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.