CN112869737B - Non-contact human body blood oxygen saturation detection method - Google Patents
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
- A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
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- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
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- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/165—Detection; Localisation; Normalisation using facial parts and geometric relationships
Abstract
The invention belongs to the technical field of blood oxygen detection, and particularly relates to a non-contact human body blood oxygen saturation detection method. A non-contact human body blood oxygen saturation detection method comprises the following steps: s1, video acquisition; s2, acquiring an image area; s3, acquiring an original signal; s4, obtaining a separation signal; s5, obtaining a variable; s6, result acquisition, based on the AC extracted in the step S5 R 、AC B And DC R 、DC B And calculating a blood oxygen saturation parameter R, and obtaining the blood oxygen saturation SPO2 according to the Lambert-beer law. The invention provides a non-contact detection method for human blood oxygen saturation, which solves the problems that the signal-to-noise ratio of an original signal extracted based on a red channel and a blue channel in the disclosed method is poor and the accuracy of a measurement result is not high.
Description
Technical Field
The invention belongs to the technical field of blood oxygen detection, and particularly relates to a non-contact human body blood oxygen saturation detection method.
Background
The blood oxygen saturation (SPO 2) is the percentage of the hemoglobin volume in human blood combined by oxygen to the total hemoglobin volume, and is an important clinical health monitoring index as an important parameter of respiratory cycle, which can estimate the oxygenation of lung and the oxygen carrying capacity of hemoglobin.
Conventional methods for measuring blood oxygen saturation are classified into invasive methods and non-invasive methods. The invasive method is to collect blood from a human body and analyze the blood with a blood gas analyzer to calculate a value of blood oxygen saturation. This method is cumbersome and does not allow continuous measurement, the most important drawback being the pain or infection risk for the user. The noninvasive method mainly uses a finger-clip oximeter, and needs to attach the oximeter to a finger of a person, and although the noninvasive method can continuously measure the value, the noninvasive method is obviously not suitable for users with skin wounds or twitch patients, and also causes discomfort for general users. In recent years, imaging photoplethysmography (IPPG) has been developed, and has been a hot research direction in the non-contact field due to its feature of not requiring data acquisition directly through human skin images by contact with human skin. The study of the blood oxygen saturation measurement method is carried out by scholars at home and abroad based on the advantage of IPPG, the content is realized based on dual-wavelength visible light, the study is generally in an exploration stage, and the device is expensive and not portable enough, so that the device is not beneficial to daily monitoring.
With the rapid development of the scientific age, devices equipped with cameras, such as notebooks, smart phones, and the like, have almost become a tool essential for life, and based on image data acquired by these color cameras, it has proven feasible to measure the blood oxygen saturation level by the IPPG technique. Some of the published documents and patents mention that the red and blue channels of the color camera video are used as a dual-wavelength combination for measuring the blood oxygen saturation, but because the resolution of these low-end cameras is low and the sensitivity is weak, if the pixel values of the red and blue channels are simply used as the measurement parameters, the noise interference is large and the measurement result is not accurate enough.
Disclosure of Invention
The invention aims to solve the technical problem of providing a non-contact human blood oxygen saturation detection method which solves the problems of poor signal-to-noise ratio of original signals extracted based on red and blue channels and low accuracy of measurement results in the disclosed method. Therefore, the invention adopts the following technical scheme:
a non-contact human body blood oxygen saturation detection method comprises the following steps:
s1, acquiring a video, and acquiring human face video data;
s2, obtaining an image area, namely intercepting each frame of image of the video in the step S1, and selecting two cheek parts of the face from the video frame image as an ROI (region of interest);
s3, obtaining an original signal, and extracting the average pixel value P of the red channel of the ROI in the step S2 R And blue colorChannel average pixel value P B As the original pulse wave signal;
s4, obtaining the separation signals, and respectively carrying out the pulse wave red channel signals P obtained in the step S3 by adopting a single-channel independent component analysis algorithm based on dynamic embedding R And blue channel signal P B Carrying out blind source separation to obtain a reconstructed separation signal I R And I B ；
S5, obtaining variables, and respectively extracting the reconstruction signals I obtained in the step S4 R And I B AC variable AC R 、AC B And a direct current variable DC R 、DC B ；
S6, result acquisition, based on the AC extracted in the step S5 R 、AC B And DC R 、DC B And calculating a blood oxygen saturation parameter R, and obtaining the blood oxygen saturation SPO2 according to the Lambert-beer law.
