Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
Mental health disorders constitute a significant global challenge, compounded by the limitations of traditional management approaches that rely heavily on subjective self-reports and infrequent professional evaluations. This study presents a groundbreaking IoT-based system that integrates big data a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/912 |
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| author | Sanaz Zamani Minh Nguyen Roopak Sinha |
| author_facet | Sanaz Zamani Minh Nguyen Roopak Sinha |
| author_sort | Sanaz Zamani |
| collection | DOAJ |
| description | Mental health disorders constitute a significant global challenge, compounded by the limitations of traditional management approaches that rely heavily on subjective self-reports and infrequent professional evaluations. This study presents a groundbreaking IoT-based system that integrates big data analytics, fuzzy logic, and machine learning to revolutionise mental health monitoring. In contrast to existing solutions, the proposed system uniquely incorporates environmental factors, such as temperature and humidity in enclosed spaces—critical yet often overlooked contributors to emotional well-being. By leveraging IoT devices to collect and process large-scale ambient data, the system provides real-time classification and personalised visualisation tailored to individual sensitivity profiles. Preliminary results reveal high accuracy, scalability, and the potential to generate actionable insights, creating dynamic feedback loops for continuous improvement. This innovative approach bridges the gap between environmental conditions and mental healthcare, promoting a transformative shift from reactive to proactive care and laying the groundwork for predictive environmental health systems. |
| format | Article |
| id | doaj-art-8284c5bde33c474dac57aca5833ba843 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-8284c5bde33c474dac57aca5833ba8432025-01-24T13:21:19ZengMDPI AGApplied Sciences2076-34172025-01-0115291210.3390/app15020912Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based ApproachSanaz Zamani0Minh Nguyen1Roopak Sinha2Department of Computer Science and Software Engineering, Auckland University of Technology, Auckland 1010, New ZealandDepartment of Computer Science and Software Engineering, Auckland University of Technology, Auckland 1010, New ZealandSchool of Information Technology, Deakin University, Burwood, VIC 3125, AustraliaMental health disorders constitute a significant global challenge, compounded by the limitations of traditional management approaches that rely heavily on subjective self-reports and infrequent professional evaluations. This study presents a groundbreaking IoT-based system that integrates big data analytics, fuzzy logic, and machine learning to revolutionise mental health monitoring. In contrast to existing solutions, the proposed system uniquely incorporates environmental factors, such as temperature and humidity in enclosed spaces—critical yet often overlooked contributors to emotional well-being. By leveraging IoT devices to collect and process large-scale ambient data, the system provides real-time classification and personalised visualisation tailored to individual sensitivity profiles. Preliminary results reveal high accuracy, scalability, and the potential to generate actionable insights, creating dynamic feedback loops for continuous improvement. This innovative approach bridges the gap between environmental conditions and mental healthcare, promoting a transformative shift from reactive to proactive care and laying the groundwork for predictive environmental health systems.https://www.mdpi.com/2076-3417/15/2/912mental health monitoringIoTambient data analyticstemperature and humiditybig datafuzzy logic |
| spellingShingle | Sanaz Zamani Minh Nguyen Roopak Sinha Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach Applied Sciences mental health monitoring IoT ambient data analytics temperature and humidity big data fuzzy logic |
| title | Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach |
| title_full | Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach |
| title_fullStr | Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach |
| title_full_unstemmed | Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach |
| title_short | Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach |
| title_sort | integrating environmental data for mental health monitoring a data driven iot based approach |
| topic | mental health monitoring IoT ambient data analytics temperature and humidity big data fuzzy logic |
| url | https://www.mdpi.com/2076-3417/15/2/912 |
| work_keys_str_mv | AT sanazzamani integratingenvironmentaldataformentalhealthmonitoringadatadriveniotbasedapproach AT minhnguyen integratingenvironmentaldataformentalhealthmonitoringadatadriveniotbasedapproach AT roopaksinha integratingenvironmentaldataformentalhealthmonitoringadatadriveniotbasedapproach |