Multi-Sensor Wearable Health Device Framework for Real-Time Monitoring of Elderly Patients Using a Mobile Application and High-Resolution Parameter Estimation - Pinheiro, Gabriel P. M.; Miranda, Ricardo K.; Praciano, Bruno J. G.; Santos, Giovanni A.; Mendonça, Fábio L. L.; Javidi, Elnaz; da Costa, João Paulo Javidi; de Sousa, Rafael T. (Frontiers in Human Neuroscience, 2022)
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. 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 device (MWHD), high-resolution parameter estimation and real-time monitoring application. The framework is appropriate for real-time monitoring of elderly patient's health without the need of physical contact with healthcare professionals, maintain 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 measurements of the photoplethysmography (PPG) sensor.