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

• URL: http://arxiv.org/abs/2406.13791v3
• Date: Mon, 21 Oct 2024 16:55:31 GMT
• Title: IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being
• Authors: Amelie Gyrard, Seyedali Mohammadi, Manas Gaur, Antonio Kung,
• Abstract summary: Digital technologies can support Sustainable Development Goals 3. "Good Health and Well-Being" ensures healthy lives and promotes well-being for all ages. burnout and depression could be reduced by encouraging better preventive health.
• Score: 8.437366120438156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sustainable Development Goals (SDGs) give the UN a road map for development with Agenda 2030 as a target. SDG3 "Good Health and Well-Being" ensures healthy lives and promotes well-being for all ages. Digital technologies can support SDG3. Burnout and even depression could be reduced by encouraging better preventive health. Due to the lack of patient knowledge and focus to take care of their health, it is necessary to help patients before it is too late. New trends such as positive psychology and mindfulness are highly encouraged in the USA. Digital Twins (DTs) can help with the continuous monitoring of emotion using physiological signals (e.g., collected via wearables). DTs facilitate monitoring and provide constant health insight to improve quality of life and well-being with better personalization. Healthcare DTs challenges are standardizing data formats, communication protocols, and data exchange mechanisms. As an example, ISO has the ISO/IEC JTC 1/SC 41 Internet of Things (IoT) and DTs Working Group, with standards such as "ISO/IEC 21823-3:2021 IoT - Interoperability for IoT Systems - Part 3 Semantic interoperability", "ISO/IEC CD 30178 - IoT - Data format, value and coding". To achieve those data integration and knowledge challenges, we designed the Mental Health Knowledge Graph (ontology and dataset) to boost mental health. As an example, explicit knowledge is described such as chocolate contains magnesium which is recommended for depression. The Knowledge Graph (KG) acquires knowledge from ontology-based mental health projects classified within the LOV4IoT ontology catalog (Emotion, Depression, and Mental Health). Furthermore, the KG is mapped to standards when possible. Standards from ETSI SmartM2M can be used such as SAREF4EHAW to represent medical devices and sensors, but also ITU/WHO, ISO, W3C, NIST, and IEEE standards relevant to mental health can be considered.

Related papers

• Personal Health Data Integration and Intelligence through Semantic Web and Blockchain Technologies [1.029029220360574]
  Data integration among various stakeholders in the healthcare space remains a challenge.<n>There is a lot of messy'' non-standard but structured data that are continually being collected from personal health devices.<n>This paper presents a solution to bridge this gap using a system that implements semantic web and blockchain technologies.
  arXiv  Detail & Related papers  (2026-03-02T18:53:42Z)
• Position: AI Will Transform Neuropsychology Through Mental Health Digital Twins for Dynamic Mental Health Care, Especially for ADHD [5.844783557050257]
  We advocate a shift from static mental health diagnostic assessments to continuous, artificial intelligence (AI)-driven assessment.<n>We focus on Attention-Deficit/Hyperactivity Disorder (ADHD) as a case study.<n>We propose the use of mental health digital twins (MHDTs) as a transformative framework for personalized mental health care.
  arXiv  Detail & Related papers  (2025-10-08T18:06:15Z)
• A Comprehensive Review of Datasets for Clinical Mental Health AI Systems [55.67299586253951]
  We present the first comprehensive survey of clinical mental health datasets relevant to the training and development of AI-powered clinical assistants.<n>Our survey identifies critical gaps such as a lack of longitudinal data, limited cultural and linguistic representation, inconsistent collection and annotation standards, and a lack of modalities in synthetic data.
  arXiv  Detail & Related papers  (2025-08-13T13:42:35Z)
• EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding [71.31963197992998]
  We introduce a large-scale, code-based competition comprising two challenges.<n>The Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data.<n>The Psychopathology factor prediction Challenge asks participants to infer subject measures of mental health from EEG data.
  arXiv  Detail & Related papers  (2025-06-23T21:25:19Z)
• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder. Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
  Mental health disorders are one of the most serious diseases in the world. Privacy concerns limit the accessibility of personalized treatment data. MentalArena is a self-play framework to train language models.
  arXiv  Detail & Related papers  (2024-10-09T13:06:40Z)
• Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation [0.0]
  This paper proposes a structured framework that delineates levels of autonomy, outlines ethical requirements, and defines beneficial default behaviors for AI agents. We also evaluate 14 state-of-the-art language models (ten off-the-shelf, four fine-tuned) using 16 mental health-related questionnaires.
  arXiv  Detail & Related papers  (2024-04-02T15:05:06Z)
• Proactive Emotion Tracker: AI-Driven Continuous Mood and Emotion Monitoring [2.271910267215261]
  The project aims to tackle the growing mental health challenges in today's digital age. It employs a modified pre-trained BERT model to detect depressive text within social media and users' web browsing data, achieving an impressive 93% test accuracy.
  arXiv  Detail & Related papers  (2024-01-24T15:05:11Z)
• Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey [53.691704671844406]
  The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare. The human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body. HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed. Recently, generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data.
  arXiv  Detail & Related papers  (2024-01-22T03:17:41Z)
• A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC [54.74071593520785]
  Mobile AIGC can be a key enabling technology for an emerging application, called human digital twin (HDT) HDT empowered by the mobile AIGC is expected to revolutionize the personalized healthcare by generating rare disease data, modeling high-fidelity digital twin, building versatile testbeds, and providing 24/7 customized medical services.
  arXiv  Detail & Related papers  (2023-07-22T15:59:03Z)
• A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises [52.31710895034573]
  This work provides the first comprehensive review of healthcare knowledge graphs (HKGs) It summarizes the pipeline and key techniques for HKG construction, as well as the common utilization approaches. At the application level, we delve into the successful integration of HKGs across various health domains.
  arXiv  Detail & Related papers  (2023-06-07T21:51:56Z)
• Reliable and Resilient AI and IoT-based Personalised Healthcare Services: A Survey [1.581123237785583]
  This paper conducts a comprehensive survey on personalized healthcare services. We first present an overview of key requirements of comprehensive personalized healthcare services in modern healthcare Internet of Things (HIoT) Second, we explored a fundamental three-layer architecture for IoT-based healthcare systems using AI and non-AI-based approaches. Third, we highlighted different security threats against each layer of IoT architecture along with the possible AI and non-AI-based solutions.
  arXiv  Detail & Related papers  (2022-08-29T23:14:02Z)
• Internet of Things (IoT) based ECG System for Rural Health Care [0.33249867230903685]
  Nearly 30% of the people in the rural areas of Bangladesh are in poverty level. modernized technology, nursing and diagnosis facilities are limited for rural people. The proposed IoT-based ECG system reduces the health care cost and complexity of cardiovascular diseases in the future.
  arXiv  Detail & Related papers  (2022-07-27T02:56:36Z)
• Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes [51.378225388679425]
  We present an end-to-end multitask system to address a novel task of detection of Perceived Burdensomeness (PB) and Thwarted Belongingness (TB) from suicide notes. We also introduce a manually translated code-mixed suicide notes corpus, CoMCEASE-v2.0, based on the benchmark CEASE-v2.0 dataset. We exploit the temporal orientation and emotion information in the suicide notes to boost overall performance.
  arXiv  Detail & Related papers  (2022-05-20T06:31:08Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.