The readers had multiple read capacity with 10 MB memory and a range of 3 to 85 metres. Field generators were used in conjunction with tag readers to help contain costs. This innovative project included a purpose specific active RFID tag that was developed to monitor patient temperature. The authors described the development strategy and architecture design, and highlighted the importance of support from clinical staff for successful implementation of an RPM system in a hospital. described a case study demonstrating how an RPM system using RFID technology was implemented hospital-wide in the Taipei Medical University Hospital around 14 years ago amid the SARS outbreak. This study contributes towards better design of a remote patient monitoring (RPM) system with noninvasive technology, specifically: Furthermore, an Ensemble Learning model was developed, capitalising on the performance advantages of these machine learning algorithms. As a result, Decision Tree appeared to have the best performance compared with Random Forest and XGBoost. Label prediction was completed using various machine learning algorithms and derived their error metrics, aiming to discover the patterns between the positions of reader–antenna and RSSI readings. A linear equation was developed with RSSI as output variable and Distance_1, Distance_2, Antennas_ Distance as independent variables with their respective coefficients. The linear relationship between dependent and independent variables was derived with their coefficients and an intercept with using an ordinary least squares (OLS) linear regression method. Specifically, we compared combinations of multiple positions of reader–antennas based on received signal strength indicators (RSSI) and from this derived better positions to fix the reader–antenna devices. We collected and analysed data from multiple reader–antenna positions in the laboratory. In this study, a laboratory was established, simulating a hospital psychiatric ward using an RPM system utilising sensors and RFID technology. Identifying the optimum configuration of sensors using RFID technology in a simulated hospital ward has become an important step toward this goal. Readings for each individual will be tracked and differentiated using noninvasive monitoring techniques. The ultimate goal of this research is to detect accurate vital signs of multiple patients through an RPM system. There are formidable challenges with integrating this type of technology into a clinical setting such as where best to deploy equipment for it not to interfere with routine clinical care. However, little is known about how these methods can be used to monitor vital signs of several patients simultaneously who are also mobile. Recent innovations have employed this technology to detect vital signs without making contact with the patient using techniques such as near-field coherent sensing of signals generated from the antennae of the RFID tag. However, traditional RPM systems are intrusive and need dedicated sensors to be attached and can cause considerable inconvenience in psychiatric clinical care. These techniques can also be used for direct patient care by providing alerts to indicate potential human errors in managing patients such as misidentification of patients and medication. There are known benefits to using radio frequency identification (RFID) technology in healthcare asset tracking and predicting future events with machine learning tools by analysing RFID tag data. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics.
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