ADVANCED AI-BASED ATHLETE MONITORING AND INJURY PREVENTION IN SWITZERLAND: A MULTIMODAL DEEP LEARNING FRAMEWORK FOR PERSONALIZED PERFORMANCE OPTIMIZATION, REAL-TIME MUSCULOSKELETAL INJURY PREDICTION AND ANTERIOR CRUCIATE LIGAMENT INJURY PREVENTION

Abstract

Abstract

Rapid advances in artificial intelligence have created opportunities for data-driven athlete monitoring, performance enhancement, and proactive prevention of musculoskeletal and anterior cruciate ligament (ACL) injuries in Switzerland. A longitudinal dataset was compiled from 320 competitive athletes representing football, athletics, cycling, skiing, ice hockey, basketball, volleyball, and other endurance and indoor sports. The cohort included 198 male and 122 female athletes, with a mean age of 24.7 ± 4.3 years, body mass index of 22.9 ± 2.4 kg/m², and sporting experience of 7.2 ± 3.8 years. Multimodal information was collected through wearable inertial sensors, heart-rate monitors, GPS devices, training-load records, sleep and recovery assessments, biomechanical screening, medical histories, and athlete-reported wellness measures. After preprocessing, feature normalization, missing-value treatment, and class balancing, the data were divided into training, validation, and testing sets. The proposed architecture integrated convolutional neural networks, bidirectional long short-term memory layers, and an attention mechanism to capture spatial, temporal, and athlete-specific risk patterns. Its performance was compared with logistic regression, support vector machines, random forests, artificial neural networks, and XGBoost using accuracy, precision, recall, F1-score, and ROC-AUC. The multimodal model achieved 94.8% accuracy, 94.1% precision, 93.6% recall, a 93.8% F1-score, and a ROC-AUC of 0.972 for musculoskeletal injury prediction. For ACL-specific risk classification, it obtained 93.2% accuracy, 92.4% sensitivity, 94.0% specificity, and a ROC-AUC of 0.961, outperforming the strongest conventional model, XGBoost, which achieved 92.6% accuracy and a 0.950 ROC-AUC. Explainable attention maps and continuously updated risk scores enabled coaches, clinicians, and athletes to identify workload spikes, asymmetric knee mechanics, neuromuscular fatigue, and insufficient recovery before injury occurrence promptly. The framework also facilitated transparent decisions and timely preventive interventions. Implementation of personalized recommendations increased the mean performance score from 78.4 to 90.0, improved recovery from 72.1 to 83.8, reduced excessive workload by 21.3%, overall injury risk by 27.6%, and high-fatigue sessions by 34.9%. Sleep duration increased from 6.9 to 7.4 hours, while training adherence rose from 82.7% to 91.3%. These findings demonstrate that multimodal deep learning can support proactive, individualized, and clinically informed athlete management within Switzerland’s sports ecosystem.