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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 KinectTM 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 KinectTM 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 languageEnglish
Title of host publicationProceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018)
PublisherAssociation for Computing Machinery
Pages1-6
Number of pages6
ISBN (Print)978-1-4503-6366-2
DOIs
Publication statusPublished - 16 May 2018
Event10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018) - Amsterdam, Netherlands
Duration: 16 May 201818 May 2018
http://www.icbbt.org/

Conference

Conference10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018)
CountryNetherlands
CityAmsterdam
Period16/05/1818/05/18
Internet address

Fingerprint

Image sensors
Learning systems
Infrared radiation
Electrocardiography
Signal processing
Experiments

Cite this

Malasinghe, L., Katsigiannis, S., Ramzan, N., & Dahal, K. (2018). Remote heart rate extraction using Microsoft KinectTM v2.0. In Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018) (pp. 1-6). Association for Computing Machinery. https://doi.org/10.1145/3232059.3232060
Malasinghe, Lakmini ; Katsigiannis, Stamos ; Ramzan, Naeem ; Dahal, Keshav. / Remote heart rate extraction using Microsoft KinectTM v2.0. Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018). Association for Computing Machinery, 2018. pp. 1-6
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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 KinectTM 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 KinectTM 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.",
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Malasinghe, L, Katsigiannis, S, Ramzan, N & Dahal, K 2018, Remote heart rate extraction using Microsoft KinectTM 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 KinectTM v2.0. / Malasinghe, Lakmini; Katsigiannis, Stamos; Ramzan, Naeem; Dahal, Keshav.

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 proceedingConference contribution

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Malasinghe L, Katsigiannis S, Ramzan N, Dahal K. Remote heart rate extraction using Microsoft KinectTM v2.0. In Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2018). Association for Computing Machinery. 2018. p. 1-6 https://doi.org/10.1145/3232059.3232060