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Measurement of heart rate variability using off-the-shelf smart phones

Overview of attention for article published in BioMedical Engineering OnLine, January 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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11 X users
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1 Facebook page

Citations

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53 Dimensions

Readers on

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121 Mendeley
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1 CiteULike
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Title
Measurement of heart rate variability using off-the-shelf smart phones
Published in
BioMedical Engineering OnLine, January 2016
DOI 10.1186/s12938-016-0127-8
Pubmed ID
Authors

Ren-You Huang, Lan-Rong Dung

Abstract

The cardiac parameters, such as heart rate (HR) and heart rate variability (HRV), are very important physiological data for daily healthcare. Recently, the camera-based photoplethysmography techniques have been proposed for HR measurement. These techniques allow us to estimate the HR contactlessly with low-cost camera. However, the previous works showed limit success for estimating HRV because the R-R intervals, the primary data for HRV calculation, are sensitive to noise and artifacts. This paper proposed a non-contact method to extract the blood volume pulse signal using a chrominance-based method followed by a proposed CWT-based denoising technique. The R-R intervals can then be obtained by finding the peaks in the denoised signal. In this paper, we taped 12 video clips using the frontal camera of a smart phone with different scenarios to make comparisons among our method and the other alternatives using the absolute errors between the estimated HRV metrics and the ones obtained by an ECG-accurate chest band. As shown in experiments, our algorithm can greatly reduce absolute errors of HRV metrics comparing with the related works using RGB color signals. The mean of absolute errors of HRV metrics from our method is only 3.53 ms for the static-subject video clips. The proposed camera-based method is able to produce reliable HRV metrics which are close to the ones measured by contact devices under different conditions. Thus, our method can be used for remote health monitoring in a convenient and comfortable way.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Italy 1 <1%
Germany 1 <1%
Korea, Republic of 1 <1%
Unknown 117 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 25 21%
Student > Ph. D. Student 20 17%
Researcher 16 13%
Student > Bachelor 12 10%
Other 8 7%
Other 17 14%
Unknown 23 19%
Readers by discipline Count As %
Engineering 33 27%
Computer Science 16 13%
Medicine and Dentistry 14 12%
Psychology 7 6%
Sports and Recreations 6 5%
Other 16 13%
Unknown 29 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 November 2016.
All research outputs
#4,754,411
of 25,371,288 outputs
Outputs from BioMedical Engineering OnLine
#103
of 867 outputs
Outputs of similar age
#75,821
of 405,197 outputs
Outputs of similar age from BioMedical Engineering OnLine
#2
of 18 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 867 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 88% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 405,197 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.