CN112294282A - Self-calibration method of emotion detection device based on RPPG - Google Patents
Self-calibration method of emotion detection device based on RPPG Download PDFInfo
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- 230000008451 emotion Effects 0.000 title claims abstract description 26
- 238000000034 method Methods 0.000 title claims abstract description 19
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- 238000010606 normalization Methods 0.000 claims abstract description 3
- 238000006243 chemical reaction Methods 0.000 claims abstract 2
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- 239000011159 matrix material Substances 0.000 claims description 8
- 230000002996 emotional effect Effects 0.000 claims description 3
- 230000000875 corresponding effect Effects 0.000 claims 5
- 238000004364 calculation method Methods 0.000 claims 1
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- 238000009532 heart rate measurement Methods 0.000 description 5
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- 238000013186 photoplethysmography Methods 0.000 description 2
- 230000009323 psychological health Effects 0.000 description 2
- 208000024172 Cardiovascular disease Diseases 0.000 description 1
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- 108010053085 Complement Factor H Proteins 0.000 description 1
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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
- A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
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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
- A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
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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
- A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
- A61B5/0077—Devices for viewing the surface of the body, e.g. camera, magnifying lens
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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
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Abstract
The invention discloses a self-calibration method of a non-contact heart rate emotion monitoring device based on a face video, which comprises the following steps: (1) opening a camera to collect a face video; (2) carrying out frame conversion on the acquired video to determine an ROI (region of interest); (3) r, G, B separating the interested region; (4) carrying out normalization processing, independent component analysis and Fourier change on the separated data, extracting a maximum power spectrum value and corresponding frequency, extracting a pulse numerical value, and storing the pulse numerical value in a pulse array; (4) calculating the heart rate number HR (pulse (round ((i-450)/15) +1) × 60 according to a formula; (5) calculating a heart rate amplitude HRA and a heart rate variability HRV according to the extracted heart rate waveform; (6) and establishing a model among HR, HRA and HRV, and calculating the emotion index H which is a.HR + b.HRA + c.H.
Description
Technical Field
The invention relates to a heart rate measuring method based on a face video, which judges the psychological health and emotion of a human body through measured heart rate information and belongs to the related field of biomedical engineering.
Background
The heart rate is the most basic information of pulse wave and is one of four vital signs of human body. The stability of the compound directly reflects the quality of the heart function and is an important physiological index of human health. Heart rate is an important parameter for monitoring cardiovascular disease. Meanwhile, the heart rate is also an important index of conductive exercise, and the kinematics research shows that the heart rate is one of parameters with sensitive physiological changes, and the heart rate has important application in criminal investigation means such as lie detection.
Most traditional heart rate detection is achieved by means of pulse feeling or auscultation by a doctor, and such heart rate measurement requires a doctor to have a rich relevant knowledge and a great deal of practical experience. With the development of science and technology, people invent heart rate monitors to measure the heart rate of people, but the heart rate monitors are high in cost, are usually only used for clinical monitoring in hospitals, and are difficult to enter the daily life of people. Subsequently, the appearance of finger-clipped heart rate oximeter and the like greatly reduces the equipment cost on the basis of high measurement accuracy, is more convenient to use, and can accurately acquire the heart rate of a user as long as the finger-clipped heart rate oximeter is clipped on a finger, but the finger-clipped heart rate oximeter is required to be directly contacted with the human body, and the long-time contact can cause discomfort of a testee, so that the finger-clipped heart rate oximeter is not suitable for long-time heart rate measurement.
In recent years, along with the popularization of computers, cameras and the like, the proposal of an image PPG (photoplethysmography) technology provides a feasible idea for realizing non-invasive and non-contact real-time heart rate measurement. Because the heart of the human body is continuously contracted and expanded, the filling degree of blood in the blood vessel of the human body can be continuously changed along with the heartbeat, and the absorption of light can present pulsatile change consistent with the heartbeat along with the change of the blood volume, so that the intensity of light reflected by the surface of the skin can also correspondingly and periodically change, and the change is represented as the change of the skin color in the collected image. The human face video based pulse wave signal extraction method based on the rppg technology realizes the extraction of the human body pulse wave signal based on the human face video, the HR, HRA and HRV values are extracted from the pulse wave signal, and the emotion influence index H is calculated according to the established emotion influence model, so that the psychological health is evaluated.
Disclosure of Invention
The invention aims to provide a self-calibration method of a non-contact heart rate emotion detection device based on a face video, which has the advantages of no wound, non-contact, high precision, low algorithm complexity and the like and has wide application prospects in the fields of patient heart rate monitoring, family heart rate measurement, body sensing games and criminal investigation lie detection.
