Smart Band Enabled AI-System for Mental Health Tracking

Abstract

The paper presents the development of an AI- enabled smart band designed to continuously monitor mental wellness through the real-time analysis of three key physiological indicators: heart rate, body temperature, and movement patterns. These signals are captured by onboard sensors and transmitted wirelessly to a cloud-based platform using the ESP32/Node MCU module. The system applies AI algorithms to detect variations and correlations in the data that may indicate mental stress, fatigue, or anxiety. Elevated heart rate, abnormal temperature fluctuations, and irregular movement patterns—detected through the accelerometer—serve as critical markers for early- stage mental health concerns. The processed data is visualized on a user-friendly web and mobile dashboard, where users can monitor their mental state trends and receive real-time alerts. When stress thresholds are exceeded, the system delivers instant notifications via app alerts, SMS, or email, encouraging timely interventions. Personalized suggestions, such as light physical activities, deep breathing exercises, or short breaks, are generated based on individual user behavior. The smart band not only provides continuous, non-invasive mental health monitoring but also learns from user data over time using machine learning to improve prediction accuracy. This approach transforms traditional mental health assessment methods by introducing a proactive, AI-driven, and data- centric system for managing everyday stress and emotional well-being.