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Smart Vital Signs Monitoring and Novel Falls Prediction System for older adults
Baig, Mirza Mansoor
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Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way healthcare is currently delivered. Smart monitoring systems automate patient monitoring tasks and thereby improve the patient workflow management. Moreover, expert systems have the potential to improve clinicians’ performance by accurately executing repetitive tasks, to which humans are ill-suited. Clinicians working in hospital wards are responsible for conducting a multitude of tasks which require constant vigilance and thus the need for a smart decision support system has arisen. In particular, wireless patient monitoring systems are emerging as a low cost, reliable and accurate means of healthcare delivery. This study focuses on three important areas of healthcare: wireless, remote and real-time vital signs monitoring, interpreting multiple physical signs and falls detection and prediction for hospitalised older adults. Vital signs monitoring systems are rapidly becoming the core of today’s healthcare deliveries. The paradigm has shifted from traditional and manual recording to computer based electronic records and further to handheld devices as versatile and innovative healthcare monitoring systems. The system proposed in this thesis aims to aid in the diagnosis of patients’ health conditions from the collected vital signs and assist clinicians with the interpretation of multiple physical signs. Data from a total of 30 patients have been collected in New Zealand Hospitals under local and national ethics approvals. The system records blood pressure, heart rate (pulse), oxygen saturation (SpO2), ear temperature and blood glucose levels from hospitalised patients and transfers this information to a web-based software application for remote monitoring and further interpretation. Ultimately, this system achieved a high level of agreement with clinicians’ interpretation when assessing specific physical signs such as bradycardia, tachycardia, hypertension, hypotension, hypoaxemia, fever and hypothermia, and was able to generate early warnings. The performance of the vital signs interpretation system was validated through off-line as well as real-time tests with a high level of agreement between the system and the physician. Another aim of this study was to develop a robust falls detection as well as falls risk prediction system. The proposed system employs real-time vital signs, motion data, falls history and other clinical information, which is a valuable tool for hospital falls prevention. The falls risk prediction model has been tested and evaluated with 30 patients using the hospital’s falls scoring scale.