Remote heart rate extraction using Microsoft Kinect™ v2.0

Lakmini Malasinghe; Stamos Katsigiannis; Naeem Ramzan; Keshav Dahal

doi:10.1145/3232059.3232060

Remote heart rate extraction using Microsoft Kinect™ v2.0

Lakmini Malasinghe
, Stamos Katsigiannis
, Naeem Ramzan
, Keshav Dahal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

222 Downloads (Pure)

Abstract

Remote and contactless heart rate detection is still an open research issue of great clinical importance. Available approaches lack the necessary accuracy and reliability for acceptance by medical experts. In this study, we propose a new method for remote heart rate extraction using the Microsoft Kinect™ v2.0 image sensor. The proposed approach relies on signal processing and machine learning methods in order to create a model for accurate estimation of the heart rate via RGB and infrared face videos. Electrocardiography (ECG) recordings and RGB and infrared face videos, captured using the Kinect™ v2.0 image sensor, were acquired from 17 subjects and used to create a machine learning model for remote heart rate detection. Experimental evaluation through supervised regression experiments showed that the proposed approach achieved a mean absolute error of 6.972 bpm, demonstrating the capabilities of the underlying technology.

Original language	English
Title of host publication	Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018)
Publisher	Association for Computing Machinery
Pages	1-6
Number of pages	6
ISBN (Print)	9781450363662
DOIs	https://doi.org/10.1145/3232059.3232060
Publication status	Published - 16 May 2018
Event	10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018) - Amsterdam, Netherlands Duration: 16 May 2018 → 18 May 2018 http://www.icbbt.org/

Conference

Conference	10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018)
Country/Territory	Netherlands
City	Amsterdam
Period	16/05/18 → 18/05/18
Internet address	http://www.icbbt.org/

Access to Document

10.1145/3232059.3232060

2018 01 10 Malasinghe et al Remote acceptedAccepted author manuscript, 568 KB

Cite this

@inproceedings{42ce532a17e3415a89af6efc445c5506,

title = "Remote heart rate extraction using Microsoft Kinect{\texttrademark} v2.0",

abstract = "Remote and contactless heart rate detection is still an open research issue of great clinical importance. Available approaches lack the necessary accuracy and reliability for acceptance by medical experts. In this study, we propose a new method for remote heart rate extraction using the Microsoft Kinect{\texttrademark} v2.0 image sensor. The proposed approach relies on signal processing and machine learning methods in order to create a model for accurate estimation of the heart rate via RGB and infrared face videos. Electrocardiography (ECG) recordings and RGB and infrared face videos, captured using the Kinect{\texttrademark} v2.0 image sensor, were acquired from 17 subjects and used to create a machine learning model for remote heart rate detection. Experimental evaluation through supervised regression experiments showed that the proposed approach achieved a mean absolute error of 6.972 bpm, demonstrating the capabilities of the underlying technology.",

author = "Lakmini Malasinghe and Stamos Katsigiannis and Naeem Ramzan and Keshav Dahal",

year = "2018",

month = may,

day = "16",

doi = "10.1145/3232059.3232060",

language = "English",

isbn = "9781450363662",

pages = "1--6",

booktitle = "Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018)",

publisher = "Association for Computing Machinery",

address = "United States",

note = "10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018) ; Conference date: 16-05-2018 Through 18-05-2018",

url = "http://www.icbbt.org/",

}

Malasinghe, L, Katsigiannis, S, Ramzan, N & Dahal, K 2018, Remote heart rate extraction using Microsoft Kinect™ v2.0. in Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018). Association for Computing Machinery, pp. 1-6, 10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018), Amsterdam, Netherlands, 16/05/18. https://doi.org/10.1145/3232059.3232060

Remote heart rate extraction using Microsoft Kinect™ v2.0. / Malasinghe, Lakmini; Katsigiannis, Stamos; Ramzan, Naeem et al.
Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018). Association for Computing Machinery, 2018. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Remote heart rate extraction using Microsoft Kinect™ v2.0

AU - Malasinghe, Lakmini

AU - Katsigiannis, Stamos

AU - Ramzan, Naeem

AU - Dahal, Keshav

PY - 2018/5/16

Y1 - 2018/5/16

N2 - Remote and contactless heart rate detection is still an open research issue of great clinical importance. Available approaches lack the necessary accuracy and reliability for acceptance by medical experts. In this study, we propose a new method for remote heart rate extraction using the Microsoft Kinect™ v2.0 image sensor. The proposed approach relies on signal processing and machine learning methods in order to create a model for accurate estimation of the heart rate via RGB and infrared face videos. Electrocardiography (ECG) recordings and RGB and infrared face videos, captured using the Kinect™ v2.0 image sensor, were acquired from 17 subjects and used to create a machine learning model for remote heart rate detection. Experimental evaluation through supervised regression experiments showed that the proposed approach achieved a mean absolute error of 6.972 bpm, demonstrating the capabilities of the underlying technology.

AB - Remote and contactless heart rate detection is still an open research issue of great clinical importance. Available approaches lack the necessary accuracy and reliability for acceptance by medical experts. In this study, we propose a new method for remote heart rate extraction using the Microsoft Kinect™ v2.0 image sensor. The proposed approach relies on signal processing and machine learning methods in order to create a model for accurate estimation of the heart rate via RGB and infrared face videos. Electrocardiography (ECG) recordings and RGB and infrared face videos, captured using the Kinect™ v2.0 image sensor, were acquired from 17 subjects and used to create a machine learning model for remote heart rate detection. Experimental evaluation through supervised regression experiments showed that the proposed approach achieved a mean absolute error of 6.972 bpm, demonstrating the capabilities of the underlying technology.

U2 - 10.1145/3232059.3232060

DO - 10.1145/3232059.3232060

M3 - Conference contribution

SN - 9781450363662

SP - 1

EP - 6

BT - Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018)

PB - Association for Computing Machinery

T2 - 10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018)

Y2 - 16 May 2018 through 18 May 2018

ER -

Remote heart rate extraction using Microsoft Kinect™ v2.0

Abstract

Conference

Access to Document

Fingerprint

Cite this