The technical scheme of the invention is as follows:
(1) collecting a video of a section of face area by using a common camera, and reading the total frame number of the video;
(2) searching a face region of the RGB graph by combining an Adaboost algorithm and a pyramid graph, acquiring the length h and the width w of the face region, and determining the cheek region as an interested ROI region;
(3) separating RGB channels of the region of interest and taking a mean value;
(4) dividing and normalizing the acquired data according to the size of the window;
(5) analyzing independent components, changing Fourier, removing linear components, and extracting a maximum power spectrum value and corresponding frequency;
(6) and obtaining an emotion influence factor H ═ a × HR + b × HRA + c × HRV + d according to the collected new heart rate signals. (H: index of emotional influence, HR: number of heart rates, HRA: amplitude of heart rates, HRV: number of heart rate variability)
The invention has the advantages and positive effects that:
the self-calibration method of the emotion detection device based on the RPPG can realize non-contact measurement of the heart rate of a human body, and simultaneously, the relationship between the emotion influence index H and the heart rate is calculated according to the measured heart rate measurement characteristic points. The heart rate monitoring method is high in accuracy, has no direct contact with a tested person, and has no radiation hazard to a tested human body. The method disclosed by the invention is beneficial to health monitoring in daily life, provides a new technical means for clinical medical treatment, and can be applied to the fields of diagnosis of human mental health, judgment of lie in criminal investigation means and the like.
Drawings
Fig. 1 is a schematic diagram of a non-contact heart rate monitoring system.
Fig. 2 is a flow chart of the overall implementation of the system.
Detailed Description
The first implementation mode comprises the following steps:
the first step is as follows: selecting an environment with proper illumination, determining a position which can clearly and completely image a face area, and fixing a camera;
the second step is that: starting a camera to acquire a video of a scene of a face area, wherein the face is allowed to move and deflect within an imaging range in the acquisition process, and the acquired data is stored as an MP4 format video file;
the third step: selecting a face region of the acquired video file, determining an ROI (region of interest), separating RGB (red, green and blue) channels and taking an average value;
the fourth step: storing the obtained RGB numerical value, continuously processing next frame data until the total frame number of the video is completely processed, and drawing a mean value curve of RGB three channels by matlab software;
the fifth step: according to the drawn curve, the G channel can be seen to contain pulse information;
and a sixth step: setting the sizes of an adopted window and a sliding window, then carrying out normalization processing on the stored RGB channel numerical values, storing a matrix of the first ten maximum power spectrum amplitude values and frequency values of each channel of each window, and removing linear components from data to obtain a separated component matrix;
the seventh step: performing power spectrum analysis on the data, extracting the maximum power spectrum amplitude value in each channel, and finding out a pulse angle mark corresponding to the maximum frequency spectrum amplitude value, thereby finding out the most possible pulse value of the current window and storing the most possible pulse value in a plurality of groups of pulses;
eighth step: when i is larger than 450, calculating the number of heartbeats according to the formula pulse (round ((i-450)/15) +1) × 60;
the ninth step: and calculating H according to H & lta & gt HR + b & ltHRA + c & ltHRV + d, and estimating the relation between the emotion and the heart rate.
The second embodiment:
testing 10 adults according to the procedure of embodiment one;
the first step is as follows: take a video of 10 adults when they were answering questions normally;
the second step is that: shooting videos of 10 adults in a lie state;
the third step: analyzing the video information of the answer questions in a normal state, and calculating the value of an emotion influence index H;
the fourth step: analyzing the video information of the answer questions in the lie state, and calculating the value of the emotion influence index H;
the fifth step: by comparing the H values, the emotional influence index H value is higher in the lie state.
Claims (8)
1. A self-calibration method of an emotion detection device based on RPPG is characterized by comprising the following steps:
acquiring a face region video; the face area video consists of a plurality of frames of RGB face images;
carrying out frame conversion on the face region video to determine a face ROI image;
r, G, B three channels of human face ROI image are separated to obtain RGB value of each channel;
extracting the maximum power spectrum amplitude and the pulse angle mark in each channel according to the RGB numerical values; the pulse angle mark is a pulse angle mark corresponding to the maximum power spectrum amplitude value;
obtaining a pulse value according to the maximum power spectrum amplitude and the pulse angle scale;
calculating heart rate data according to the pulse value; the heart rate data comprises heart rate number per minute, heart rate amplitude and heart rate variability;
and calculating the emotion index according to the heart rate data to realize self-calibration.
