90% accuracy.

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Why the study?

Do wearable sensors combined with AI/ML models accurately predict sports injuries and monitor rehabilitation in physically active individuals?

Population

15 studies involving athletes or physically active individuals

Comparison

Wearable sensors combined with AI/ML models vs not specified

Design

Systematic review following PRISMA 2020 guidelines

Key result

AI-integrated wearable systems achieved 86-95% classification accuracy for sports injury prediction, while workload-based models showed a 15-fold increase in injury risk at high workload ratios.

Authors

RSRenu SharmaRDRajdeep DasTYTapesh Yadav

Discussion

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Overview

AI wearables show promise for athlete injury monitoring; extends evidence synthesis to university physical education contexts.

Study Design

Type

Systematic Review (n=15)

Structured PICO

Do wearable sensors combined with AI/ML models accurately predict sports injuries and monitor rehabilitation in physically active individuals?

P
Population
Systematic review of 15 studies evaluating AI-integrated wearable sensors for sports injury prediction and rehabilitation monitoring in athletes and physically active individuals.
E
Exposure
Wearable sensors (predominantly IMUs) combined with AI/ML models (CNN, LSTM, RNN) for injury prediction or rehabilitation monitoring
O
Outcome
Diagnostic performance (classification accuracy, F1 scores) for injury prediction and rehabilitation monitoring

AI-integrated wearable systems show high accuracy for sports injury prediction and monitoring, though current evidence is limited by small sample sizes and lack of external validation.

Limitations

  • small samples
  • absent external validation
  • analytical limitations
  • heterogeneous injury definitions

Cite This Study

Sharma et al. (2026) conducted a systematic review in Sports injury (n=15). AI-integrated wearable sensors was evaluated on Diagnostic performance for injury prediction and rehabilitation monitoring. AI-integrated wearable systems achieved 86-95% classification accuracy for sports injury prediction, while workload-based models showed a 15-fold increase in injury risk at high workload ratios.

synapsesocial.com/papers/6a4e41e8e312ebeda78cd517https://doi.org/10.15561/20755279.2026.0303
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Artificial intelligence and wearable technology in athlete load management and injury prediction: a systematic literature review2026
  2. 2Injury Prediction and Risk Modelling in Team Sports Using Artificial Intelligence and Sensor-Based Monitoring: A Scoping Review2026 · 3 citations
  3. 3From data to prevention: A systematic review of artificial intelligence applications in sports injury prediction2026
  4. 4Artificial Intelligence in Sports Biomechanics: A Scoping Review on Wearable Technology, Motion Analysis, and Injury Prevention2025 · 106 citations
  5. 5Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review2026 · 5 citations