FastBVP-Net: a lightweight pulse extraction network for measuring heart rhythm via facial videos

Zhuang, Jialiang; Chen, Yuheng; Zhang, Yun; Zheng, Xiaodong

doi:10.48550/arxiv.2206.12558

Cited by 1 publication

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“…Therefore, a data augmentation strategy is required to solve the problem of signal contamination. In 2022, Zhuang et al [ 29 ] proposed a modified YUV color space that gives more attention to the features of the brightness dimension in color space and reduces the noise caused by variations in the environmental illumination. First, we randomly mask a small part of the initial spatiotemporal map

along both the time dimension and spatial dimension, as shown in Figure 2 .…”

Section: Methodsmentioning

confidence: 99%

Remote Blood Pressure Estimation via the Spatiotemporal Mapping of Facial Videos

Chen

Zhuang

et al. 2023

Sensors

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Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world.

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along both the time dimension and spatial dimension, as shown in Figure 2 .…”

Section: Methodsmentioning

confidence: 99%