AI enabled RPM for Mental Health Facility

TL;DR

This paper presents an AI-enabled remote patient monitoring system using RFID technology to predict future vital signs and classify physical activities of mental health patients, enhancing safety and timely intervention.

Contribution

It introduces a novel AI-powered RPM framework with RFID technology for non-invasive, contactless monitoring and prediction of mental health patients' vital signs and activities.

Findings

Accurately predicts vital signs for the next 3 hours.

Classifies physical activities into 10 categories.

Demonstrates effectiveness with a PTSD patient case study.

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.