The method adopts a Dynamic Embedding (DE) -based single-channel Independent Component Analysis (ICA) algorithm (DE-ICA) to respectively carry out blind source separation on the acquired red and blue channel signals, effectively eliminates noise irrelevant to BVP, further carries out blood oxygen saturation estimation of a dual-wavelength method according to the Lambert-beer law and improves the detection accuracy.
On the basis of the technical scheme, the invention can also adopt the following further technical scheme:
the step S4 further includes the steps of:
s41, selecting the embedding dimension m and the time delay delta, and respectively adding the P acquired in the step S3 R And P B And performing phase space reconstruction to form two groups of multidimensional vectors. The ICA algorithm requires that the number of observation signals is more than or equal to the number of source signals, so that one-dimensional signals P with the length of N are firstly and respectively used R And P B And performing phase space reconstruction to form two groups of multidimensional vectors.
S42, when m is larger, firstly reducing the dimension of the obtained embedded matrix P to k by using a rapid ICA algorithm, and then performing blind source separation on the matrix subjected to dimension reduction by using the rapid ICA algorithm to separate an independent component Q;
S43、independent component Q related to BVP is selected from Q j Multiplying by the column vector B of the corresponding mixing matrix B j Obtaining independent sub-components P j ＝∑b i Q i I ∈ j, j is the set of independent components associated with the BVP;
s44, component P j Performing inverse reconstruction to obtain final one-dimensional separation signal
Further, the step S42 further includes separating out independent components Q = AP, Q = [ Q ] 1 ,Q 2 ,...,Q n ] T A is an n × k dimensional unmixing matrix, P is a multidimensional vector, n is the number of separated independent signals, and n = k;
estimate the mixing matrix B = pinv (a), B = [ B ] 1 ,b 2 ,...,b k ] T Pinv represents the pseudo-inverse.
Further, the method for selecting the embedding dimension m and the time delay Δ in step S41 includes:
the embedding dimension m and the time delay delta are chosen such that the signal P is observed separately R And P B Becoming a new delay variable, the formula is as follows:
P(t)＝[p(t),p(t+Δ),...,p(t+m-1)Δ],t＝1,2,...,N-(m-1)Δ
and combining the signals according to the formula to form a multi-dimensional embedded delay matrix signal, wherein the formula is as follows:
the time delay delta is taken as 1, the embedding dimension m is set according to the sampling frequency of the observation signal and the minimum frequency of the source signal,
wherein fps is an observation signal P R And P B The sampling frequency of (2).
The fps is the frame rate of the camera and is 30 frames/second; f. of L And taking the minimum frequency of the separated source signals, wherein m is 30-60. Wherein, in order to ensure that the obtained embedded matrix P can extract enough implicit information, f L Typically taking the minimum frequency of the separated source signal. Since the signal frequency associated with BVP is typically 1-2HZ, m is typically 30-60.
Further, when the dimension of the obtained embedded matrix P is reduced by using the fast ICA algorithm in step S42, the dimension k after the dimension reduction is selected based on the eigenvalue contribution cumulative quantity.
Further, the inverse process of DE-ICA in step S44 can be expressed as the following formula:
wherein
ceil denotes rounding up. According to the formula
The sequence of (A) is P j Performing reconstruction to one-dimensional separation signal
Repeated elements during reconstruction are replaced by means.
Extracting a reconstructed signal I in the step S5 R And I B Of alternating current variable AC R 、AC B And a direct current variable DC R 、DC B The method comprises the following steps:
for the reconstructed signal I R And I B Filtering with 0.6-3 HZ band-pass filter to extract frequency signal S related to human artery blood flow volume pulse BVP R And S B ；
By calculating S R And S B Standard deviation of (A) to obtain the AC variable AC R And AC B ；
By calculating S R And S B Obtaining the direct current variable DC R 、DC B 。
The calculation method of the blood oxygen saturation value in the step S6 comprises the following steps:
SPO2＝A+B*R
wherein the content of the first and second substances,
B. and B is obtained by performing least square linear fitting on the obtained R sequence and the oximeter reference value.
Compared with the prior art, the invention has the following beneficial effects:
the acquired red and blue channel signals are respectively subjected to blind source separation by adopting a DE-ICA algorithm, noise irrelevant to BVP is effectively eliminated, and then blood oxygen saturation estimation of a dual-wavelength method is carried out according to the Lambert-beer law, so that the detection accuracy is improved.
