A Deep Learning Approach for Predicting On-Field Anterior Cruciate Ligament Injuries Using Wearable Sensors

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@article{1717153,
      author = {Jai Vinush C, Krishna Sai G M, Surya Prakash M, Mohammed Anam, Suma KV},
      title = {A Deep Learning Approach for Predicting On-Field Anterior Cruciate Ligament Injuries Using Wearable Sensors},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {4298-4306},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717153.pdf},
      abstract = {Anterior cruciate ligament (ACL) injuries are among the most serious and economically burdensome events in competitive football. This paper presents a wearable sensor-based framework that fuses triaxial inertial measurement unit (IMU) data, dual-channel surface electromyography (sEMG), and optical heart rate signals to assess ACL injury risk in near real time. Three families of classifiers — traditional machine learning, gradient boosting ensembles, and deep recurrent networks — were evaluated under a shared preprocessing and cross-validation protocol. CatBoost achieved the highest accuracy (92.3%, recall 0.93), while the proposed LSTM–GRU hybrid attained an F1-score of 0.92, reflecting strong temporal modelling of biomechanical sequences. Results suggest that multimodal sensor fusion can reliably separate normal from high-risk movement patterns, offering a viable route towards deployable athlete monitoring at both professional and grassroots level.},
      keywords = {Anterior Cruciate Ligament Injury Prediction, Wearable Sensor Analytics, Sports Injury Prevention, Ensemble Learning, LSTM–GRU Hybrid Network, Biomechanical Signal Processing.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1717153}
  }