Abstract
Remote pulse rate measurement from facial video has gained particular attention over the last few years. Research exhibits significant advancements and demonstrates that common video cameras correspond to reliable devices that can be employed to measure a large set of biomedical parameters without any contact with the subject. A new framework for measuring and mapping pulse rate from video is presented in this pilot study. The method, which relies on convolutional 3D networks, is fully automatic and does not require any special image preprocessing. In addition, the network ensures concurrent mapping by producing a prediction for each local group of pixels. A particular training procedure that employs only synthetic data is proposed. Preliminary results demonstrate that this convolutional 3D network can effectively extract pulse rate from video without the need for any processing of frames. The trained model was compared with other state-of-the-art methods on public data. Results exhibit significant agreement between estimated and ground-truth measurements: the root mean square error computed from pulse rate values assessed with the convolutional 3D network is equal to 8.64 bpm, which is superior to 10 bpm for the other state-of-the-art methods. The robustness of the method to natural motion and increases in performance correspond to the two main avenues that will be considered in future works.
Highlights
The domain of physiological signal measurement using contactless devices has gained vast attention
Results exhibit significant agreement between estimated and ground-truth measurements: the root mean square error computed from pulse rate values assessed with the convolutional 3D network is equal to 8.64 bpm, which is superior to 10 bpm for the other state-of-the-art methods
Research exhibits significant advancements over the last few years and demonstrates that standard video cameras are reliable devices that can be employed to measure a large set of biomedical parameters without any contact with the subject
Summary
The domain of physiological signal measurement using contactless devices has gained vast attention. Research exhibits significant advancements over the last few years and demonstrates that standard video cameras are reliable devices that can be employed to measure a large set of biomedical parameters without any contact with the subject. The main challenge consists in improving robustness to natural motion that produces undesirable noise and artifacts in the measurements. This issue is common to most systems that record and analyze images to sense vital signs and biomedical parameters. In the era of ubiquitous computing where mobile devices (smartphones, laptops, tablets, ...) are omnipresent, cameras and webcams are sensors that are already available and, that are interesting for unobtrusively measuring vital signs
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