Human-centered and context-aware smart ML-based IoT framework for online fatigue detection: A real-world study of football training

Fatigue is one of the factors that most influences competitive athletes’ performance, leading to injuries and overtraining. To effectively monitor and predict fatigue levels during real-world training, it is necessary to integrate Internet of Things (IoT) technology with machine learning (ML). In this context, the paper presents three main contributions : a) a smart IoT framework that integrates edge and cloud-based modules to collect physiological parameters, monitor fatigue during real-world sessions, and assist coaches in optimizing exercise strategies ; b) a dataset collected through the proposed framework in a real pilot study with eight futsal players over five training sessions, each lasting between 35 and 50 m depending on performed exercises, using ECG and PPG-based sensors ; c) an online ML-based fatigue detection module and on-cloud analysis of various ML models, traditional and deep learning, including CNN+GRU, XGBoost, and Transformer architectures, and context-aware feature sets. We evaluated the accuracy of our fatigue detection method using standard metrics, achieving an F1-score of up to 95 % with pilot study data. Our framework incorporates a context-aware design, where contextual information (exercise type) and sensing modality (ECG- or PPG-based) are explicitly integrated with physiological features (HRV and HR) in the fatigue prediction model to adapt it to different settings, improving robustness and interpretability. Finally, we evaluated the framework’s efficacy and the value of user and expert input, highlighting the benefits of integrating IoT and ML within a human-centered, context-aware approach to balance sensor accuracy, comfort, and efficiency in competitive sports training.