CN113951855A - Non-contact heart rate measuring method based on human face - Google Patents
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Abstract
The invention relates to the technical field of micro-expression recognition and discloses a non-contact heart rate measuring method based on a human face, which comprises the following steps: s1: acquiring data, and acquiring a video frame and a timestamp of the video frame through a usb camera; s2: determining an interested area, and determining the interested area according to the size of the first frame video frame; s3: spatial filtering, namely performing gold-tower multi-resolution decomposition on the video sequence; carrying out spatial filtering on the video sequence to obtain different spatial frequency base bands easily; s4: and (4) time domain filtering. According to the invention, the measured interested area is positioned according to the size of the first frame image by the video frame obtained by the usb camera, and the tester moves the interested area in the face to the positioned interested area, so that the consumption of hardware resources is reduced, the cost is saved, extremely tiny color or motion changes which are difficult to perceive by the face in the video can be captured, and the weak pulse changes which are difficult to perceive are amplified for convenient observation and measurement.
Description
Technical Field
The invention relates to the technical field of micro-expression recognition, in particular to a non-contact heart rate measuring method based on human faces.
Background
The heart rate (also known as quiet heart rate) refers to the number of heart beats per minute in a quiet state of a normal person, generally 60-100 times/minute, and can generate individual difference due to age, gender or other physiological factors, generally speaking, the smaller the age, the faster the heart rate, the slower the heart beat of an old person is compared with a young person, the faster the heart rate of a female person is compared with a male person of the same age, heart rate variation is closely related to heart diseases, if the heart rate is too fast or too slow, detailed examination should be performed as soon as possible, and the heart rate variation is closely related to the heart diseases. If the heart rate is more than 160 times/minute or less than 40 times/minute, most patients with heart diseases are often accompanied by discomfort such as palpitation and chest distress, and detailed examination should be performed early to treat the disease.
The heart rate is taken as an important physiological parameter of a human body, the human body health can be directly and effectively reflected, the heart rate monitoring of the human body health plays an irreplaceable role, the current heart rate monitoring mode is mainly contact, but the heart rate monitoring mode is complex to operate, needs to be in contact with the skin of the human body for a long time in the detection process, causes certain inconvenience and discomfort to a measurer, and cannot meet the requirements of people.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a non-contact heart rate measuring method based on a human face, and mainly aims to solve the problems that the existing heart rate monitoring method is complex to operate, needs to be in contact with the skin of a human body for a long time in the detection process, causes certain inconvenience and discomfort to a measurer and cannot meet the requirements of people.
(II) technical scheme
In order to achieve the purpose, the invention provides the following technical scheme:
the non-contact heart rate measuring method based on the human face comprises the following steps:
s1: acquiring data, and acquiring a video frame and a timestamp of the video frame through a usb camera;
s2: determining an interested area, and determining the interested area according to the size of the first frame video frame;
s3: spatial filtering, namely performing gold-tower multi-resolution decomposition on the video sequence; carrying out spatial filtering on the video sequence to obtain different spatial frequency base bands easily;
s4: time domain filtering, namely performing time domain band-pass filtering on the image of each scale to obtain a plurality of interested frequency bands, performing time domain band-pass filtering on each baseband after obtaining the baseband with different spatial frequencies, and extracting the part of interested change signals;
s5: amplifying the filtering result, and performing Taylor series difference approximation on the signal of each frequency band to linearly amplify the approximation result;
s6: synthesizing an image, synthesizing the amplified image;
s7: judging the stability, and judging the stability of the input signal;
s8: trend analysis, which is to obtain stable signals and perform trend separation;
s9: calculating and correcting, setting a threshold value A for the absolute value of the difference between heart rate values appearing twice continuously in an input heart rate signal, if the difference between the obtained heart rate values is within the threshold value A, updating the current heart rate value to be the obtained heart rate value, recording data for m times continuously when the measured heart rate values of two times continuously are larger than the threshold value A, updating the current heart rate value when the data for m times continuously and the current heart rate value are larger than the threshold value A and the difference value between every two data for m times is smaller than or equal to the threshold value A, taking the average value of the current m times as the current heart rate value, and otherwise, taking the heart rate value of the previous frame to improve the accuracy of heart rate measurement.
As a still further aspect of the present invention, the image in S3 exhibits different SNRs (signal-to-noise ratios) at different spatial frequencies, and the lower the spatial frequency, the higher the signal-to-noise ratio.
