Key result
A neural architecture search approach for heart rate estimation from face videos achieved better performance and lower computational cost compared with state-of-the-art methods on public databases.
Why the study?
Existing deep-learning methods for remote photoplethysmography heart-rate estimation have high computational costs, particularly in mobile-computing scenarios.
A novel neural architecture search approach for remote photoplethysmography improves heart rate estimation accuracy from face videos while reducing computational costs, facilitating mobile health monitoring.
May enable efficient mobile rPPG-based HR monitoring; leaves open prospective clinical validation.
In anticipation of its great potential application to natural human-computer interaction and health monitoring, heart-rate (HR) estimation based on remote photoplethysmography has recently attracted increasing research attention. Whereas the recent deep-learning-based HR estimation methods have achieved promising performance, their computational costs remain high, particularly in mobile-computing scenarios. We propose a neural architecture search approach for HR estimation to automatically search a lightweight network that can achieve even higher accuracy than a complex network while reducing the computational cost. First, we define the regions of interests based on face landmarks and then extract the raw temporal pulse signals from the R, G, and B channels in each ROI. Then, pulse-related signals are extracted using a plane-orthogonal-to-skin algorithm, which are combined with the R and G channel signals to create a spatial-temporal map. Finally, a differentiable architecture search approach is used for the network-structure search. Compared with the state-of-the-art methods on the public-domain VIPL-HR and PURE databases, our method achieves better HR estimation performance in terms of several evaluation metrics while requiring a much lower computational cost1.
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Lü et al. (2021) studied Heart rate estimation. Neural architecture search approach for HR estimation vs. State-of-the-art methods was evaluated on HR estimation performance and computational cost. A neural architecture search approach for heart rate estimation from face videos achieved better performance and lower computational cost compared with state-of-the-art methods on public databases.
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