Drawings
FIG. 1 is a schematic flow chart of a non-contact human blood oxygen saturation detection method according to the present invention;
FIG. 2 is a schematic diagram of human face ROI detection according to a non-contact human blood oxygen saturation detection method of the present invention;
FIG. 3 is a waveform comparison diagram after noise filtering by the DE-ICA algorithm of the non-contact human blood oxygen saturation detection method of the present invention;
FIG. 4 is a diagram illustrating the results of measuring the blood oxygen saturation in the natural state by using the non-contact human blood oxygen saturation detection method of the present invention;
FIG. 5 is a schematic diagram of the results of measuring the blood oxygen saturation level in breath holding state by using the non-contact human blood oxygen saturation level detection method of the present invention.
Detailed Description
In order to further understand the present invention, the following will specifically describe the non-contact human blood oxygen saturation detection method provided by the present invention with reference to the specific embodiments, but the present invention is not limited thereto, and the non-essential modifications and adjustments made by those skilled in the art under the core guidance of the present invention still belong to the protection scope of the present invention.
As shown in fig. 1-5, a non-contact human blood oxygen saturation detection method includes the following steps:
s1, acquiring human face video data.
Specifically, the intelligent mobile phone with the common camera is placed 50cm in front of the face, so that the face is kept right opposite to the camera and is completely in a shot picture. The resolution of the recorded video is 540 multiplied by 960, the frame rate is 30, the RGB color space is adopted, the recorded video is 10s, the format is stored in an mp4 format, and the operating environment is win7+ Python3.
S2, intercepting each frame of image in the video, and selecting two cheek parts of the face from the video frame image as regions of interest (ROI).
Specifically, a human face detector Dlib library is adopted to identify the human face in the video image and locate 68 feature points of the face, and two cheek ROIs are framed based on part of the 68 feature points.
S3, extracting the average pixel value P of the red channel of the ROI R And blue channel average pixel value P B As the original pulse wave signal.
Specifically, RGB channel values of two ROIs in each frame image are extracted, and red and blue channel pixel values of the two ROIs are averaged, thereby generating a two-channel signal P based on the ROIs R And P B 。
S4, as shown in figure 3, respectively carrying out pulse wave red channel signals P by adopting a DE-ICA algorithm R And blue channel signal P B Carrying out blind source separation to obtain a reconstructed separation signal I R And I B ；
Further, step S4 comprises the steps of:
s41 and ICA algorithms require that the number of observed signals is larger than or equal to that of source signals, so that one-dimensional signals P with the length of N are firstly respectively used R And P B And performing phase space reconstruction to form two groups of multidimensional vectors.
In particular, the embedding dimension m and the time delay Δ are chosen such that the observed signal P is respectively R And P B Becoming a new delay variable, the formula is as follows:
P(t)＝[p(t),p(t+Δ),...,p(t+m-1)Δ],t＝1,2,...,N-(m-1)Δ
and combining the signals according to the formula to form a multi-dimensional embedded delay matrix signal, wherein the formula is as follows:
wherein, the time delay Δ is generally 1, and the embedding dimension m can be set according to the sampling frequency of the observation signal and the minimum frequency of the source signal, and the formula is as follows:
wherein fps is an observation signal P R And P B I.e. here the frame rate of the camera, 30 frames/second. To ensure that the acquired embedding matrix P can extract enough implicit information, f L Typically taking the minimum frequency of the separated source signal. Since the signal frequency related to BVP is generally 1-2Hz, m is generally 30-60, 30 in this example.
S42, because the embedding dimension m is larger, a fast ICA (FastICA) algorithm may be used to reduce the dimension of the obtained embedded matrix P to k (in this embodiment, k is selected based on that the eigenvalue contribution cumulative amount is greater than or equal to 90%), and then FastICA is used to perform blind source separation on the matrix after dimension reduction, so as to separate out independent components Q = AP and Q = [ Q ] = 1 ,Q 2 ,...,Q n ] T Where a is an n × k dimensional unmixing matrix and n is the number of separated independent signals, where n = k. Simultaneously estimate the mixing matrix B = pinv (a), B = [ B ] 1 ,b 2 ,...,b k ] T Pinv represents the pseudo-inverse.
S43, extracting independent component Q related to BVP from Q j Multiplying by the column vector B of the corresponding mixing matrix B j Obtaining independent sub-components P j ＝∑b i Q i I ∈ j, j being an independent component relating to BVPAnd (4) collecting.
S44, component P j Performing inverse reconstruction to obtain final one-dimensional separation signal
Specifically, the inverse process of DE-ICA can represent the following formula,
wherein
ceil denotes rounding up. According to the formula
The sequence of (A) is P j Performing reconstruction to one-dimensional separation signal
Repeated elements during reconstruction are replaced by means.
