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.
|Title of host publication||MWSSH 2022 - Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||7|
|Publication status||Published - 21 Oct 2022|
|Event||The 28th Annual International Conference on Mobile Computing and Networking - Sydney, Australia|
Duration: 21 Oct 2022 → …
|Name||MWSSH 2022 - Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare|
|Conference||The 28th Annual International Conference on Mobile Computing and Networking|
|Abbreviated title||ACM MobiCom '22|
|Period||21/10/22 → …|
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- mental health monitoring
- neural networks