CN110236514A - The real-time heart rate detection method that mode based on video is extracted and median filtering combines - Google Patents

The real-time heart rate detection method that mode based on video is extracted and median filtering combines Download PDF

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Publication number
CN110236514A
CN110236514A CN201910629610.0A CN201910629610A CN110236514A CN 110236514 A CN110236514 A CN 110236514A CN 201910629610 A CN201910629610 A CN 201910629610A CN 110236514 A CN110236514 A CN 110236514A
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umid
frame
heart rate
real
channel
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厉阳晨
倪瑶
周梅
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East China Normal University
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East China Normal University
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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
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/7475—User input or interface means, e.g. keyboard, pointing device, joystick
    • A61B5/748—Selection of a region of interest, e.g. using a graphics tablet
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/04—Babies, e.g. for SIDS detection

Abstract

The mode based on video that the invention discloses a kind of is extracted and the real-time heart rate detection method of median filtering combination, comprising the following steps: opens camera, shoots face video；Human-face detector is created, each frame human face region is obtained, therefrom intercept face area-of-interest and obtains its RGB channel value；RGB channel is converted into the channel YUV, extracts each channel frame two dimension U, forms three-dimensional U access matrix；The method combined with median filtering is extracted using mode and handles the channel U, characteristic value is calculated, obtains the one-dimensional characteristic sequence on time shaft；Using Butterworth LPF to one-dimensional characteristic sequential filtering, heart rate signal is obtained；Beats are obtained with peak-valley counting method, conversion obtains one minute heart rate value；Acquisition more new data in real time, repeats the above steps to obtain real-time heart rate value.The present invention improves signal-to-noise ratio in signal extraction range, increases data reliability；On characteristics extraction, influence of the noise to useful signal is reduced, improves the accuracy that heart rate value calculates.

