IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being

The paper explores the potential of IoT-based digital technologies, particularly Digital Twins (DT) and Knowledge Graphs, in promoting mental health and achieving the United Nations' Sustainable Development Goal 3 (Good Health and Well-Being). Digital Twins, supported by standardized ontologies like ETSI, ITU/WHO, and W3C, enable early detection of mental health issues like burnout and depression through continuous emotion monitoring. The Mental Health Knowledge Graph integrates diverse data sources and aims to enhance personalized care by improving data exchange and understanding. The focus is on leveraging semantic web technologies to manage mental health more effectively, with applications in the US and the EU. The text highlights the use of AI and IoT in mental health, with a focus on self-care, stress management, and the integration of wearable sensors. It discusses the importance of addressing mental health challenges, including the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) and the need for positive psychology. The authors present a vision for using Digital Twins in various aspects of mental well-being, while also addressing challenges and standardization efforts. Research on IoT devices for mental health monitoring employs machine learning algorithms and various sensors, with a mention of the need for ontologies and knowledge graphs. The study also covers the role of Web Technologies, Semantic Web standards, and the importance of collaboration among industry, governments, and standards organizations. The text concludes by emphasizing the potential of AI and IoT in mental health, but also points out the gaps and ongoing efforts to standardize and improve the field, particularly for mental health-specific standards.