Further, the image with higher spatial frequency in S3 may be difficult to approximate by taylor series expansion, because in this case, the result of the approximation is confused and the direct amplification is significantly distorted.
On the basis of the scheme, when the heart rate signal needs to be amplified in S4, 0.4-4 Hz (24-240 bpm) can be selected for band-pass filtering.
In a further aspect of the present invention, in S7, when the standard deviation of the signal is less than or equal to the amplification factor multiplied by 0.045+1, the signal is determined to be stable, otherwise, the signal is determined to be unstable, and the signal is determined to be unstable as a noise signal.
Further, when the trend separation is performed in S8, the low frequency signal is removed, the trend-removed signal is normalized and mean-filtered, the peak value of the signal is obtained by detection, the peak value is detected, and the heart rate value is calculated according to the detected peak value, so that the data representativeness is improved.
Based on the above scheme, in S9, since the human heart rate ranges from 40bpm to 240bpm, when the calculated heart rate is less than 40bpm, the heart rate greater than 240bpm is considered as a noise signal.
(III) advantageous effects
Compared with the prior art, the invention provides a non-contact heart rate measuring method based on human faces, which has the following beneficial effects:
1. the heart rate is measured in a static state based on heart rate measurement, the measured interesting area is positioned according to the size of the first frame image through the video frame obtained by the usb camera, and the tester moves the interesting area in the face to the positioned interesting area, so that the consumption of hardware resources is reduced, and the cost is saved.
2. The invention can capture extremely tiny color or motion change which is difficult to be perceived by human faces in videos, and magnifies the weak pulse change which is difficult to be perceived so as to be convenient for observation and measurement.
3. The method can correct the heart rate value measured and calculated, improves the accuracy of heart rate measurement, and the baseband should use different amplification factors, the topmost image, namely the image with the lowest spatial frequency and the highest signal-to-noise ratio, can use the largest amplification factor, and the amplification factors of the next layer are sequentially reduced, thereby being beneficial to reducing noise.
4. The image signal can be approached conveniently through the spatial filtering step, and subsequent time sequence analysis (heart rate extraction) can be conveniently carried out on the amplification result through selecting the band-pass filter.
Drawings
Fig. 1 is a schematic flow structure diagram of a non-contact heart rate measurement method based on a human face according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1, the non-contact heart rate measuring method based on human face includes the following steps:
s1: acquiring data, and acquiring a video frame and a timestamp of the video frame through a usb camera;
s2: the method comprises the steps of determining an interested area, determining the interested area according to the size of a first frame video frame, measuring the heart rate basically in a static environment, and firstly using a face detection algorithm to relocate the heart rate to the interested area in the traditional heart rate measurement;
s3: spatial filtering, namely performing gold-tower multi-resolution decomposition on the video sequence; carrying out spatial filtering on the video sequence to obtain different spatial frequency base bands easily;
s4: time domain filtering, namely performing time domain band-pass filtering on the image of each scale to obtain a plurality of interested frequency bands, performing time domain band-pass filtering on each baseband after obtaining the baseband with different spatial frequencies, and extracting the part of interested change signals;
s5: amplifying the filtering result, and performing Taylor series difference approximation on the signal of each frequency band to linearly amplify the approximation result;
s6: synthesizing images, synthesizing the amplified images, wherein the video amplification is to capture extremely tiny color or motion changes which are difficult to be perceived by human faces in videos, and amplify the weak pulse changes which are difficult to be perceived so as to be convenient for observation and measurement;
s7: judging the stability, and judging the stability of the input signal;
s8: trend analysis, which is to obtain stable signals and perform trend separation;
s9: calculating and correcting, setting a threshold value A for the absolute value of the difference between heart rate values appearing twice continuously in an input heart rate signal, if the difference between the obtained heart rate values is within the threshold value A, updating the current heart rate value to be the obtained heart rate value, recording data for 3 times continuously when the measured heart rate values of two times continuously are larger than the threshold value A, updating the current heart rate value when the data for 3 times continuously and the current heart rate value are larger than the threshold value A and the difference value between every two data for 3 times is smaller than or equal to the threshold value A, taking the average value of the current 3 times as the current heart rate value, and otherwise, taking the heart rate value of the previous frame to improve the accuracy of heart rate measurement.
