Alleviating Surgeons’ Stress through Listening to Natural Sounds in a Half-Encapsulated Rest Space after an Operation: A Pilot, Longitudinal Field Study

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A physiology-based approach for estimation of mental fatigue levels with both high time resolution and high level of granularity

Mental fatigue (MF) monitoring is essential for eliminating accidents in high-risk tasks and providing better productivity management in daily work tasks involving human operators. Previous works only built MF monitoring systems with either high time resolution or high granularity level. We proposed a physiological-based approach to estimate MF Level every 2 seconds in a regression manner, a system that realized both high time resolution and high granularity level. The approach consists of an accurate MF level assessment method using the alignment score within a modified N-back task that is free from the lure effect, a long short-term memory (LSTM) deep learning framework, and a performance-reliability validation process. We used multiple physiological signals including ECG, respiration, and pupil diameter. As a result, we provided feasible estimating performance not worse than existing studies while realizing both high time resolution and high level of granularity. The interpretation analysis utilized accumulated local effects (ALE) to illustrate how black-box models make estimations and to improve the reliability of this approach