S45, for the one-dimensional signal P obtained in the step S102 R And P B Respectively processing the above steps to obtain a reconstructed one-dimensional separation signal I R And I B 。
S5, respectively aligning the reconstructed signals I R And I B Filtering with 0.6-3 HZ band-pass filter to extract frequency signal S related to human artery blood flow volume pulse (BVP) R And S B By calculating S R And S B Standard deviation of (A) to obtain the AC variable AC R And AC B (ii) a By calculating S R And S B Obtaining the direct current variable DC R And DC B 。
S6, calculating to obtain the blood oxygen saturation SPO2 according to the Lambert-beer law, wherein the formula is as follows:
SPO2＝A+B*R
wherein the content of the first and second substances,
A. b can be obtained by performing least square linear fitting on the obtained R sequence and an oximeter reference value, and then the blood oxygen saturation can be predicted based on the value.
The method of the present invention was tested as shown in fig. 4. In this example, 8 subjects, 4 females and 4 males were selected, and the experimental environment and procedure were as follows: under natural light, the room temperature is 23 ℃, a subject sits at a position which is about 50cm away from a camera and keeps a natural breathing state, a facial video which lasts for 10s is recorded as sample data, and the method provided by the invention is adopted to measure and calculate the blood oxygen saturation based on the sample data. The recording was performed by holding the subject with a common finger clip oximeter (standard for medical instruments) and recording the average of the blood oxygen saturation values over 10s as a reference. The result shows that the measurement precision of the method provided by the invention reaches a better standard.
As shown in fig. 5, one of the subjects was selected for breath-hold test without loss of generality. The experimental environment was the same as above except that the subject was allowed to breathe naturally for 20s, and held for 30s from 21s while the subject's blood oxygen saturation value was recorded as a reference value by holding a common finger clip oximeter. The result also shows that the measurement precision of the method provided by the invention reaches a better standard.
It should be noted that the present disclosure is not limited to the foregoing embodiments and may be appropriately changed without departing from the spirit of the present disclosure; for example, different regions of interest are selected on the face in S101; for example, the band-pass filtering in S104 selects a suitable frequency range, etc.
While the invention has been shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the appended claims.
Claims (3)
1. A non-contact human body blood oxygen saturation detection method is characterized by comprising the following steps:
s1, acquiring a video, and acquiring human face video data;
s2, obtaining an image area, namely intercepting each frame of image of the video in the step S1, and selecting two cheek parts of the face from the video frame image as an ROI (region of interest);
s3, obtaining an original signal, and extracting the average pixel value P of the red channel of the ROI in the step S2 R And blue channel average pixel value P B As the original pulse wave signal;
s4, obtaining the separation signals, and respectively carrying out the pulse wave red channel signals P obtained in the step S3 by adopting a single-channel independent component analysis algorithm based on dynamic embedding R And blue channel signal P B Carrying out blind source separation to obtain a reconstructed separation signal I R And I B ；
S5, obtaining variables, and respectively extracting the reconstruction signals I obtained in the step S4 R And I B AC variable AC R 、AC B And a direct current variable DC R 、DC B ；
S6, result acquisition, based on the AC extracted in the step S5 R 、AC B And DC R 、DC B Calculating a blood oxygen saturation parameter R, and obtaining blood oxygen saturation SPO2 according to the Lambert-beer law;
the step S4 further includes the steps of:
s41, selecting the embedding dimension m and the time delay delta, and respectively adding the P acquired in the step S3 R And P B Performing phase space reconstruction to form two groups of multidimensional vectors;
s42, when m is larger, firstly reducing the dimension of the obtained embedded matrix P to k by using a rapid ICA algorithm, and then performing blind source separation on the matrix subjected to dimension reduction by using the rapid ICA algorithm to separate an independent component Q;
s43, extracting independent component Q related to BVP from Q j Multiplying by the column vector B of the corresponding mixing matrix B j Obtaining independent sub-components P j ＝∑b i Q i I ∈ j, j is a set of independent components related to the volume pulse BVP of the arterial blood of the human body;
s44, for independent sub-component P j Performing inverse reconstruction to obtain final one-dimensional separation signal
Said step S42 further comprises separating out the independent components Q = AP, Q = [ Q ] 1 ,Q 2 ,...,Q n ] T A is an n × k dimensional unmixing matrix, P is a multidimensional vector, n is the number of separated independent signals, and n = k;
estimate the mixing matrix B = pinv (a), B = [ B ] 1 ,b 2 ,...,b k ] T Pinv represents the pseudo-inverse;
the method for selecting the embedding dimension m and the time delay delta in the step S41 includes:
the time delay delta is taken as 1, the embedding dimension m is set according to the sampling frequency of the observation signal and the minimum frequency of the source signal,
wherein fps is an observation signal P R And P B The sampling frequency of (a);
the fps is the frame rate of the camera and is 30 frames/second; f. of L Taking the minimum frequency of the separated source signals, wherein m is 30-60;
and in the step S42, when the dimension of the obtained embedded matrix P is reduced by using the fast ICA algorithm, selecting the dimension k after the dimension reduction based on the eigenvalue contribution rate cumulant.
