Acupuncture point positioning method, device, equipment and medium

Abstract

The invention discloses an acupuncture point positioning method, device and equipment and a medium, and relates to the technical field of acupuncture point localization, the method comprises the following steps: acquiring a real-time electroencephalogram signal of a user and an image of a body surface to be localized; inputting the real-time electroencephalogram signal and the image into an acupuncture point positioning model to obtain accurate acupuncture point position information in the image; and projecting the acupuncture point position information to the body surface to be positioned so as to execute acupuncture point positioning. Therefore, by inputting the real-time electroencephalogram signal of the user and the image of the body surface to be positioned into the acupuncture point positioning model, the position information of the acupuncture point can be quickly and accurately obtained, and after the position information of the acupuncture point is projected to the body surface to be positioned, acupuncture point positioning can be completed, so that the accuracy and efficiency of acupuncture point positioning are improved.

Description

Technical Field

[0001] This application relates to the technical field of acupoint location, and particularly to an acupoint location method, device, equipment and medium. Background Art

[0002] An acupoint refers to a special point area on the human meridian line. Traditional Chinese medicine can stimulate corresponding acupoints through acupuncture, massage, moxibustion and other methods to treat diseases. Therefore, the accurate location of acupoints is extremely important. Specifically, the acupoints can be located by means of the sensitization phenomenon of acupoints. As the parts where the qi of the human zang-fu organs and meridians is infused into the body surface, the sensitization phenomenon of acupoints widely exists and is manifested as multiple forms of acupoint sensitization such as heat sensitization, pain sensitization, electro-sensitization, and form sensitization.

[0003] In related technologies, there are two methods for acupoint location. The first is based on the theory of human meridians to determine the absolute position of acupoints on the human body and the relative positions between acupoints, so as to map all positions onto the human body for acupoint location. However, due to the large differences in body types among individuals, it is impossible to accurately locate acupoints for each user, resulting in a low accuracy rate of acupoint location. The second is to apply electromagnetic stimulation to the user and test the deviation between the actual electrophysiological data of the user and the theoretical electrophysiological data. If the actual electrophysiological data corresponding to a certain point on the user is consistent with the theoretical electrophysiological data of the target acupoint, it means that this point is the target acupoint. However, since the theoretical electrophysiological data of different acupoints are different, when locating different acupoints, it is necessary to adjust the theoretical electrophysiological data in real time, making the operation extremely cumbersome and resulting in a low efficiency of acupoint location.

[0004] Therefore, how to improve the accuracy and efficiency of acupoint location has become a technical problem to be solved urgently. Summary of the Invention

[0005] This application provides an acupoint location method, device, equipment and medium, which can improve the accuracy and efficiency of acupoint location.

[0006] This application discloses the following technical solutions:

[0007] In a first aspect, this application provides an acupoint location method, which includes:

[0008] Obtain the real-time electroencephalogram signal of the user and the image of the body surface to be located;

[0009] Input the real-time electroencephalogram signal and the image into an acupoint location model to obtain the acupoint position information in the image;

[0010] Project the acupoint position information onto the body surface to be located to perform acupoint location.

[0011] Optionally, the construction method of the acupoint location model includes:

[0012] Obtain a user image, where the user image includes target acupoints;

[0013] Obtain the initial position of the target acupoint on the user image;

[0014] Obtain the electroencephalogram (EEG) signals of the initial position and the surrounding points of the initial position;

[0015] According to the EEG signals, classify the initial position and the surrounding points of the initial position into pain acupoint positions and non-pain acupoint positions;

[0016] According to the user image, the pain acupoint positions and the non-pain acupoint positions, train a machine learning model to construct an acupoint localization model.

[0017] Optionally, the obtaining the initial position of the target acupoint on the user image includes:

[0018] Obtain a human body image marked with target acupoints, where the human body image is a two-dimensional image;

[0019] According to the circular detection algorithm, obtain the two-dimensional coordinates of the target acupoint in the human body image;

[0020] Obtain the reference points in the user image and the human body image;

[0021] By aligning the reference points in the user image and the reference points in the human body image, affine-transform the two-dimensional coordinates into the user image to obtain the initial position of the target acupoint on the user image.

