Why the study?
Protecting elderly patients became imperative during the COVID-19 pandemic, but state-of-the-art wearable device platforms face limitations in hardware, parameter estimation algorithms, and software architecture.
Does a multi-sensor wearable health device framework with high-resolution parameter estimation improve heart rate measurement accuracy compared to state-of-the-art PPG methods?
Does a multi-sensor wearable health device framework with high-resolution parameter estimation improve heart rate measurement accuracy compared to state-of-the-art PPG methods?
A novel multi-sensor wearable health device framework utilizing high-resolution signal processing improves heart rate estimation accuracy compared to existing PPG methods.
Prototype framework may aid remote elderly monitoring; leaves open clinical validation before practice change.
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.
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Pinheiro et al. (2022) studied this question.
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