Current Volume 9
A Deep Learning Approach for Predicting On-Field Anterior Cruciate Ligament Injuries Using Wearable Sensors
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.
Citations
IRE Journals:
Jai Vinush C, Krishna Sai G M, Surya Prakash M, Mohammed Anam, Suma KV "A Deep Learning Approach for Predicting On-Field Anterior Cruciate Ligament Injuries Using Wearable Sensors" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 4298-4306 https://doi.org/10.64388/IREV9I10-1717153
IEEE:
Jai Vinush C, Krishna Sai G M, Surya Prakash M, Mohammed Anam, Suma KV
"A Deep Learning Approach for Predicting On-Field Anterior Cruciate Ligament Injuries Using Wearable Sensors" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1717153
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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