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@article{192805,
        author = {D PRASANTH and V RUKESHBHARATHI and D SANGAMITHRA and N SATHYAPRIYA},
        title = {A Survey on Artificial Intelligence – Powered Stress Monitoring and Adaptive Solution},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {2120-2122},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192805},
        abstract = {This study presents a comprehensive survey on Artificial Intelligence–powered stress monitoring and adaptive solution systems. The survey explores how AI techniques are used to detect, analyze, and manage stress in individuals across different environments. Various data sources such as physiological signals, behavioral patterns, and contextual information are reviewed. Machine learning and deep learning models employed for stress detection are examined in detail. The paper highlights the role of wearable devices and smart sensors in real-time stress monitoring. Adaptive solution mechanisms, including personalized feedback and intervention strategies, are discussed. The effectiveness of AI-driven systems in improving mental well-being is evaluated. Key challenges such as data privacy, accuracy, and ethical concerns are identified. The survey also compares existing models and frameworks in stress management applications.},
        keywords = {Physiological Signals, Deep Learning, Random Forest, Decision Tree, Stress Detection.},
        month = {February},
        }