IoTMindCare: An Integrative Reference Architecture for Safe and Personalized IoT-Based Depression Management

Abstract

Depression affects millions of people worldwide. Traditional management relies heavily on periodic clinical assessments and self-reports, which lack real-time responsiveness and personalization. Despite numerous research prototypes exploring Internet of Things (IoT)-based mental health support, almost none have translated into practical, mainstream solutions. This gap stems from several interrelated challenges, including the absence of robust, flexible, and safe architectural frameworks; the diversity of IoT device ownership; the need for further research across many aspects of technology-based depression support; highly individualized user needs; and ongoing concerns regarding safety and personalization. We aim to develop a reference architecture for IoT-based safe and personalized depression management. We introduce IoTMindCare, integrating current best practices while maintaining the flexibility required to incorporate future research and technology innovations. A structured review of contemporary IoT-based solutions for depression management is used to establish their strengths, limitations, and gaps. Then, following the Attribute-Driven Design (ADD) method, we design IoTMindCare. The Architecture Trade-off Analysis Method (ATAM) is used to evaluate the proposed reference architecture. The proposed reference architecture features a modular, layered logical view design with cross-layer interactions, incorporating expert input to define system components, data flows, and user requirements. Personalization features, including continuous, context-aware feedback and safety-related mechanisms, were designed based on the needs of stakeholders, primarily users and caregivers, throughout the system architecture. ATAM evaluation shows that IoTMindCare supports safety and personalization significantly better than current designs. This work provides a flexible, safe, and extensible architectural foundation for IoT-based depression management systems, enabling the construction of optimal solutions that integrate the most effective current research and technology while remaining adaptable to future advancements. IoTMindCare provides a unifying, aggregation-style reference architecture that consolidates design principles and operational lessons from multiple prior IoT mental-health solutions, enabling these systems to be instantiated, compared, and extended rather than directly competing with any single implementation.