Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation
Remote photo-plethysmography (rPPG) uses a camera to estimate a person’s heart rate (HR). Similar to how heart rate can provide useful information about a person’s vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/23/8630 |
| _version_ | 1827700930065203200 |
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| author | Amogh Gudi Marian Bittner Jan van Gemert |
| author_facet | Amogh Gudi Marian Bittner Jan van Gemert |
| author_sort | Amogh Gudi |
| collection | DOAJ |
| description | Remote photo-plethysmography (rPPG) uses a camera to estimate a person’s heart rate (HR). Similar to how heart rate can provide useful information about a person’s vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rates, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This unsupervised method requires no rPPG specific training and is able to operate in real-time. We also introduce a new multi-modal video dataset, VicarPPG 2, specifically designed to evaluate rPPG algorithms on HR and HRV estimation. We validate and study our method under various conditions on a comprehensive range of public and self-recorded datasets, showing state-of-the-art results and providing useful insights into some unique aspects. Lastly, we make available CleanerPPG, a collection of human-verified ground truth peak/heart-beat annotations for existing rPPG datasets. These verified annotations should make future evaluations and benchmarking of rPPG algorithms more accurate, standardized and fair. |
| first_indexed | 2024-03-10T14:22:41Z |
| format | Article |
| id | doaj.art-c488e1eddb8f4a54bb8de62b879c0b2d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:22:41Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c488e1eddb8f4a54bb8de62b879c0b2d2023-11-20T23:16:53ZengMDPI AGApplied Sciences2076-34172020-12-011023863010.3390/app10238630Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for EvaluationAmogh Gudi0Marian Bittner1Jan van Gemert2Vicarious Perception Technologies (VicarVision), 1015 AH Amsterdam, The NetherlandsVicarious Perception Technologies (VicarVision), 1015 AH Amsterdam, The NetherlandsIntelligent Systems, Delft University of Technology, 2628 XE Delft, The NetherlandsRemote photo-plethysmography (rPPG) uses a camera to estimate a person’s heart rate (HR). Similar to how heart rate can provide useful information about a person’s vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rates, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This unsupervised method requires no rPPG specific training and is able to operate in real-time. We also introduce a new multi-modal video dataset, VicarPPG 2, specifically designed to evaluate rPPG algorithms on HR and HRV estimation. We validate and study our method under various conditions on a comprehensive range of public and self-recorded datasets, showing state-of-the-art results and providing useful insights into some unique aspects. Lastly, we make available CleanerPPG, a collection of human-verified ground truth peak/heart-beat annotations for existing rPPG datasets. These verified annotations should make future evaluations and benchmarking of rPPG algorithms more accurate, standardized and fair.https://www.mdpi.com/2076-3417/10/23/8630remote photoplethysmographyheart rate variabilityunsupervisedclean ground truth |
| spellingShingle | Amogh Gudi Marian Bittner Jan van Gemert Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation Applied Sciences remote photoplethysmography heart rate variability unsupervised clean ground truth |
| title | Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation |
| title_full | Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation |
| title_fullStr | Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation |
| title_full_unstemmed | Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation |
| title_short | Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation |
| title_sort | real time webcam heart rate and variability estimation with clean ground truth for evaluation |
| topic | remote photoplethysmography heart rate variability unsupervised clean ground truth |
| url | https://www.mdpi.com/2076-3417/10/23/8630 |
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