The image in S3 of the present invention exhibits different SNRs (signal-to-noise ratios) at different spatial frequencies, the lower the spatial frequency, the higher the signal-to-noise ratio, to prevent distortion, therefore, these base bands should use different magnification, the top most image, i.e., the lowest spatial frequency, highest signal-to-noise ratio image, the largest magnification may be used, with the next layer of successively smaller magnifications, contributing to noise reduction, the higher spatial frequency image in S3 may be difficult to approximate with a taylor series expansion, since in this case the results of the approximation are mixed up, the direct amplification is significantly distorted, the image signal is convenient to approach through the spatial filtering step, when the heart rate signal needs to be amplified at S4, the band-pass filtering can be carried out by selecting 0.4-4 Hz (24-240 bpm), by selecting the band-pass filter, subsequent time sequence analysis (heart rate extraction) can be conveniently carried out on the amplification result.
It should be noted that, in S7, when the standard deviation of the signal is equal to or less than the amplification times multiplied by 0.045+1, the signal is determined to be stable, otherwise, the signal is unstable and is regarded as a noise signal, when trend separation is performed in S8, the low-frequency signal is removed, the signal after trend removal is normalized and mean filtered, the peak value of the signal is obtained through detection, the peak value is detected, and then a heart rate value is calculated according to the detected peak value, so that the data representativeness is improved, in S9, because the range of the human heart rate is 40bpm to 240bpm, when the calculated and measured heart rate is less than 40bpm, and when the calculated and measured heart rate is greater than 240bpm, the signal is regarded as a noise signal.
Example 2
Referring to fig. 1, the non-contact heart rate measuring method based on human face includes the following steps:
s1: acquiring data, and acquiring a video frame and a timestamp of the video frame through a usb camera;
s2: the method comprises the steps of determining an interested area, determining the interested area according to the size of a first frame video frame, measuring the heart rate basically in a static environment, and firstly using a face detection algorithm to relocate the heart rate to the interested area in the traditional heart rate measurement;
s3: spatial filtering, namely performing gold-tower multi-resolution decomposition on the video sequence; carrying out spatial filtering on the video sequence to obtain different spatial frequency base bands easily;
s4: time domain filtering, namely performing time domain band-pass filtering on the image of each scale to obtain a plurality of interested frequency bands, performing time domain band-pass filtering on each baseband after obtaining the baseband with different spatial frequencies, and extracting the part of interested change signals;
s5: amplifying the filtering result, performing Taylor series difference approximation on the signal of each frequency band, linearly amplifying the approximation result, wherein the video amplification is to capture extremely tiny color or motion change which is difficult to be perceived by human faces in the video, and amplify the weak pulse change which is difficult to be perceived so as to be convenient for observation and measurement;
s6: synthesizing images, synthesizing the amplified images, wherein the video amplification is to capture extremely tiny color or motion changes which are difficult to be perceived by human faces in videos, and amplify the weak pulse changes which are difficult to be perceived so as to be convenient for observation and measurement;
s7: judging the stability, and judging the stability of the input signal;
s8: trend analysis, which is to obtain stable signals and perform trend separation;
s9: calculating and correcting, setting a threshold value A for the absolute value of the difference between the heart rate values appearing twice continuously in the input heart rate signal, if the difference between the obtained heart rate values is within the threshold value A, updating the current heart rate value to be the obtained heart rate value, recording the data for 5 times continuously when the measured heart rate values appearing twice continuously are larger than the threshold value A, updating the current heart rate value when the data for 5 times continuously and the current heart rate value are larger than the threshold value A and the difference value between every two data for 5 times is smaller than or equal to the threshold value A, taking the average value of the current 5 times as the current heart rate value, and otherwise, taking the heart rate value of the previous frame to improve the accuracy of heart rate measurement.
The image in S3 of the present invention exhibits different SNRs (signal-to-noise ratios) at different spatial frequencies, the lower the spatial frequency, the higher the signal-to-noise ratio, to prevent distortion, therefore, these base bands should use different magnification, the top most image, i.e., the lowest spatial frequency, highest signal-to-noise ratio image, the largest magnification may be used, with the next layer of successively smaller magnifications, contributing to noise reduction, the higher spatial frequency image in S3 may be difficult to approximate with a taylor series expansion, since in this case the results of the approximation are mixed up, the direct amplification is significantly distorted, the image signal is convenient to approach through the spatial filtering step, when the heart rate signal needs to be amplified at S4, the band-pass filtering can be carried out by selecting 0.4-4 Hz (24-240 bpm), by selecting the band-pass filter, subsequent time sequence analysis (heart rate extraction) can be conveniently carried out on the amplification result.
