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

Gabriel P M Pinheiro; Ricardo K Miranda; Bruno J G Praciano; Giovanni A Santos; Fábio L L Mendonça; Elnaz Javidi; João Paulo Javidi da Costa; Rafael T de Sousa Jr

doi:10.3389/fnhum.2021.750591

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

Front Hum Neurosci. 2022 Jan 17:15:750591. doi: 10.3389/fnhum.2021.750591. eCollection 2021.

Authors

Gabriel P M Pinheiro¹, Ricardo K Miranda¹, Bruno J G Praciano¹, Giovanni A Santos², Fábio L L Mendonça², Elnaz Javidi¹, João Paulo Javidi da Costa^{1

2

3}, Rafael T de Sousa Jr²

Affiliations

¹ Department of Mechanical Engineering, University of Brasília, Brasília, Brazil.
² Department of Electrical Engineering, University of Brasília, Brasília, Brazil.
³ Department 2-Campus Lippstadt, Hamm-Lippstadt University of Applied Sciences, Hamm, Germany.

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.

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