Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{176114,
        author = {Vaishnavi R. Dhurate and Dhanashri A. Bul and Kalash V. Deshmukh and Gaurav M. Upase and Prof. L.S. Bhattad},
        title = {Mental Health and Well Being Surveillance Assessment and Tracking Solution Among the Children},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5337-5341},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176114},
        abstract = {The increasing prevalence of mental health issues among children (ages 6-12) highlights the need for early assessment and intervention to prevent long-term psychological challenges. This research introduces an AI-driven mental health surveillance and assessment system, designed to assist parents, educators, and healthcare professionals in evaluating children's emotional well-being. The system utilizes a structured 25-question assessment based on the Youth Beck Inventory for Children and Adolescents, covering key emotional parameters such as anxiety, fear, sadness, anger, and frustration. Machine learning algorithms analyze user responses to determine mental health risk levels and generate detailed reports. Additionally, the system offers therapeutic interventions such as laughter therapy, spiritual therapy, child therapy, yoga therapy, speech therapy, along with an option to consult mental health professionals for further support. By integrating AI-based assessment with real-time evaluation, this solution aims to enhance early detection, personalized support, and overall well-being in children.},
        keywords = {AI-driven mental health assessment, child psychology, sentiment analysis, early intervention, machine learning.},
        month = {April},
        }