Key result
A proposed head-motion robust video-based heart rate estimation method using facial feature point fluctuations achieved the highest accuracy among state-of-the-art methods on a benchmark dataset.
Why the study?
Does head-motion robust video-based heart rate estimation using facial feature point fluctuations improve accuracy compared to state-of-the-art methods in a benchmark dataset?
Does head-motion robust video-based heart rate estimation using facial feature point fluctuations improve accuracy compared to state-of-the-art methods in a benchmark dataset?
A novel video-based heart rate estimation method using facial feature point fluctuations improves accuracy by removing head-motion noise.
Requires clinical validation before use; extends benchmark accuracy of motion-robust video HR estimation but leaves real-world applicability open.
Interest in measuring heart rates (HRs) without physical contact has increased in the area of stress checking and health care. In this paper, we propose head-motion robust video-based heart rate estimation using facial feature point fluctuations. The proposed method adaptively estimates and removes such rigid-noise components as noise stemming from horizontal head motion and extracts relatively small heart signals. Rigid-noise components can be accurately estimated and removed by using changes in facial feature points which are not dominant over heart signals and are more dominant over noise signals than are such luminance signals as RGB. In evaluation experiments on a benchmark dataset, our method achieved the highest accuracy among state-of-the-art methods.
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Umematsu et al. (2018) studied this question. Head-motion robust video-based heart rate estimation using facial feature point fluctuations vs. State-of-the-art methods was evaluated on Accuracy of heart rate estimation. A proposed head-motion robust video-based heart rate estimation method using facial feature point fluctuations achieved the highest accuracy among state-of-the-art methods on a benchmark dataset.
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