A major obstacle in fields including sports, clinical rehabilitation, and workplace safety is the timely detection and prevention of physical injuries. The majority of conventional monitoring systems are reactive, depending on post-event analysis or unimodal data sources, which restricts their ability to provide
proactive actions and early warnings. Furthermore, current AI-driven health systems lack rigorous validation procedures, which compromises their suitability for practical implementation in safety-critical settings.
In this work, we present MHIDS (Multimodal Hybrid Injury Detection System), an integrated, AI-based diagnostic framework that combines wearable physiological sensors, computer vision,and personalized physiological modeling for real-time injury forecasting. A continuously updated digital twin is employed
to capture each user’s biomechanical and physiological profile, allowing adaptive, individualized risk assessment. Unlike conventional approaches, MHIDS incorporates a closed-loop feedback mechanism that dynamically reconfigures sensing parameters and provides actionable recommendations (e.g., posture correc-
tion, intensity adjustment, or rest scheduling), thereby shifting the paradigm from passive detection to proactive prevention.
To guarantee correctness and operational trustworthiness,MHIDS is formally modeled in UPPAAL as a network of timed automata, ensuring critical properties such as bounded response times (<100 ms), safety, liveness, and deadlock freedom. Experimental validation using the publicly available MHEALTH dataset demonstrates superior predictive performance, achieving an accuracy of 99.21%, precision of 98.94%, recall of 99.07%,and F1-score of 99.00%, significantly outperforming state-of-the art baselines. Index Terms—Multimodal, Hybrid, Injury, Detection, AI, Healthcare Monitoring Auteurs : Imen Chebbi , Sarra Abidi, Leila Ben Ayed

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Heart failure (HF) is still a major cause of morbidity and death worldwide, hence prompt intervention requires early
detection. In this work, we suggest a unique hybrid deep learning architecture that uses clinical data to predict the early development of heart failure by combining supervised and unsupervised learning. Our design uses a deep neural network (DNN) for risk categorization after a deep autoencoder for denoised feature
extraction.
Unlike prior works that focus solely on predictive accuracy,we incorporate formal logic-based safety constraints using the Marabou verification framework, enabling our model to operate with mathematical safety guarantees an essential step for real world deployment in clinical settings. Specifically, we verify two
critical properties: output stability (resistance to small input perturbations) and bounded input robustness (reliable behavior
within clinically valid input ranges).
The model achieves a classification accuracy of 99.45%,validated through stratified 10-fold cross-validation and repeated trials to ensure statistical robustness. Our results demonstrate that integrating formal methods with deep learning significantly improves both performance and safety, highlighting the framework’s potential for deployment in trustworthy clinical AI systems.
Index Terms—Safety, Formally, Verified, Heart, Failure, DNN

Auteurs : Sarra Abidi , Imen Chebbi, Leila Ben Ayed

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