Abstract
Automatized scalable healthcare support solutions allow real-time 24/7 health monitoring of patients, prioritizing medical treatment according to health conditions, reducing medical appointments in clinics and hospitals, and enabling easy exchange of information among healthcare professionals. With recent health safety guidelines due to the COVID-19 pandemic, protecting the elderly has become imperative. However, state-of-the-art health wearable device platforms present limitations in hardware, parameter estimation algorithms, and software architecture. This paper proposes a complete framework for health systems composed of multi-sensor wearable health devices (MWHD), high-resolution parameter estimation, and real-time monitoring applications. The framework is appropriate for real-time monitoring of elderly patients' health without physical contact with healthcare professionals, maintaining safety standards. The hardware includes sensors for monitoring steps, pulse oximetry, heart rate (HR), and temperature using low-power wireless communication. In terms of parameter estimation, the embedded circuit uses high-resolution signal processing algorithms that result in an improved measure of the HR. The proposed high-resolution signal processing-based approach outperforms state-of-the-art HR estimation measurements using the photoplethysmography (PPG) sensor.
Highlights
Nowadays, health systems, including hospitals and their intensive care units (ICU), are challenged by a substantial need to increase critical care capacity due to the Coronavirus Disease 2019 (COVID-19) pandemic (Phua et al, 2020)
The authors show that fewer elderly people are using wearable devices, while more than 60% of them were interested in using such devices
Inspired by the outstanding results of Reis et al (2017), Reis et al (2016), and Rega et al (2019), we propose the usage of the high-resolution signal processing algorithm ESPRIT to measure the heart rate (HR)
Summary
Health systems, including hospitals and their intensive care units (ICU), are challenged by a substantial need to increase critical care capacity due to the Coronavirus Disease 2019 (COVID-19) pandemic (Phua et al, 2020). As suggested in Alwashmi (2020), health systems invest in automatized and scalable digital health support solutions, such as healthcare wearable devices and information systems empowered with artificial intelligence Such automatized digital health solutions allow real-time 24/7 health monitoring of patients, prioritizing medical treatment according to the patients’ health conditions, reducing medical appointments in clinics and hospitals, by sharing secure information among healthcare professionals. This is especially of interest when treating the elderly, as they are at greater risk in hospital environments as concluded in Costantino et al (2021). We propose a multi-sensor wearable health device (MWHD) framework with a real-time monitoring application and high-resolution parameter estimation.
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