MiCare: An IoT-Based System for Real-Time Mental Health Monitoring and Early Disease Detection

Mental health disorders, particularly among young adults, are a growing concern, requiring innovative solutions for effective diagnosis and treatment, based on a solid data management. MiCare, an AI-driven technological platform, aims to revolutionise mental healthcare through personalised patient care management, continuous remote monitoring, and early detection of abnormalities. Integrating wearable devices, patient records, and electronic health records, the platform features a Bayesian Network-based Clinical Decision Support System (Clinical Decision Support System (CDSS)) that leverages heterogeneous data to assist healthcare professionals with data-driven insights while ensuring transparency, explainability, and responsible data management. A centralised Signal Processing component processes physiological signals such as Photoplethysmographic (PPG) and Galvanic Skin Response (GSR), transforming real-time sensor data into features that serve as digital mental health biomarkers. These are combined with psychodiagnostic tools and patient diaries collected through the Mobile App, as well as clinician inputs via the Dashboard, constituting a comprehensive database for personalised therapeutic support. Key innovations include broader coverage of mental health disorders, integration of physiological data with traditional psychological measures, and predictive analytics for early intervention. MiCare supports remote, cost-effective therapy, empowering clinicians with actionable insights also via informative data visualisations, and patients with an engaging, gamified approach. This paper highlights MiCare 's potential to enhance mental health diagnosis, monitoring, and treatment, leveraging data integration to foster a paradigm shift towards data-driven, patient-centred mental healthcare.