AI-Powered Wearable IoT Devices for Mental Health Assessment, Stress Prediction, and Personalized Healthcare Recommendation Systems in Machine Learning and Cloud Computing

Abstract

AI, wearable Internet of Things (IoT), machine learning and cloud computing are coming together in a manner that enables continual and individual mental health care. This paper introduces an AI-driven Wearable IoT system to evaluate mental health, predict stress, and provide personalized healthcare suggestions. This proposed method combines physiological and behavioural data like heart rate, heart-rate variability, electrodermal activity, skin temperature, motion data, sleep patterns, and physical activity via wearable devices. These diverse signals can be converted to digital biomarkers via machine learning and deep learning algorithms for the early detection of stress states and forecast for future changes in mental health status. Cloud computing offers scalable data storage, model training, longitudinal analysis and secure healthcare-data-management, while intelligent recommendation layer creates personalized interventions according to the physiological, behavioral pattern of each individual. Recent research shows that wearable-based stress detection and multimodal sensing, digital phenotyping, and mood prediction are feasible, but there are issues of generalizability, data quality, privacy, interpretability, and validation in clinical settings. The suggested framework, therefore, promotes multimodal intelligence, personalized prediction, secure integration with the cloud and prevention support for healthcare, instead of the clinical diagnostic autonomy.