It should be noted that, in S7, when the standard deviation of the signal is equal to or less than the amplification times multiplied by 0.045+1, the signal is determined to be stable, otherwise, the signal is unstable and is regarded as a noise signal, when trend separation is performed in S8, the low-frequency signal is removed, the signal after trend removal is normalized and mean filtered, the peak value of the signal is obtained through detection, the peak value is detected, and then a heart rate value is calculated according to the detected peak value, so that the data representativeness is improved, in S9, because the range of the human heart rate is 40bpm to 240bpm, when the calculated and measured heart rate is less than 40bpm, and when the calculated and measured heart rate is greater than 240bpm, the signal is regarded as a noise signal.
In the description herein, it is noted that relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. The non-contact heart rate measuring method based on the human face is characterized by comprising the following steps of:
s1: acquiring data, and acquiring a video frame and a timestamp of the video frame through a usb camera;
s2: determining an interested area, and determining the interested area according to the size of the first frame video frame;
s3: spatial filtering, namely performing gold-tower multi-resolution decomposition on the video sequence; carrying out spatial filtering on the video sequence to obtain different spatial frequency base bands easily;
s4: time domain filtering, namely performing time domain band-pass filtering on the image of each scale to obtain a plurality of interested frequency bands, performing time domain band-pass filtering on each baseband after obtaining the baseband with different spatial frequencies, and extracting the part of interested change signals;
s5: amplifying the filtering result, and performing Taylor series difference approximation on the signal of each frequency band to linearly amplify the approximation result;
s6: synthesizing an image, synthesizing the amplified image;
s7: judging the stability, and judging the stability of the input signal;
s8: trend analysis, which is to obtain stable signals and perform trend separation;
s9: calculating and correcting, setting a threshold value A for the absolute value of the difference between heart rate values appearing twice continuously in an input heart rate signal, if the difference between the obtained heart rate values is within the threshold value A, updating the current heart rate value to be the obtained heart rate value, recording data for m times continuously when the measured heart rate values of two times continuously are larger than the threshold value A, updating the current heart rate value when the data for m times continuously and the current heart rate value are larger than the threshold value A and the difference value between every two data for m times is smaller than or equal to the threshold value A, taking the average value of the current m times as the current heart rate value, and otherwise, taking the heart rate value of the previous frame to improve the accuracy of heart rate measurement.
2. The non-contact human face-based heart rate measurement method according to claim 1, wherein the image in S3 shows different SNRs (signal-to-noise ratios) at different spatial frequencies, and the lower the spatial frequency, the higher the signal-to-noise ratio.
3. The non-contact human-face-based heart rate measurement method according to claim 2, wherein the image with higher spatial frequency in S3 is difficult to approximate by taylor series expansion, because in this case, the result of approximation is mixed up and the direct amplification is obviously distorted.
4. The non-contact human face-based heart rate measurement method as claimed in claim 1, wherein in the step S4, when the heart rate signal needs to be amplified, 0.4-4 Hz (24-240 bpm) can be selected for band-pass filtering.
5. The method for non-contact human-face-based heart rate measurement according to claim 1, wherein in S7, when the standard deviation of the signal is less than or equal to the amplification factor multiplied by 0.045+1, the signal is determined to be stable, otherwise, the signal is determined to be unstable, and the signal is determined to be unstable as a noise signal.
6. The non-contact human face-based heart rate measurement method according to claim 1, wherein when trend separation is performed in S8, low-frequency signals are removed, the trend-removed signals are normalized and mean-filtered, peak values of the signals are obtained through detection, the peak values are detected, and then a heart rate value is calculated according to the detected peak values, so that data representativeness is improved.
7. The non-contact human face-based heart rate measurement method as claimed in claim 1, wherein in S9, since the human heart rate ranges from 40bpm to 240bpm, when the calculated heart rate is less than 40bpm, the heart rate greater than 240bpm is considered as a noise signal.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116999044A (en) * | 2023-09-07 | 2023-11-07 | 南京云思创智信息科技有限公司 | Real-time motion full-connection bidirectional consistent optical flow field heart rate signal extraction method |
| CN116999044B (en) * | 2023-09-07 | 2024-04-16 | 南京云思创智信息科技有限公司 | Real-time motion full-connection bidirectional consistent optical flow field heart rate signal extraction method |
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