Contactless Fatigue Level Diagnosis System Through Multimodal Sensor Data

Fatigue management is critical for high-risk professions such as pilots, firefighters, and healthcare workers, where physical and mental exhaustion can lead to catastrophic accidents and loss of life. Traditional fatigue assessment methods, including surveys and physiological measurements, are limited in real-time monitoring and user convenience. To address these issues, this study introduces a novel contactless fatigue level diagnosis system leveraging multimodal sensor data, including video, thermal imaging, and audio. The system integrates non-contact biometric data collection with an AI-driven classification model capable of diagnosing fatigue levels on a 1 to 5 scale with an average accuracy of 89%. Key features include real-time feedback, adaptive retraining for personalized accuracy improvement, and compatibility with high-stress environments. Experimental results demonstrate that retraining with user feedback enhances classification accuracy by 11 percentage points. The system's hardware is validated for robustness under diverse operational conditions, including temperature and electromagnetic compliance. This innovation provides a practical solution for improving operational safety and performance in critical sectors by enabling precise, non-invasive, and efficient fatigue monitoring.