2. The method as claimed in claim 1, wherein the step S5 is performed by extracting a reconstructed signal I R And I B AC variable AC R 、AC B And a direct current variable DC R 、DC B The method comprises the following steps:
for the reconstructed signal I R And I B Filtering by a band-pass filter of 0.6-3 HZ to extract the BVP of arterial blood volume pulseCorrelated frequency signal S R And S B ；
By calculating S R And S B Standard deviation of (A) to obtain the AC variable AC R And AC B ；
By calculating S R And S B Obtaining the direct current variable DC R 、DC B 。
3. The method as claimed in claim 1, wherein the calculating method of the blood oxygen saturation value in step S6 comprises:
SPO2＝A+B*R
wherein the content of the first and second substances,
A. and B is obtained by performing least square linear fitting on the obtained R sequence and the oximeter reference value.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107358220A (en) * | 2017-07-31 | 2017-11-17 | 江西中医药大学 | A kind of human heart rate and the contactless measurement of breathing |
| CN107773223A (en) * | 2016-08-24 | 2018-03-09 | 上海佑壳尔科技有限公司 | A kind of online finger instrument for obtaining pulse wave and acquisition pulse wave peak shape parameter method |
| CN111444797A (en) * | 2020-03-16 | 2020-07-24 | 五邑大学 | Non-contact heart rate detection method |
| CN111714105A (en) * | 2020-07-24 | 2020-09-29 | 长春理工大学 | Human vital sign perception system based on IPPG |
| CN112237421A (en) * | 2020-09-23 | 2021-01-19 | 浙江大学山东工业技术研究院 | Video-based dynamic heart rate variability analysis model |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6701170B2 (en) * | 2001-11-02 | 2004-03-02 | Nellcor Puritan Bennett Incorporated | Blind source separation of pulse oximetry signals |
| CN101940475A (en) * | 2010-09-03 | 2011-01-12 | 深圳市纽泰克电子有限公司 | Method for improving detection accuracy of blood oxygen saturation |
| TWI489325B (en) * | 2011-09-21 | 2015-06-21 | Pixart Imaging Inc | Optical finger mouse, electronic device and physiological characteristic detection device |
| CN107456219B (en) * | 2017-09-07 | 2020-10-30 | 成都云卫康医疗科技有限公司 | Dynamic heart rate and blood oxygen measurement method based on Pearson correlation coefficient |
| CN110236515A (en) * | 2019-07-19 | 2019-09-17 | 合肥工业大学 | A kind of contactless heart rate detection method based on near-infrared video |
| CN110755090A (en) * | 2019-11-15 | 2020-02-07 | 合肥师范学院 | Human body blood oxygen saturation measuring method based on network camera |
| CN112116922B (en) * | 2020-09-17 | 2024-04-12 | 集美大学 | Noise blind source signal separation method, terminal equipment and storage medium |
| CN112200099A (en) * | 2020-10-14 | 2021-01-08 | 浙江大学山东工业技术研究院 | Video-based dynamic heart rate detection method |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107773223A (en) * | 2016-08-24 | 2018-03-09 | 上海佑壳尔科技有限公司 | A kind of online finger instrument for obtaining pulse wave and acquisition pulse wave peak shape parameter method |
| CN107358220A (en) * | 2017-07-31 | 2017-11-17 | 江西中医药大学 | A kind of human heart rate and the contactless measurement of breathing |
| CN111444797A (en) * | 2020-03-16 | 2020-07-24 | 五邑大学 | Non-contact heart rate detection method |
| CN111714105A (en) * | 2020-07-24 | 2020-09-29 | 长春理工大学 | Human vital sign perception system based on IPPG |
| CN112237421A (en) * | 2020-09-23 | 2021-01-19 | 浙江大学山东工业技术研究院 | Video-based dynamic heart rate variability analysis model |
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