Description

The real-time heart rate detection method that mode based on video is extracted and median filtering combines
Technical field
The present invention relates to digital image processing techniques fields, in particular to the mode based on video is extracted and median filtering knot The real-time heart rate detection method closed.
Background technique
The heart rate vital sign information important as human body, be detect cardiovascular disease and guidance science take exercise it is important Parameter information.Most traditional heart rate detection is obtained in such a way that doctor is using feeling the pulse or auscultation, this heart rate measurement Need doctor that there is relevant knowledge abundant and a large amount of practical experience.With the development of science and technology, people have invented rhythm of the heart Instrument measures the heart rate of people, but the cost of heart rate monitor is very high, is generally only used for the clinical monitoring of hospital, it is difficult to enter people Daily life in.Then, finger clamping type oximeter etc. appear in measurement accuracy rate it is high on the basis of greatly reduce and set Standby cost, and use is more convenient, and still, the use of finger clamping type oximeter must have with human body directly to be contacted, for a long time Contact will cause the discomfort of subject, thus be not suitable for prolonged heart rate measurement.
Popularizing with computer, camera etc. in recent years, image PPG (Photo plethysmography) technology It proposes to realize that noninvasive, non-contacting real-time heart rate measurement provides practicable thinking.Image PPG technology refers to, due to For human heart in constantly contraction and diastole, the filling degree that will lead to the blood in the blood vessel of people also can be continuous with heartbeat Ground changes, and can show with the variation of volumetric blood to the absorption of light and change with the consistent pulsating nature of heartbeat, together When skin surface reflection the intensity of light corresponding cyclically-varying can also occur, to show as the variation of skin color.
Summary of the invention
The purpose of the present invention is to provide the modes based on video to extract the real-time heart rate detection side combined with median filtering Method, this method can effectively improve real-time heart rate detection precision and stability based on camera.
Realizing the specific technical solution of the object of the invention is:
The real-time heart rate detection method that mode based on video is extracted and median filtering combines, the described method comprises the following steps:
(1) camera is opened, includes the video of face with the frame per second shooting 20s not less than 7Hz；
(2) human-face detector is created, each frame human face region is obtained, therefrom intercepts face area-of-interest, and obtain interested The RGB channel value in region；
(3) area-of-interest RGB channel is converted into the channel YUV, extracts the two-dimentional channel U of each frame, form the three-dimensional channel U square Battle array；
(4) the U access matrix that the method combined with median filtering handles each frame is extracted using mode, calculates each frame feature Value, obtains the one-dimensional characteristic sequence on time shaft；
(5) cutoff frequency is used to be filtered for the Butterworth LPF of 3Hz to one-dimensional characteristic sequence, it will be filtered One-dimensional characteristic sequence is as heart rate signal；
(6) beats in 20s are obtained using peak-valley counting method, the real-time heart rate value of the 20s is obtained according to time scale；
(7) acquisition more new data, repetition step (2)-(6) obtain real-time heart rate value in real time.
The step (1) specifically:
The camera object is created, frame per second fs, fs >=7Hz are set；
Frame counter frame_counter=0 is set；
Note present frame is Frame (i), and the frame per second shooting with fs includes video 20s, frame_counter=K=20*fs of face.
The step (2) specifically:
The human-face detector is created using viola-Jones calculus, obtains the starting point coordinate (x, y) and face of human face region Size (w, h)；
According to human face ratio, interception Frame (i) altitude range is x+0.5*h ~ x+0.7*h, and width range is y+0.1*w ~ y+ The region of 0.3*w, is denoted as Interest (i), as area-of-interest, obtains the RGB channel of the area-of-interest.
The step (3) specifically:
The RGB channel of the area-of-interest is converted into the channel YUV, the two-dimentional U for extracting area-of-interest described in each frame is logical Road, U path computation formula are as follows:
U = -0.169*R - 0.331*G + 0.5 *B
The two-dimentional channel U of each frame in the 20s is obtained, the three-dimensional U channel sequence U (1), U (2) ..., U are formed It (K), is M row N column.
The step (4) specifically:
To each frame two dimension U channel U (i) of the area-of-interest into line scans, the channel each row U mode, shape are calculated separately At the channel U mode sequence Umost (1), Umost (2) ..., Umost (M)；
To the channel U mode sequence Umost (1), Umost (2) ..., Umost (M) is ranked up, and is extracted intermediate value Umid (i), is made For the characteristic value of each frame of the area-of-interest；
Each frame eigenvalue cluster at the one-dimensional characteristic sequence Umid (1) on the time shaft, Umid (2) ..., Umid (K).
The step (5) specifically:
Using P rank 3Hz Butterworth filter to the one-dimensional characteristic sequence Umid (1), Umid (2) ..., Umid (K) It is filtered, obtains the filtered sequence Fil (1), Fil (2) ..., Fil (J) that length is J；
Wherein, J=K+P-1.
The step (6) specifically:
Filtered sequence Fil (1), Fil (2) ..., the Fil (J) that the length is J are scanned, peak value: the Fil is found (1) calculation processing is not done；The Fil (2), Fil (3) ..., Fil (J-1) is compared with adjacent two o'clock, if the point value It is bigger than adjacent two o'clock, then it is assumed that the point is a peak value；That is: if Fil (j) > Fil (j-1) and Fil (j) > Fil (j+1), Fil (j) is a peak point；
Peak point number in the 20s is counted, Peak_20s is denoted as；
Calculate Heart_Rate=Peak_20s*3, i.e., the corresponding real-time heart rate value of described 20s.
The step (7) specifically:
Abandon the preceding 5s data Umid (1) in the 20s, Umid (2) ... Umid (L1), wherein L1=fs*5, rear 15s number According to forming interim sequence Umid (1), Umid (2) ... Umid (L2), wherein L2=fs*15；