[0022] Optionally, the method for determining the surrounding points of the initial position includes:

[0023] Taking the initial position as the center and a target distance as the radius to draw a circle, and determine the surrounding points of the initial position at intervals of a target angle on the circle.

[0024] Optionally, the EEG signals include resting EEG signals and pain EEG signals, and the classifying the initial position and the surrounding points of the initial position into pain acupoint positions and non-pain acupoint positions according to the EEG signals includes:

[0025] Perform frequency-domain extraction processing on the resting EEG signals and the pain EEG signals respectively to obtain resting EEG information and pain EEG information;

[0026] Taking the position where the difference between the pain EEG information and the resting EEG information is positive, or the position corresponding to the maximum difference between the pain EEG information and the resting EEG information, as the pain acupoint position;

[0027] Take the positions other than the pain acupoint position among the initial position and the surrounding points of the initial position as non-pain acupoint positions.

[0028] Optionally, the frequency-domain extraction processing of the resting EEG signal and the pain EEG signal respectively to obtain the resting EEG information and the pain EEG information includes:

[0029] Perform preprocessing on the resting EEG signal and the pain EEG signal respectively to obtain the processed resting EEG signal and the processed pain EEG signal, and the preprocessing includes one or more of filtering processing, re-referencing processing, and artifact removal processing;

[0030] Perform frequency-domain extraction processing on the processed resting EEG signal and the processed pain EEG signal respectively to obtain the resting EEG information and the pain EEG information.

[0031] In a second aspect, the present application provides an acupoint positioning device, which includes an image acquisition module, a position acquisition module, and an acupoint positioning module;

[0032] The image acquisition module is used to acquire the real-time EEG signal of the user and the image of the body surface to be positioned;

[0033] The position acquisition module is used to input the real-time EEG signal and the image into an acupoint positioning model to obtain the acupoint position information in the image;

[0034] The acupoint positioning module is used to project the acupoint position information onto the body surface to be positioned to perform acupoint positioning.

[0035] Optionally, the device for constructing the acupoint positioning model includes a first positioning module, a second positioning module, a third positioning module, a fourth positioning module, and a fifth positioning module;

[0036] The first positioning module is used to acquire a user image, and the target acupoint is included in the user image;

[0037] The second positioning module is used to acquire the initial position of the target acupoint on the user image;

[0038] The third positioning module is used to acquire the EEG signals of the initial position and the surrounding points of the initial position;

[0039] The fourth positioning module is used to classify the initial position and the surrounding points of the initial position into pain acupoint positions and non-pain acupoint positions according to the EEG signals;

[0040] The fifth positioning module is used to train a machine learning model based on the user image, the pain acupoint position, and the non-pain acupoint position to construct an acupoint positioning model.

[0041] Optionally, the second positioning module includes: a first positioning sub-module, a second positioning sub-module, a third positioning sub-module, and a fourth positioning sub-module;

[0042] The first positioning sub-module is used to obtain a human body image marked with a target acupoint, and the human body image is a two-dimensional image;

[0043] The second positioning sub-module is used to obtain the two-dimensional coordinates of the target acupoint in the human body image according to the circular detection algorithm;

[0044] The third positioning sub-module is used to obtain the reference points in the user image and the human body image;

[0045] The fourth positioning sub-module is used to align the reference points in the user image with the reference points in the human body image, and affine the two-dimensional coordinates to the user image to obtain the initial position of the target acupoint on the user image.

[0046] Optionally, the determining device for the surrounding points of the initial position includes: a surrounding positioning module;

[0047] The surrounding positioning module is used to draw a circle with the initial position as the center and a target distance as the radius, and determine the surrounding points of the initial position at intervals of a target angle on the circle.

