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Remote Health Monitoring System

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 4941

Special Issue Editor


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Guest Editor
Centre for Informatics and Systems of the University of Coimbra (CISUC), Department of Informatics Engineering, University of Coimbra, 3030-790 Coimbra, Portugal
Interests: data science; computational intelligence; intelligent systems; fault diagnosis; prediction and decision
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Special Issue Information

Dear Colleagues,

The use of remote monitoring systems plays a decisive role in the deployment of eHealth solutions. The use of innovative sensors, reliable telecommunications infrastructures, and advanced diagnosis methodologies allows for healthcare to be extended outside the hospital, supporting healthcare services in a more proactive manner. In practice, permanent monitoring of the patients, continuous assessment of physiological parameters, prompt diagnosis, and early prediction of possible critical events are possible.

This Special Issue will focus on recent developments in the field of personal remote monitoring systems (pHealth), from non-obtrusive methods for acquisition of bio-signals and physiological parameters, to advances in bio-signal analysis and data processing algorithms, to the implementation and the identification of innovative pHealth solutions.

Possible contributions may include continuous easy-to-use bio-signals measurement; intelligent techniques and advanced algorithms to process and analyze collected data, in particular fusion methodologies that are able to deal with dynamic interactions and different sources of information (e.g., bio-signals, vital parameters, and contextual information); and new applications and trends of monitoring, diagnosis, prediction, and decision-making in the field of pHealth.

Dr. Jorge Henriques
Guest Editor

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Keywords

  • pHealth applications
  • Non-obtrusive sensors
  • Bio-signal processing
  • Diagnosis and prognosis
  • Clinical decision support systems
  • Computational intelligence
  • Data mining
  • Chronic disease managment

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Published Papers (1 paper)

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Research

15 pages, 1305 KiB  
Article
Video-Based Pulse Rate Variability Measurement Using Periodic Variance Maximization and Adaptive Two-Window Peak Detection
by Peixi Li, Yannick Benezeth, Richard Macwan, Keisuke Nakamura, Randy Gomez, Chao Li and Fan Yang
Sensors 2020, 20(10), 2752; https://doi.org/10.3390/s20102752 - 12 May 2020
Cited by 10 | Viewed by 4413
Abstract
Many previous studies have shown that the remote photoplethysmography (rPPG) can measure the Heart Rate (HR) signal with very high accuracy. The remote measurement of the Pulse Rate Variability (PRV) signal is also possible, but this is much more complicated because it is [...] Read more.
Many previous studies have shown that the remote photoplethysmography (rPPG) can measure the Heart Rate (HR) signal with very high accuracy. The remote measurement of the Pulse Rate Variability (PRV) signal is also possible, but this is much more complicated because it is then necessary to detect the peaks on the temporal rPPG signal, which is usually quite noisy and has a lower temporal resolution than PPG signals obtained by contact equipment. Since the PRV signal is vital for various applications such as remote recognition of stress and emotion, the improvement of PRV measurement by rPPG is a critical task. Contact based PRV measurement has already been investigated, but the research on remotely measured PRV is very limited. In this paper, we propose to use the Periodic Variance Maximization (PVM) method to extract the rPPG signal and event-related Two-Window algorithm to improve the peak detection for PRV measurement. We have made several contributions. Firstly, we show that the newly proposed PVM method and Two-Window algorithm can be used for PRV measurement in the non-contact scenario. Secondly, we propose a method to adaptively determine the parameters of the Two-Window method. Thirdly, we compare the algorithm with other attempts for improving the non-contact PRV measurement such as the Slope Sum Function (SSF) method and the Local Maximum method. We calculated several features and compared the accuracy based on the ground truth provided by contact equipment. Our experiments showed that this algorithm performed the best of all the algorithms. Full article
(This article belongs to the Special Issue Remote Health Monitoring System)
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