Lee Younggun, Lee Yongkyun, Kim Sungho, Kim Sitae, Yoo Seunghoon
Department of Electronics and Communication Engineering, Republic of Korea Air Force Academy, 635 Danjae-ro, Sangdang-gu, Cheongju 28187, Republic of Korea.
Department of Mathematics, Republic of Korea Air Force Academy, 635 Danjae-ro, Sangdang-gu, Cheongju 28187, Republic of Korea.
Bioengineering (Basel). 2025 Jan 26;12(2):116. doi: 10.3390/bioengineering12020116.
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.
疲劳管理对于飞行员、消防员和医护人员等高风险职业至关重要，在这些职业中，身心疲惫可能导致灾难性事故和生命损失。传统的疲劳评估方法，包括调查和生理测量，在实时监测和用户便利性方面存在局限性。为了解决这些问题，本研究引入了一种新颖的非接触式疲劳水平诊断系统，该系统利用多模态传感器数据，包括视频、热成像和音频。该系统将非接触式生物特征数据收集与一个由人工智能驱动的分类模型集成在一起，该模型能够以1至5级的尺度诊断疲劳水平，平均准确率为89%。关键特性包括实时反馈、用于个性化精度提升的自适应再训练，以及与高压力环境的兼容性。实验结果表明，根据用户反馈进行再训练可将分类准确率提高11个百分点。该系统的硬件在包括温度和电磁兼容性在内的各种运行条件下的稳健性得到了验证。这项创新通过实现精确、非侵入性和高效的疲劳监测，为提高关键部门的运行安全性和性能提供了一个切实可行的解决方案。
