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Camera-based heart rate measurement is becoming an attractive option as a non-contact modality for continuous remote health and engagement monitoring. However, reliable heart rate extraction from camera-based measurement is challenging in realistic scenarios, especially when the subject is moving. In this work, we develop a motion-robust algorithm, labeled RobustPPG, for extracting photoplethysmography signals (PPG) from face video and estimating the heart rate. Our key innovation is to explicitly model and generate motion distortions due to the movements of the person’s face. We use inverse rendering to obtain the 3D shape and albedo of the face and environment lighting from video frames and then render the human face for each frame. The rendered face is similar to the original face but does not contain the heart rate signal; facial movements alone cause pixel intensity variation in the generated video frames. Finally, we use the generated motion distortion to filter the motion-induced measurements. We demonstrate that our approach performs better than the state-of-the-art methods in extracting a clean blood volume signal with over 2 dB signal quality improvement and 30% improvement in RMSE of estimated heart rate in intense motion scenarios.
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PPG3D: Does 3D head tracking improve camera-based PPG estimation?
Over the last few years, camera-based estimation of vital signs referred to as imaging photoplethysmography (iPPG) has garnered significant attention due to the relative simplicity, ease, unobtrusiveness and flexibility offered by such measurements. It is expected that iPPG may be integrated into a host of emerging applications in areas as diverse as autonomous cars, neonatal monitoring, and telemedicine. In spite of this potential, the primary challenge of non-contact camera-based measurements is the relative motion between the camera and the subjects. Current techniques employ 2D feature tracking to reduce the effect of subject and camera motion but they are limited to handling translational and in-plane motion. In this paper, we study, for the first-time, the utility of 3D face tracking to allow iPPG to retain robust performance even in presence of out-of-plane and large relative motions. We use a RGB-D camera to obtain 3D information from the subjects and use the spatial and depth information to fit a 3D face model and track the model over the video frames. This allows us to estimate correspondence over the entire video with pixel-level accuracy, even in the presence of out-of-plane or large motions. We then estimate iPPG from the warped video data that ensures per-pixel correspondence over the entire window-length used for estimation. Our experiments demonstrate improvement in robustness when head motion is large.
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- Award ID(s):
- 1801372
- PAR ID:
- 10301748
- Date Published:
- Journal Name:
- 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
- Page Range / eLocation ID:
- 1194 to 1197
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/EMBC44109.2020.9176065
