Doctors face high cognitive loads daily. Therefore, mental fatigue monitoring becomes essential for maintaining patient safety in Indian hospitals. A recent study involving 40 participants explored how task segmentation influences fatigue levels and physiological responses. Researchers used a Stroop-based task under two structures: NORMAL and HALF. While both lasted the same amount of time, they differed in block segmentation. Consequently, the results showed that task design significantly alters how fatigue develops over time.

Initially, participants used compensatory control to maintain performance. However, as exhaustion grew, they shifted toward increased automaticity. This change resulted in faster reaction times but caused a notable decline in accuracy. Electrodermal activity (EDA) and electrocardiography (ECG) emerged as the most reliable single-signal indicators for clinicians to track.

Improving Mental Fatigue Monitoring Through Multimodal Fusion

Multimodal physiological signals provided the most reliable data. By combining five distinct signals, including respiration and EEG, the researchers achieved high precision. Specifically, using the LightGBM algorithm for fusion resulted in an F1 score of 0.9186. This demonstrates the power of integrated data in predicting cognitive decline.

In contrast, the HALF structure attenuated physiological differentiation at higher fatigue levels. This suggests that task blocks influence how we perceive exhaustion. Understanding these dynamics is vital for high-risk settings, such as emergency departments and surgical suites, where continuous monitoring can prevent errors.

Frequently Asked Questions

What are the most reliable physiological indicators of mental fatigue?

Electrodermal activity (EDA) and electrocardiography (ECG) are the most effective single-signal indicators. However, combining multiple signals yields the highest accuracy for detection.

How does task structure influence the detection of exhaustion?

Task segmentation affects how fatigue manifests physiologically. For instance, the HALF structure in this study attenuated physiological differentiation, potentially masking true exhaustion levels compared to standard structures.

Disclaimer: This content is for informational and educational purposes only. It does not constitute professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition. Refer to the latest local and national guidelines for clinical practice.

References

Liu S et al. Impact of task structure on mental fatigue induction and work performance. Ergonomics. 2026 Jun 05. doi: 10.1080/00140139.2026.2681130. PMID: 42247254.

Ren L, Wu L, Feng T, Liu X. A New Method for Inducing Mental Fatigue: A High Mental Workload Task Paradigm Based on Complex Cognitive Abilities and Time Pressure. Brain Sci. 2025;15(6):541. doi: 10.3390/brainsci15060541.

Cos CA, et al. Enhancing Mental Fatigue Detection through Physiological Signals and Machine Learning Using Contextual Insights and Efficient Modelling. JSAN. 2023;12(11):234. doi: 10.3390/jsan12060234.