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Blood pressure (BP) monitoring is vital in daily healthcare, especially forcardiovascular diseases. However, BP values are mainly acquired through thecontact sensing method, which is inconvenient and unfriendly to continuous BPmeasurement. Hence, we propose an efficient end-to-end network to estimate theBP 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 BPranges by a designed blood pressure classifier and simultaneously calculatedthe specific value by a blood pressure calculator in each BP range. Inaddition, we also developed an innovative oversampling training strategy tohandle the unbalanced data distribution problem. Finally, we trained theproposed network on a private dataset ASPD and tested it on the popular datasetMMSE-HR. As a result, the proposed network achieved a state-of-the-art MAE of12.35 mmHg and 9.5 mmHg on systolic and diastolic BP measurements, which isbetter than the recent works. It concludes that the proposed method hasexcellent potential for camera-based BP monitoring in real-world scenarios.

Remote blood pressure measurement via spatiotemporal mapping of a short-time facial video