• Dr. S. Yamuna Rani

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is transforming the landscape of modern healthcare. This paper presents a comprehensive study on leveraging AI-powered IoT wearables to monitor mental health conditions at home. Using the WESAD dataset, we evaluate standard machine learning models—Logistic Regression, Support Vector Machine, Random Forest, and K-Nearest Neighbors—to classify mental states such as stress, amusement, and baseline. The methodology involves data preprocessing, feature engineering, model training, and performance evaluation. Our findings reveal Random Forest as the most effective model with an F1-score of 88%. The results demonstrate the feasibility and effectiveness of integrating AI-IoT systems in home-based mental healthcare. Future directions include real-time deployment, privacy-preserving analytics, and personalization of models.

AI-IoT Wearables for Home Mental Health Care

"AI-IoT Wearables for Home Mental Health Care", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.689-691, May-2025, Available :http://www.jetir.org/papers/JETIRGV06097.pdf

"AI-IoT Wearables for Home Mental Health Care", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. pp689-691, May-2025, Available at : http://www.jetir.org/papers/JETIRGV06097.pdf