[0048] Optionally, the electroencephalogram signals include resting electroencephalogram signals and pain electroencephalogram signals, and the fourth positioning module includes: a fifth positioning sub-module, a sixth positioning sub-module, and a seventh positioning sub-module;

[0049] The fifth positioning sub-module is used to perform frequency domain extraction processing on the resting electroencephalogram signals and the pain electroencephalogram signals respectively to obtain resting electroencephalogram information and pain electroencephalogram information;

[0050] The sixth positioning sub-module is used to use the position where the difference between the pain electroencephalogram information and the resting electroencephalogram information is positive, or the position corresponding to the maximum difference between the pain electroencephalogram information and the resting electroencephalogram information, as the pain acupoint position;

[0051] The seventh positioning sub-module is used to use the positions among the initial position and the surrounding points of the initial position except the pain acupoint position as the non-pain acupoint positions.

[0052] Optionally, the fifth positioning sub-module includes: a preprocessing module and a frequency domain extraction module;

[0053] The preprocessing module is configured to preprocess the resting EEG signal and the pain EEG signal respectively to obtain a processed resting EEG signal and a processed pain EEG signal, and the preprocessing includes one or more of filtering processing, rereferencing processing, and artifact removal processing;

[0054] The frequency domain extraction module is configured to perform frequency domain extraction processing on the processed resting EEG signal and the processed pain EEG signal respectively to obtain resting EEG information and pain EEG information.

[0055] In a third aspect, the present application provides an acupoint positioning device, including: a memory and a processor;

[0056] The memory is configured to store a program;

[0057] The processor is configured to implement the steps of the above acupoint positioning method when executing the computer program.

[0058] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above acupoint positioning method are implemented.

[0059] Compared with the prior art, the present application has the following beneficial effects:

[0060] The present application provides an acupoint positioning method, device, equipment and medium. The method includes: acquiring a user's real-time EEG signal and an image of the body surface to be located; inputting the real-time EEG signal and the image into an acupoint positioning model to obtain accurate acupoint position information in the image; projecting the acupoint position information onto the body surface to be located to perform acupoint positioning. Thus, by inputting the user's real-time EEG signal and the image of the body surface to be located into the acupoint positioning model, the position information of the acupoints can be obtained quickly and accurately, and after projecting the acupoint position information onto the body surface to be located, the acupoint positioning can be completed, thereby improving the accuracy and efficiency of acupoint positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0062] Figure 1 It is a flowchart of an acupoint positioning method provided by an embodiment of the present application;

[0063] Figure 2Schematic diagram of a human body image marked with acupoints provided by an embodiment of the present application;

[0064] Figure 3 Schematic diagram of a signal acquisition position provided by an embodiment of the present application;

[0065] Figure 4 Schematic diagram of acquiring electroencephalogram signals provided by an embodiment of the present application;

[0066] Figure 5 Topographic map of pain difference provided by an embodiment of the present application;

[0067] Figure 6 Schematic diagram of an acupoint positioning device provided by an embodiment of the present application;

[0068] Figure 7 Schematic diagram of a computer-readable medium provided by an embodiment of the present application;

[0069] Figure 8 Schematic diagram of the hardware structure of a server provided by an embodiment of the present application. Detailed implementation manners

[0070] As described above, the sensitization phenomenon can be manifested as multi-acupoint sensitization manifestations such as heat sensitivity, pain sensitivity, electro-sensitivity, and form sensitivity. By means of the sensitization phenomenon of acupoints, acupoints can be located.

[0071] In traditional acupoint positioning methods, most are realized based on the experience of traditional Chinese medicine or the subjective pain feelings of the subjects. Nowadays, there are mainly two acupoint positioning methods based on modern technologies:

[0072] The first is based on the theory of human meridians to determine the absolute positions of acupoints on the human body and the relative positions between acupoints, so as to map all positions onto the human body for acupoint positioning. However, due to the large differences in body types among individuals, it is impossible to accurately position acupoints for each user, resulting in a low accuracy rate of acupoint positioning.

