Abstract
Blood pressure indicates cardiac function and peripheral vascular resistance and is critical for disease diagnosis. Traditionally, blood pressure data are mainly acquired through contact sensors, which require high maintenance and may be inconvenient and unfriendly to some people (e.g., burn patients). In this paper, an efficient non-contact blood pressure measurement network based on face videos is proposed for the first time. An innovative oversampling training strategy is proposed to handle the unbalanced data distribution. The input video sequences are first normalized and converted to our proposed YUVT color space. Then, the Spatio-temporal slicer encodes it into a multi-domain Spatio-temporal mapping. Finally, the neural network computation module, used for high-dimensional feature extraction of the multi-domain spatial feature mapping, after which the extracted high-dimensional features are used to enhance the time-domain feature association using LSTM, is computed by the blood pressure classifier to obtain the blood pressure measurement intervals. Combining the output of feature extraction and the result after classification, the blood pressure calculator, calculates the blood pressure measurement values. The solution uses a blood pressure classifier to calculate blood pressure intervals, which can help the neural network distinguish between the high-dimensional features of different blood pressure intervals and alleviate the overfitting phenomenon. It can also locate the blood pressure intervals, correct the final blood pressure values and improve the network performance. Experimental results on two datasets show that the network outperforms existing state-of-the-art methods.
Abstract (translated)
URL
https://arxiv.org/abs/2203.03634