Acquisition 5s data are updated, replenish the interim sequence Umid (1), after Umid (2) ... Umid (L2), thus real Existing sequence Umid (1), the update of Umid (2) ... Umid (K), K=L1+L2 repeat step (2) ~ (6) to get the real-time heart is arrived Rate value.
The beneficial effect of the technical scheme provided by the present invention is that: the mode proposed by the invention based on video is extracted in The real-time heart rate detection method that value filtering combines, the method determine specific sense according to face characteristic in signal extraction range Interest regional scope improves signal-to-noise ratio, increases data reliability；On characteristics extraction, by by each frame U channel value according to The mode of row scanning takes mode, takes intermediate value according still further to the mode of column scan, to reduce influence of the noise to useful signal, improves The accuracy that heart rate value calculates.The scene for the real-time heart rate detection of baby that the present invention is suitable for directly contacting, is also applied for Heart rate detection is carried out to the crowd of sitting.
Detailed description of the invention
Fig. 1 is flow chart of the present invention；
Fig. 2 is that the present invention extracts three-dimensional U access matrix flow chart；
Fig. 3 is that the present invention extracts video stream characteristics sequence flow figure；
Fig. 4 is the flow chart that the present invention is filtered characteristic sequence using 3Hz Butterworth LPF；
Fig. 5 is the flow chart that the present invention calculates heart rate value using peak-valley counting method；
Fig. 6 is the present invention flow chart that heart rate updates in real time.
Specific embodiment
Illustrate technological means, technological improvement and beneficial effect of the present invention in order to be more clearly understood, ties below Closing attached drawing, the present invention will be described in detail.
Embodiment
Refering to fig. 1-6, the present invention the following steps are included:
S101: opening camera, includes the video of face with the frame per second shooting 20s not less than 7Hz.Below with 20 years old healthy male For be illustrated.
The step specifically:
The camera object is created, frame per second fs is set, for example, fs=10Hz；
Frame counter frame_counter=0 is set；
Note present frame is Frame (i), and the frame per second shooting with fs includes the video 20s of face, frame_counter=K=200.
S102: creation human-face detector obtains each frame human face region, therefrom intercepts face area-of-interest, and obtain The RGB channel value of area-of-interest.
The step specifically:
The human-face detector is created using viola-Jones calculus, obtains the starting point coordinate (x, y) and face of human face region Size (w, h)；For example, starting point coordinate is (100,150), facial size is (200,300).
According to human face ratio, intercepting Frame (i) altitude range is 200 ~ 250, the region that width range is 170 ~ 210, It is denoted as Interest (i), as area-of-interest, obtains the RGB channel of the area-of-interest.
S103: being converted to the channel YUV for area-of-interest RGB channel, extracts the two-dimentional channel U of each frame, forms three-dimensional U Access matrix, refering to Fig. 2.
The step specifically:
The RGB channel of the area-of-interest is converted into the channel YUV, the two-dimentional U for extracting area-of-interest described in each frame is logical Road, U path computation formula are as follows:
U = -0.169*R - 0.331*G + 0.5 *B
The two-dimentional channel U of each frame in the 20s is obtained, each U access matrix is 50 rows 40 column, forms the three-dimensional channel U Sequence U (1), U (2) ..., U (200).
The method that S104: being extracted using mode and median filtering combines handles the U access matrix of each frame, calculates each Frame characteristic value obtains the one-dimensional characteristic sequence on time shaft, refering to Fig. 3.
The step specifically:
To each frame two dimension U channel U (i) of the area-of-interest into line scans, the channel each row U mode, shape are calculated separately At the channel U mode sequence Umost (1), Umost (2) ..., Umost (50)；
To the channel U mode sequence Umost (1), Umost (2) ..., Umost (50) is ranked up, and is extracted intermediate value Umid (i), Characteristic value as each frame of the area-of-interest；
Each frame eigenvalue cluster at the one-dimensional characteristic sequence Umid (1) on the time shaft, Umid (2) ..., Umid (200).
S105: using cutoff frequency to be filtered for the Butterworth LPF of 3Hz to one-dimensional characteristic sequence, will filter One-dimensional characteristic sequence after wave is as heart rate signal, refering to Fig. 4.
The step specifically:
Using 30 rank 3Hz Butterworth filters to the one-dimensional characteristic sequence Umid (1), Umid (2) ..., Umid (200) it is filtered, obtains the filtered sequence Fil (1), Fil (2) ..., Fil (229) that length is J；
S106: obtaining beats in 20s using peak-valley counting method, obtains the real-time heart rate value of the 20s according to time scale, As the real-time heart rate value of the 20s, refering to Fig. 5.
The step specifically:
Filtered sequence Fil (1), Fil (2) ..., the Fil (229) that the length is J are scanned, peak value: the Fil is found (1) calculation processing is not done；The Fil (2), Fil (3) ..., Fil (228) is compared with adjacent two o'clock, if the point value It is bigger than adjacent two o'clock, then it is assumed that the point is a peak value；That is: if Fil (j) > Fil (j-1) and Fil (j) > Fil (j+1), Fil (j) is a peak point；
Peak point number in the 20s is counted, Peak_20s, such as Peak_20s=30 are denoted as；
Calculate corresponding real-time heart rate value Heart_Rate=Peak_20s*3=90 the 20s, i.e. implementation of the people in the 20s Heart rate value is 90 beats/min.
S107: 5s data before abandoning update 5s data, repeat the above steps and calculate Current heart rate value, refering to Fig. 6.
The step specifically:
The preceding 5s data in the 20s are abandoned, Umid (1), Umid (2) ... Umid (50), rear 15s data composition is temporarily Sequence Umid (1), Umid (2) ... Umid (150)；
Acquisition 5s data are updated, replenish the interim sequence Umid (1), after Umid (2) ... Umid (150), thus It realizes sequence Umid (1), Umid (2) ... the update of Umid (200).Step (2) ~ (6) are repeated to get real-time heart rate is arrived Value.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.