2. The self-calibration method of the RPPG based emotion detection device as claimed in claim 1, wherein after said R, G, B three-channel separation of the ROI image of the human face to obtain RGB values of each channel, further comprising:
and carrying out normalization processing on the RGB values of all channels.
3. The self-calibration method of the emotion detection device based on RPPG as claimed in claim 1, wherein the performing frame transformation on the face region video to determine a face ROI image specifically comprises:
combining an Adaboost algorithm with a pyramid image, searching the RGB face image, and determining the length and the width of the RGB face image;
expanding the RGB face image, and determining a face ROI image according to the length and the width of the RGB face image; the face ROI image bboxPolygon ═ x, y, x + w, y, x + w, y + h, x, y + h ]; wherein x is the x-axis coordinate of the face ROI image coordinate, y is the y-axis coordinate of the face ROI image coordinate, h is the length of the RGB face image, w is the width of the RGB face image, x ═ bbox (1) + bbox (3) × 0.15, y ═ bbox (2) + bbox (4) × 0.05, w ═ bbox (3) × 0.7, h ═ bbox (4) × 0.85.
4. The self-calibration method of the emotion detection device based on RPPG as claimed in claim 1, wherein said extracting maximum power spectrum amplitude and pulse angle scale in each channel according to said RGB values specifically comprises:
calculating the power spectrum amplitude of each channel of each window by adopting an independent component analysis method and Fourier transform according to the RGB numerical value;
sequencing the power spectrum amplitude values from top to bottom to obtain a power spectrum amplitude value sequence corresponding to each channel;
determining a maximum power spectrum amplitude group and a corresponding frequency matrix corresponding to each channel according to the power spectrum amplitude sequence; the maximum power spectrum amplitude value group consists of the first ten power spectrum amplitude values in the power spectrum amplitude value sequence; the frequency matrix is a frequency matrix corresponding to the first ten power spectrum amplitude values;
removing linear components in the maximum power spectrum amplitude value set and the frequency matrix to obtain a processed maximum power spectrum amplitude value set and a processed frequency matrix;
and determining the maximum power spectrum amplitude and the pulse angle standard in each channel according to the processed maximum power spectrum amplitude group and the processed frequency matrix.
5. The self-calibration method of the RPPG based emotion detection device as recited in claim 1, wherein the calculation formula of the heart rate number is:
HR＝pulse(round((i-450)/15)+1)*60；
HR represents the heart rate, i is larger than 450, pulse represents an array, and the pulse array stores pulse values.
6. The self-calibration method of the RPPG based emotion detection device as claimed in claim 1, wherein said calculating the emotion index according to the heart rate data to achieve self-calibration specifically comprises:
constructing an emotion influence index model according to the heart rate data; the model of emotional impact index is a correlated non-linear model of the heart rate number, the heart rate amplitude, and the heart rate variability; the emotion influence index model
H＝a*HR+b*HRA+c*HRV+d；
Where HR denotes the number of heart rates, HRA denotes the amplitude of the heart rate, HRV denotes the variability of the heart rate, a denotes the coefficient of the HR correlation, b denotes the coefficient of the HRA correlation, c denotes the coefficient of the HRV correlation, and d is a compensation constant.
7. The self-calibration method of the RPPG based emotion detection device as claimed in claim 1, wherein the face region video is obtained by setting a timing initial value to 0 and performing video acquisition for 1 minute on a scene of the face region by using a camera.
8. The self-calibration method of an RPPG based emotion detection device as recited in claim 4, wherein said extracting maximum power spectral amplitude and pulse angle scale in each channel according to said RGB values further comprises:
the size of the initial sliding window is 30s, and the size of the sliding window and the last overlapping part is 29 s.
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| CN113827240A (en) * | 2021-09-22 | 2021-12-24 | 北京百度网讯科技有限公司 | Emotion classification method and emotion classification model training method, device and equipment |
| CN113892952A (en) * | 2021-06-09 | 2022-01-07 | 上海良相智能化工程有限公司 | Intelligent studying and judging system |
| CN117281516A (en) * | 2023-11-24 | 2023-12-26 | 吾征智能技术(北京)有限公司 | Mental stress monitoring system, method, medium and device |
| WO2024056087A1 (en) * | 2022-09-16 | 2024-03-21 | Jing Wei Chin | Method and device for camera-based heart rate variability monitoring |
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Application publication date: 20210202 |