Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda
Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a v...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/18/6296 |
| _version_ | 1797517234525962240 |
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| author | Chun-Hong Cheng Kwan-Long Wong Jing-Wei Chin Tsz-Tai Chan Richard H. Y. So |
| author_facet | Chun-Hong Cheng Kwan-Long Wong Jing-Wei Chin Tsz-Tai Chan Richard H. Y. So |
| author_sort | Chun-Hong Cheng |
| collection | DOAJ |
| description | Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations. |
| first_indexed | 2024-03-10T07:13:54Z |
| format | Article |
| id | doaj.art-ddd57259cb304f3191d0e1d532de9d54 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:13:54Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ddd57259cb304f3191d0e1d532de9d542023-11-22T15:14:41ZengMDPI AGSensors1424-82202021-09-012118629610.3390/s21186296Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research AgendaChun-Hong Cheng0Kwan-Long Wong1Jing-Wei Chin2Tsz-Tai Chan3Richard H. Y. So4Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, ChinaPanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, ChinaPanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, ChinaPanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, ChinaPanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, ChinaHeart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.https://www.mdpi.com/1424-8220/21/18/6296noncontact monitoringheart rate measurementremote photoplethysmographyrPPGdeep learning |
| spellingShingle | Chun-Hong Cheng Kwan-Long Wong Jing-Wei Chin Tsz-Tai Chan Richard H. Y. So Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda Sensors noncontact monitoring heart rate measurement remote photoplethysmography rPPG deep learning |
| title | Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda |
| title_full | Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda |
| title_fullStr | Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda |
| title_full_unstemmed | Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda |
| title_short | Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda |
| title_sort | deep learning methods for remote heart rate measurement a review and future research agenda |
| topic | noncontact monitoring heart rate measurement remote photoplethysmography rPPG deep learning |
| url | https://www.mdpi.com/1424-8220/21/18/6296 |
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