Emotion-Aware Wearable Health Monitoring System Using ML-Based Sentiment Inference

The integration of wearable technology and machine learning has significantly advanced continuous health monitoring systems. However, most existing wearable solutions focus primarily on physiological parameters while neglecting emotional and mental health factors that strongly influence overall well-being. This paper proposes an Emotion-Aware Wearable Health Monitoring System that integrates physiological sensor data with machine learning–based sentiment inference to provide comprehensive health insights. The system collects real-time data from heart rate, skin temperature, galvanic skin response (GSR), and accelerometer sensors, along with contextual inputs such as speech or text. A multi-modal machine learning framework analyzes physiological and sentiment features to detect emotional states including stress, anxiety, fatigue, and calmness. The inferred emotional states are correlated with physical health parameters to generate personalized recommendations and real-time alerts via a cloud-based dashboard. Experimental evaluation using collected sensor data demonstrates the feasibility of emotion-aware health monitoring for preventive and personalized healthcare applications.