Claims (8)

  1. The real-time heart rate detection method that 1. mode based on video is extracted and median filtering combines, which is characterized in that the method The following steps are included:
    (1) camera is opened, includes the video of face with the frame per second shooting 20s not less than 7Hz；
    (2) human-face detector is created, each frame human face region is obtained, therefrom intercepts face area-of-interest, and obtain interested The RGB channel value in region；
    (3) area-of-interest RGB channel is converted into the channel YUV, extracts the two-dimentional channel U of each frame, form the three-dimensional channel U square Battle array；
    (4) the U access matrix that the method combined with median filtering handles each frame is extracted using mode, calculates each frame feature Value, obtains the one-dimensional characteristic sequence on time shaft；
    (5) cutoff frequency is used to be filtered for the Butterworth LPF of 3Hz to one-dimensional characteristic sequence, it will be filtered One-dimensional characteristic sequence is as heart rate signal；
    (6) beats in 20s are obtained using peak-valley counting method, the real-time heart rate value of the 20s is obtained according to time scale；
    (7) acquisition more new data, repetition step (2)-(6) obtain real-time heart rate value in real time.
  2. 2. real-time heart rate detection method according to claim 1, which is characterized in that the step (1) specifically:
    The camera object is created, frame per second fs, fs >=7Hz are set；
    Frame counter frame_counter=0 is set；
    Note present frame is Frame (i), and the frame per second shooting with fs includes video 20s, frame_counter=K=20*fs of face.
  3. 3. real-time heart rate detection method according to claim 1, which is characterized in that the step (2) specifically:
    The human-face detector is created using viola-Jones calculus, obtains the starting point coordinate (x, y) and face of human face region Size (w, h)；
    According to human face ratio, interception Frame (i) altitude range is x+0.5*h ~ x+0.7*h, and width range is y+0.1*w ~ y+ The region of 0.3*w, is denoted as Interest (i), as area-of-interest, obtains the RGB channel of the area-of-interest.
  4. 4. real-time heart rate detection method according to claim 1, which is characterized in that the step (3) specifically:
    The RGB channel of the area-of-interest is converted into the channel YUV, the two-dimentional U for extracting area-of-interest described in each frame is logical Road, U path computation formula are as follows:
    U = -0.169*R - 0.331*G + 0.5 *B
    The two-dimentional channel U of each frame in the 20s is obtained, the three-dimensional U channel sequence U (1), U (2) ..., U are formed It (K), is M row N column.
  5. 5. real-time heart rate detection method according to claim 1, which is characterized in that the step (4) specifically:
    To each frame two dimension U channel U (i) of the area-of-interest into line scans, the channel each row U mode, shape are calculated separately At the channel U mode sequence Umost (1), Umost (2) ..., Umost (M)；
    To the channel U mode sequence Umost (1), Umost (2) ..., Umost (M) is ranked up, and is extracted intermediate value Umid (i), is made For the characteristic value of each frame of the area-of-interest；
    Each frame eigenvalue cluster at the one-dimensional characteristic sequence Umid (1) on the time shaft, Umid (2) ..., Umid (K).
  6. 6. real-time heart rate detection method according to claim 1, which is characterized in that the step (5) specifically:
    Using P rank 3Hz Butterworth filter to the one-dimensional characteristic sequence Umid (1), Umid (2) ..., Umid (K) It is filtered, obtains the filtered sequence Fil (1), Fil (2) ..., Fil (J) that length is J；
    Wherein, J=K+P-1.
  7. 7. real-time heart rate detection method according to claim 1, which is characterized in that the step (6) specifically:
    Filtered sequence Fil (1), Fil (2) ..., the Fil (J) that the length is J are scanned, peak value: the Fil is found (1) calculation processing is not done；The Fil (2), Fil (3) ..., Fil (J-1) is compared with adjacent two o'clock, if the point value It is bigger than adjacent two o'clock, then it is assumed that the point is a peak value；That is: if Fil (j) > Fil (j-1) and Fil (j) > Fil (j+1), Fil (j) is a peak point；
    Peak point number in the 20s is counted, Peak_20s is denoted as；
    Calculate Heart_Rate=Peak_20s*3, i.e., the corresponding real-time heart rate value of described 20s.
  8. 8. real-time heart rate detection method according to claim 1, which is characterized in that the step (7) specifically:
    Abandon the preceding 5s data Umid (1) in the 20s, Umid (2) ... Umid (L1), wherein L1=fs*5, rear 15s number According to forming interim sequence Umid (1), Umid (2) ... Umid (L2), wherein L2=fs*15；
    Acquisition 5s data are updated, replenish the interim sequence Umid (1), after Umid (2) ... Umid (L2), thus real Existing sequence Umid (1), the update of Umid (2) ... Umid (K), K=L1+L2 repeat step (2) ~ (6) to get the real-time heart is arrived Rate value.
CN201910629610.0A 2019-07-12 2019-07-12 The real-time heart rate detection method that mode based on video is extracted and median filtering combines Pending CN110236514A (en)

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