Multi-Sensor Wearable Health Device Framework for Real-Time Monitoring of Elderly Patients Using a Mobile Application and High-Resolution Parameter Estimation

Frontiers in Human Neuroscience

Human Neuroscience

Giovanni A. Santos¹  Fábio L. L. Mendonça¹  Rafael T. de Sousa¹  Bruno J. G. Praciano²  Ricardo K. Miranda²  Elnaz Javidi²  Gabriel P. M. Pinheiro²  João Paulo Javidi da Costa³ 

[1] Department of Electrical Engineering, University of Brasília, Brasília, Brazil;Department of Mechanical Engineering, University of Brasília, Brasília, Brazil;Department of Mechanical Engineering, University of Brasília, Brasília, Brazil;Department of Electrical Engineering, University of Brasília, Brasília, Brazil;Department 2-Campus Lippstadt, Hamm-Lippstadt University of Applied Sciences, Hamm, Germany;

关键词: embedded high-resolution parameter estimation;    healthcare multi-sensor wearable hardware development;    health monitoring application architecture;    ESPRIT;    photoplethysmography;   

DOI  :  10.3389/fnhum.2021.750591

 received in 2021-07-30, accepted in 2021-12-22,  发布年份 2022

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2022 Pinheiro, Miranda, Praciano, Santos, Mendonça, Javidi, da Costa and de Sousa.