Title | Super-Resolution Convolutional Network for Image Quality Enhancement in Remote Photoplethysmography based Heart Rate Estimation |
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Authors | Smera Premkumar, K., Angelopoulou, A., Chaussalet, T.J., Kapetanios, D.E. and Jude Hemanth, D. |
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Type | Conference paper |
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Abstract | Heart rate (HR) is one of the important vital parameters of the human body and understanding this vital sign provides key insights into human wellness. Imaging photoplethysmography (iPPG) allows HR detection from video recordings and its unbeatable compliance over the state of art methods has made much attention among researchers. Since it is a camera-based technique, measurement accuracy depends on the quality of input images. In this paper, we present a pipeline for efficient measurement of HR that includes a learning-based super-resolution preprocessing step. This preprocessing image enhancement step has shown promising results on low-resolution input images and works better on iPPG algorithms. The experimental results verified the reliability of this method. |
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Keywords | Remote Photoplethysmography |
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| Convolutional Neural Network |
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| Heart Rate (HR) Detection |
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| Image Enhancement |
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Year | 2022 |
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Conference | International Conference on Computational Science ICCS 2022 |
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Publisher | Springer |
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Accepted author manuscript | File Access Level Open (open metadata and files) |
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Publisher's version | File Access Level Open (open metadata and files) |
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Publication dates |
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Published | 15 Jun 2022 |
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ISBN | 9783031087561 |
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Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-08757-8_15 |
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Web address (URL) of conference proceedings | https://link.springer.com/chapter/10.1007/978-3-031-08757-8_15#citeas |
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Web address (URL) | https://www.iccs-meeting.org/iccs2022/ |
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