ESPRIT-Based Step Count for Wearable Devices

Rega, Danilo G.; Miranda, Ricardo Kehrle; Javidi, Elnaz; Maranhão, João Paulo A.; Paulo, C L da Costa Joao; Gabriel, P M Pinheiro

doi:10.1109/icspcs47537.2019.9008702

Cited by 1 publication

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The current paper focuses on high-resolution HR measurements. Additionally, an application of step counting is shown in Rega et al ( 2019 ). We also implemented the other parameters as directed by the manufacturer's datasheet of the MAX30102 pulse oximetry sensor from Maxim Integrated ( 2018 ).…”

Section: Proposed Healthcare Platform Composed Of Low-cost Hardware High-resolution Parameter Estimation Algorithms and Real-time Monitormentioning

confidence: 99%

“…In Reis et al ( 2017 ), the high-resolution signal processing technique named SPHINS is successfully applied for the frequency estimation in forensic applications. In Rega et al ( 2019 ), a high accuracy step counter algorithm based on ESPRIT has been proposed using the accelerometer signals acquired by the sensors of our MWHD prototype proposed in section 3.1. 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 HR.…”

Section: Proposed Healthcare Platform Composed Of Low-cost Hardware High-resolution Parameter Estimation Algorithms and Real-time Monitormentioning

confidence: 99%

See 1 more Smart Citation

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

Pinheiro

Miranda

Praciano

et al. 2022

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Proposed Healthcare Platform Composed Of Low-cost Hardware High-resolution Parameter Estimation Algorithms and Real-time Monitormentioning

confidence: 99%

Section: Proposed Healthcare Platform Composed Of Low-cost Hardware High-resolution Parameter Estimation Algorithms and Real-time Monitormentioning

confidence: 99%