[Paper Review] Sensors and Systems for Monitoring Mental Fatigue: A systematic review

Prabin Sharma, Joanna C. Justus+2 more|arXiv (Cornell University)|Jul 4, 2023

Sleep and Work-Related FatiguePsychology3 citations

TL;DR

This systematic review evaluates biosensor-based systems for monitoring mental fatigue, focusing on EEG and ambient sensors. It finds that EEG-based systems offer moderate to good sensitivity for fatigue detection, with no significant benefit from high-density EEG, and emphasizes the need for integrated wearable and ambient sensor systems for real-world deployment in industries like transportation and e-learning.

ABSTRACT

Mental fatigue is a leading cause of motor vehicle accidents, medical errors, loss of workplace productivity, and student disengagements in e-learning environment. Development of sensors and systems that can reliably track mental fatigue can prevent accidents, reduce errors, and help increase workplace productivity. This review provides a critical summary of theoretical models of mental fatigue, a description of key enabling sensor technologies, and a systematic review of recent studies using biosensor-based systems for tracking mental fatigue in humans. We conducted a systematic search and review of recent literature which focused on detection and tracking of mental fatigue in humans. The search yielded 57 studies (N=1082), majority of which used electroencephalography (EEG) based sensors for tracking mental fatigue. We found that EEG-based sensors can provide a moderate to good sensitivity for fatigue detection. Notably, we found no incremental benefit of using high-density EEG sensors for application in mental fatigue detection. Given the findings, we provide a critical discussion on the integration of wearable EEG and ambient sensors in the context of achieving real-world monitoring. Future work required to advance and adapt the technologies toward widespread deployment of wearable sensors and systems for fatigue monitoring in semi-autonomous and autonomous industries is examined.

Motivation & Objective

• To critically evaluate theoretical models of mental fatigue and their applicability in sensor-based monitoring.
• To identify and analyze key enabling sensor technologies, particularly biosensors like EEG, for tracking mental fatigue in real time.
• To assess the performance and limitations of existing biosensor-based systems in detecting mental fatigue across diverse environments.
• To examine the feasibility and challenges of deploying wearable and ambient sensor systems for continuous, real-world mental fatigue monitoring.
• To identify future research directions for advancing sensor systems toward widespread adoption in semi-autonomous and autonomous industries.

Proposed method

• Conducted a systematic literature review using structured search criteria across academic databases, focusing on studies published in recent years.
• Selected 57 studies (N=1082 participants) that employed biosensors for mental fatigue detection, with a focus on EEG-based systems.
• Evaluated sensor technologies based on signal quality, usability, and real-world applicability, including wearable and ambient sensors.
• Analyzed performance metrics such as sensitivity, specificity, and accuracy of fatigue detection across studies.
• Synthesized findings on the comparative effectiveness of high-density versus standard EEG systems for fatigue monitoring.
• Provided critical discussion on system integration, scalability, and deployment challenges in real-world settings like workplaces and e-learning platforms.

Experimental results

Research questions

• RQ1 What are the most effective sensor technologies for detecting mental fatigue in real-world environments?
• RQ2 How does the performance of EEG-based systems vary with sensor density, particularly comparing high-density and standard EEG setups?
• RQ3 What are the key limitations of current biosensor-based systems in monitoring mental fatigue across different application domains?
• RQ4 In what ways can wearable and ambient sensors be integrated to improve continuous and unobtrusive mental fatigue monitoring?
• RQ5 What future research is required to enable scalable deployment of fatigue monitoring systems in semi-autonomous and autonomous industries?

Key findings

• EEG-based sensors demonstrated moderate to good sensitivity for detecting mental fatigue, making them a viable option for real-time monitoring.
• High-density EEG sensors did not show incremental benefits over standard EEG systems for mental fatigue detection, suggesting cost-performance trade-offs favor simpler setups.
• The majority of reviewed studies (N=1082 participants) relied on EEG, indicating its dominance as the primary modality for biosignal-based fatigue tracking.
• Despite strong performance in controlled settings, significant challenges remain in deploying these systems in real-world environments due to environmental noise and user variability.
• Integration of wearable EEG with ambient sensors is identified as a promising pathway for enabling continuous, unobtrusive, and reliable fatigue monitoring in daily settings.
• Future work must focus on improving system robustness, scalability, and adaptability for deployment in high-stakes domains such as transportation and healthcare.