Key result
One-branch convolutional attention networks with residual connections outperform complex models for heart rate estimation.
Why the study?
Complex deep learning methods for remote photoplethysmography theoretically provide higher accuracy but increase computational cost and inference time, creating a need to balance network complexity with model performance.
Incorporating a residual connection into a simpler one-branch network improves remote photoplethysmography heart rate estimation performance while maintaining moderate inference time suitable for real-time applications.
Answers are grounded in this paper and cite their sources.
May favor simpler residual networks for real-time rPPG HR estimation; leaves open generalizability to clinical populations.
The public’s interest in remote photoplethysmography began with conventional signal processing and has increased significantly in recent years due to the rise of deep learning techniques. While complex deep learning methods theoretically offer higher accuracy in photoplethysmography estimation, they come with increased computational cost and inference time. Therefore, striking a balance between network complexity and model performance, including computational cost and inference time concerns, has become a topic of study. This study extends the previous works on two-branch network and one-branch end-to-end convolutional attention network by introducing a residual connection in the latter. The performance and inference time of the proposed network are compared with the existing methods using a single public dataset. The results reveal that the adjusted network performs better than more complex networks while maintaining an acceptable inference time. This finding suggests that incorporating a residual connection into a simpler one-branch network can alleviate the limitations of using a more complex network, even with improved performance. Moreover, the moderate inference time allows for implementing real-time applications like sports and fitness monitoring.
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Lee et al. (2023) studied Heart rate estimation. One-branch convolutional attention network with residual connection vs. More complex networks was evaluated on Performance and inference time. An adjusted one-branch convolutional attention network with a residual connection performed better than more complex networks for heart rate estimation while maintaining acceptable inference time.
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