InsightNet: non-contact blood pressure measuring network based on face
video
- URL: http://arxiv.org/abs/2203.03634v1
- Date: Mon, 7 Mar 2022 07:06:42 GMT
- Title: InsightNet: non-contact blood pressure measuring network based on face
video
- Authors: Jialiang Zhuang and Bin Li and Yun Zhang and Xiujuan Zheng
- Abstract summary: Blood pressure data are mainly acquired through contact sensors, which require high maintenance and may be inconvenient and unfriendly to some people.
In this paper, an efficient non-contact blood pressure measurement network based on face videos is proposed for the first time.
- Score: 9.824584818736032
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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