Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through the contact sensing method, which is inconvenient and unfriendly to continuous BP measurement. Hence, we propose an efficient end-to-end network to estimate the BP values from a facial video to achieve remote BP measurement in daily life. In this study, we first derived a Spatial-temporal map of a short-time (~15s) facial video. According to the Spatial-temporal map, we then regressed the BP ranges by a designed blood pressure classifier and simultaneously calculated the specific value by a blood pressure calculator in each BP range. In addition, we also developed an innovative oversampling training strategy to handle the unbalanced data distribution problem. Finally, we trained the proposed network on a private dataset ASPD and tested it on the popular dataset MMSE-HR. As a result, the proposed network achieved a state-of-the-art MAE of 12.35 mmHg and 9.5 mmHg on systolic and diastolic BP measurements, which is better than the recent works. It concludes that the proposed method has excellent potential for camera-based BP monitoring in real-world scenarios.
翻译:血液压力监测(BP)在日常保健中至关重要,特别是心血管疾病。然而,BP值主要通过接触感测方法获得,接触感测方法不方便,不利于连续的BP测量。因此,我们建议建立一个高效端到端网络,从面部视频中估计BP值,以在日常生活中实现远程的BP测量。我们首先从短期(~15s)面部视频中绘制空间时空地图。根据空间时空图,我们随后用一个设计的血压分类器回溯BP范围,同时用BP范围内的血压计算器计算具体值。此外,我们还开发了一个创新的过度抽样培训战略,以处理不平衡的数据分布问题。最后,我们用一个私人数据集对拟议的网络进行了培训,并在流行的数据集MMSE-HR上测试了这个网络。结果,拟议的网络实现了一个基于12.35毫米Hg和9.5毫米Hg的状态艺术MAE,在Systolic BP测量上实现了9.5毫米的定位值,这比近期的BP测算法要好。