AI enabled RPM for Mental Health Facility
Paper
Paper/Presentation Title | AI enabled RPM for Mental Health Facility |
---|---|
Presentation Type | Paper |
Authors | Shaik, Thanveer (Author), Tao, Xiaohui (Author), Higgins, Niall (Author), Xie, Haoran (Author), Gururajan, Raj (Author) and Zhou, Xujuan (Author) |
Journal or Proceedings Title | Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare (WMSSH 2022) |
Journal Citation | pp. 26-32 |
Number of Pages | 7 |
Year | 2022 |
Place of Publication | New York, United States |
ISBN | 9781450395205 |
Digital Object Identifier (DOI) | https://doi.org/10.1145/3556551.3561191 |
Web Address (URL) of Paper | https://dl.acm.org/doi/10.1145/3556551.3561191 |
Conference/Event | 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare (WMSSH 2022) |
Event Details | 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare (WMSSH 2022) Event Date 21 Oct 2022 Event Location Sydney, Australia |
Abstract | Mental healthcare is one of the prominent parts of the healthcare industry with alarming concerns related to patients' depression, stress leading to self-harm and threat to fellow patients and medical staff. To provide a therapeutic environment for both patients and staff, aggressive or agitated patients need to be monitored remotely and track their vital signs and physical activities continuously. Remote patient monitoring (RPM) using non-invasive technology could enable contactless monitoring of acutely ill patients in a mental health facility. Enabling the RPM system with AI unlocks a predictive environment in which future vital signs of the patients can be forecasted. This paper discusses an AI-enabled RPM system framework with a non-invasive digital technology RFID using its in-built NCS mechanism to retrieve vital signs and physical actions of patients. Based on the retrieved time series data, future vital signs of patients for the upcoming 3 hours and classify their physical actions into 10 labelled physical activities. This framework assists to avoid any unforeseen clinical disasters and take precautionary measures with medical intervention at right time. A case study of a middle-aged PTSD patient treated with the AI-enabled RPM system is demonstrated in this study. |
Keywords | RPM, AI, neural networks, mental health monitoring |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
460102. Applications in health | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | Academic Quality Unit |
School of Mathematics, Physics and Computing | |
Academic Transformation Portfolio | |
Enterprise Information, Data and Analytics | |
University of Southern Queensland | |
Department of Health, Queensland | |
Lingnan University of Hong Kong, China |
https://research.usq.edu.au/item/q7vv0/ai-enabled-rpm-for-mental-health-facility
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