A Comprehensive AI-Based Mental Health Monitoring Toward Nervous System Exhaustion

Abstract

Mental health conditions, including stress, anxiety, depression, and mental fatigue, continue to affect millions of people and have become an important area of research. The increasing availability of wearable devices, physiological sensors, and digital platforms has encouraged the use of artificial intelligence for automated mental health assessment. A wide range of computational techniques, including traditional machine learning, deep learning, multimodal learning, and transformer-based models, have been explored to analyze physiological, behavioral, and textual data for identifying mental health conditions. This review provides a comprehensive comparison of these approaches by discussing the datasets used, feature extraction methods, model architectures, performance, advantages, and existing limitations reported in recent studies. Although significant progress has been achieved in improving detection accuracy, several challenges remain, such as limited dataset diversity, poor model generalization, privacy concerns, computational complexity, and the lack of continuous long-term monitoring. The findings also indicate that current research primarily focuses on identifying an individual\'s present mental state rather than evaluating changes that occur over extended periods. By consolidating the existing literature, this review highlights unresolved research challenges and discusses future directions for developing intelligent, multimodal frameworks that can support Nervous System Exhaustion (NSE) assessment and facilitate early burnout prevention.

Introduction

The studies reviewed demonstrate significant progress in the application of artificial intelligence for the detection and monitoring of stress, anxiety, depression, burnout, and mental fatigue. Machine learning, deep learning, wearable sensing technologies, multimodal data fusion, and transformer-based architectures have shown promising results in automated mental health assessment. However, several limitations continue to restrict the practical deployment and long-term reliability of these systems.

A major challenge identified across the reviewed literature is the reliance on small, homogeneous, or controlled datasets. Although such datasets are useful for developing and evaluating machine learning models, they may not adequately represent the physiological, behavioral, and environmental variations observed in real-world populations. Consequently, models trained under controlled conditions may experience reduced generalization when applied to individuals with different lifestyles, physiological characteristics, occupations, or daily activities.

Another important limitation is the susceptibility of wearable physiological signals to noise, motion artifacts, sensor placement variations, and environmental disturbances. Signals such as ECG, PPG, HRV, and EDA can be affected by physical activity and changes in sensor contact, which may reduce prediction reliability during continuous monitoring. Similarly, differences between consumer-grade and research-grade wearable devices can lead to variations in signal quality and model performance.

The literature also indicates that many existing approaches primarily focus on detecting the current mental or physiological state rather than identifying gradual changes that occur over extended periods. Stress, mental fatigue, and nervous system exhaustion may develop progressively through repeated exposure to workload, inadequate recovery, disturbed sleep, and prolonged physiological activation. Therefore, a system that only performs instantaneous classification may fail to recognize important long-term trends and early warning indicators.

Furthermore, although multimodal systems generally provide richer information than single-sensor approaches, combining multiple physiological, behavioral, and contextual data sources introduces additional challenges related to data synchronization, computational requirements, missing data, privacy, and model complexity. Deep learning and transformer-based models may provide high predictive performance but can require substantial computational resources and may offer limited interpretability. For healthcare-related applications, the ability to understand why a model produces a particular prediction is particularly important.

Personalization also remains an insufficiently addressed issue. Physiological responses to stress and fatigue vary considerably between individuals. A physiological pattern that indicates elevated stress in one person may not represent the same condition in another. Consequently, generalized models may not provide sufficiently reliable predictions for long-term individual monitoring unless mechanisms for personalized calibration or adaptive learning are incorporated.

Based on these observations, there is a need for an intelligent and robust mental health monitoring framework that can integrate multimodal physiological and behavioral information, handle noisy real-world data, adapt to individual differences, and analyze temporal patterns rather than relying solely on instantaneous measurements. The system should also aim to provide interpretable predictions while maintaining computational efficiency and protecting user privacy.

A further research opportunity lies in the early detection of Nervous System Exhaustion (NSE). Existing literature largely addresses stress, anxiety, burnout, and fatigue as separate or immediate classification problems, while comparatively less attention has been given to identifying the gradual transition from repeated stress and insufficient recovery toward prolonged nervous-system exhaustion. A long-term monitoring framework that analyzes trends in physiological and behavioral indicators could therefore provide an additional layer of early warning before severe deterioration occurs.

Hence, the identified research gap can be summarized as the need for a personalized, multimodal, explainable, privacy-aware, and computationally efficient AI system capable of continuous long-term monitoring and early identification of Nervous System Exhaustion under real-world conditions. Addressing these gaps can contribute toward the development of more reliable intelligent health-monitoring systems that move beyond simple detection of present mental states toward proactive and longitudinal mental health assessment.

Conclusion

This survey looked into recent research on how artificial intelligence and machine learning are being used to monitor mental health conditions such as stress, anxiety, depression, and mental fatigue. The studies reviewed show that there has been considerable progress using wearable sensing technology, analyzing physiological signals, applying multimodal learning methods, and using transformer-based models. These advances have led to better prediction abilities and more efficient ways to assess mental health. However, the analysis also points out several ongoing challenges, such as not having diverse enough datasets, poor performance across different individuals, high computational needs, limited model transparency, privacy issues, and not enough testing in real-world settings. A comparison of the selected studies shows that most current systems are built to detect immediate psychological states rather than tracking long-term physiological and behavioral changes linked to nervous system health. Moreover, many approaches focus heavily on stress detection but pay less attention to the full assessment and management of Nervous System Exhaustion (NSE).These findings suggest the need for smart frameworks that combine various physiological and behavioral indicators, offer clear and understandable predictions, and allow for continuous health monitoring in everyday situations. In short, this literature review highlights where current research stands, points out the main shortcomings of existing methods, and stresses the importance of creating reliable and personalized systems for detecting and managing Nervous System Exhaustion. Future research should aim to develop scalable, explainable, and multimodal machine learning models that can provide early prediction, ongoing monitoring, and personalized interventions to enhance long-term neurological and mental health.

References

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Copyright

Copyright © 2026 Aditi Rajesh, Bibi Ayesha, Nandini M R, Tulasi S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.