MindSense: An Intelligent IoT-Based Mental Health Monitoring Framework Using Multi-Modal Sensor Fusion and Predictive Analytics

Millions of individuals have mental diseases, and it is hard to diagnose and watch them continuously because of the low access to healthcare facilities and self-reporting. The conventional evaluation instruments rely on episodic interviews and questionnaires, which do not reflect the adoption of real-time modifications. In this paper, introduce MindSense, a smart IoT-based mental health monitoring system that is able to provide sustained, objective information with multimodal sensor fusion. The wearable biosensors, environmental sensors and behavioral analysis are integrated in the framework to identify external signs of depression, stress, and anxiety. MindSense is a hybrid ensemble deep learning model that analyzes physiological signals, environmental condition and pattern of activities. It is tested in the real-world with a 100-percent classification accuracy, and it is the best among SVM, KNN, and Naive Bayes by up to 13.5% with a low training time of 8.93 seconds and perfect precision, recall, and F1-scores. The analysis of the feature importance identifies stress level, air quality index, and temperature as important factors. The predictive nature of the system in terms of real-time facilitates proactive intervention, personalised recommendations, and early warning to healthcare workers. MindSense bridges the divide between affordable and continuous monitoring of mental health and has great prospects of utilization in the home, workplace, and healthcare environments.