AI enabled RPM for Mental Health Facility

Shaik, Thanveer ORCID: https://orcid.org/0000-0002-9730-665X and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Higgins, Niall and Xie, Haoran and Gururajan, Raj ORCID: https://orcid.org/0000-0002-5919-0174 and Zhou, Xujuan (2022) AI enabled RPM for Mental Health Facility. In: 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare (WMSSH 2022), 21 Oct 2022, 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.


Statistics for USQ ePrint 51393
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 19 Oct 2022 00:05
Last Modified: 02 Nov 2022 02:13
Uncontrolled Keywords: RPM, AI, neural networks, mental health monitoring
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460102 Applications in health
Socio-Economic Objectives (2020): 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Identification Number or DOI: https://doi.org/10.1145/3556551.3561191
URI: http://eprints.usq.edu.au/id/eprint/51393

Actions (login required)

View Item Archive Repository Staff Only