Lee Seongbeen, Lee Minseon, Sim Joo Yong
Department of Mechanical Systems Engineering, Sookmyung Women's University, Seoul 04310, Republic of Korea.
Bioengineering (Basel). 2023 Dec 15;10(12):1428. doi: 10.3390/bioengineering10121428.
Non-contact remote photoplethysmography can be used in a variety of medical and healthcare fields by measuring vital signs continuously and unobtrusively. Recently, end-to-end deep learning methods have been proposed to replace the existing handcrafted features. However, since the existing deep learning methods are known as black box models, the problem of interpretability has been raised, and the same problem exists in the remote photoplethysmography (rPPG) network. In this study, we propose a method to visualize temporal and spectral representations for hidden layers, deeply supervise the spectral representation of intermediate layers through the depth of networks and optimize it for a lightweight model. The optimized network improves performance and enables fast training and inference times. The proposed spectral deep supervision helps to achieve not only high performance but also fast convergence speed through the regularization of the intermediate layers. The effect of the proposed methods was confirmed through a thorough ablation study on public datasets. As a result, similar or outperforming results were obtained in comparison to state-of-the-art models. In particular, our model achieved an RMSE of 1 bpm on the PURE dataset, demonstrating its high accuracy. Moreover, it excelled on the V4V dataset with an impressive RMSE of 6.65 bpm, outperforming other methods. We observe that our model began converging from the very first epoch, a significant improvement over other models in terms of learning efficiency. Our approach is expected to be generally applicable to models that learn spectral domain information as well as to the applications of regression that require the representations of periodicity.
非接触式远程光电容积脉搏波描记法可通过连续且不引人注意地测量生命体征，应用于各种医疗和保健领域。最近，已提出端到端深度学习方法来取代现有的手工特征。然而，由于现有的深度学习方法被称为黑箱模型，因此出现了可解释性问题，远程光电容积脉搏波描记法（rPPG）网络也存在同样的问题。在本研究中，我们提出了一种方法，用于可视化隐藏层的时间和频谱表示，通过网络深度对中间层的频谱表示进行深度监督，并针对轻量级模型对其进行优化。优化后的网络提高了性能，并实现了快速训练和推理时间。所提出的频谱深度监督不仅有助于通过中间层的正则化实现高性能，还能实现快速收敛速度。通过对公共数据集进行全面的消融研究，证实了所提出方法的效果。结果表明，与现有最先进模型相比，获得了相似或更优的结果。特别是，我们的模型在PURE数据集上实现了1 bpm的均方根误差（RMSE），证明了其高精度。此外，它在V4V数据集上表现出色，RMSE达到了令人印象深刻的6.65 bpm，优于其他方法。我们观察到，我们的模型从第一个epoch就开始收敛，在学习效率方面比其他模型有显著提高。我们的方法有望普遍适用于学习频谱域信息的模型以及需要周期性表示的回归应用。