[0073] The second is to apply electromagnetic stimulation to the user and test the deviation between the actual electrophysiological data of the user and the theoretical electrophysiological data. If the actual electrophysiological data corresponding to a certain point on the user is consistent with the theoretical electrophysiological data of the target acupoint, it indicates that this point is the target acupoint. However, since the theoretical electrophysiological data of different acupoints are different, when positioning different acupoints, it is necessary to adjust the theoretical electrophysiological data in real time, making the operation extremely cumbersome and resulting in a low efficiency of acupoint positioning.

[0074] In view of this, the present application discloses an acupoint positioning method, device, equipment and medium. The method includes: acquiring the real-time electroencephalogram signal of a user and the image of the body surface to be positioned; inputting the real-time electroencephalogram signal and the image into an acupoint positioning model to obtain the accurate acupoint position information in the image; and projecting the acupoint position information onto the body surface to be positioned to perform acupoint positioning. Thus, by inputting the real-time electroencephalogram signal of the user and the image of the body surface to be positioned into the acupoint positioning model, the position information of the acupoints can be obtained quickly and accurately. After projecting the acupoint position information onto the body surface to be positioned, the acupoint positioning can be completed, thereby improving the accuracy and efficiency of acupoint positioning.

[0075] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0076] See Figure 1 , which is a flowchart of an acupoint positioning method provided by an embodiment of the present application. The method includes:

[0077] S101: Collect user images.

[0078] The user image refers to the image of the part of the user where acupoint positioning is required. Exemplarily, if the user hopes to perform acupoint positioning on the Weicang acupoint and Geguan acupoint on the back, the collected user image is the image of the user's back.

[0079] In some specific implementation manners, the user image can be collected by a binocular camera. Among them, the binocular camera is a camera that uses the bionics principle to obtain the user image with synchronous exposure through two calibrated cameras. Comparing the use of a binocular camera to collect user images with an ordinary monocular camera, the depth information of each pixel point in the user image can be effectively obtained, which helps to improve the accuracy of subsequent acupoint positioning.

[0080] It should be noted that the user images involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with the relevant laws, regulations and standards of relevant countries and regions.

[0081] S102: Align the user image with the human body image marked with the target acupoints to obtain the initial position of the target acupoints on the user image.

[0082] The initial position refers to the position after rough positioning of the target acupoints.

[0083] In some specific implementation manners, after collecting the user image, it is first necessary to obtain a three-dimensional human body model marked with target acupoints, and obtain a two-dimensional human body image marked with the target acupoints from it. Refer to Figure 2 , which is a schematic diagram of a human body image marked with acupoints provided by an embodiment of the present application. All acupoints on this human body image are marked based on the theory of human meridians. It can be understood that this human body image at least includes the target acupoints that the user needs to locate. Exemplarily, if the user hopes to perform acupoint positioning on the Weicang acupoint and Geguan acupoint on the back, then at least the Weicang acupoint and Geguan acupoint need to be marked in this human body image. It should be noted that the present application does not limit the specific human body image and target acupoints.

[0084] Secondly, through the circular detection algorithm, the coordinates of the target acupoints in the human body image are obtained. Exemplarily, the coordinates of the target acupoints can be two-dimensional coordinates (x, y), and the coordinates of the target acupoints represent the position of the target acupoints in the human body image.

[0085] Subsequently, according to the openpose algorithm, the reference points in the user image and the human body image can be identified. The reference points are points that can be accurately distinguished and quickly located, such as the right shoulder point, the left shoulder point, the middle hip point, etc.

[0086] Finally, by aligning the reference points in the user image with the reference points in the human body image, the user image and the human body image are aligned, and the coordinates of the target acupoints in the human body image are mapped to the user image, so as to obtain the initial position of the target acupoints on the user image.

[0087] S103: Obtain the electroencephalogram signals of the initial position and its surrounding points.

[0088] The electroencephalogram signal is the overall reflection of the electrophysiological activities of the cranial nerve tissue on the surface of the cerebral cortex. In this application, the resting electroencephalogram signal and the pain electroencephalogram signal are mainly studied. Since the main response regions of the pain electroencephalogram signal are located in the frontal region, central region and parietal region of the head, the electroencephalogram signals of the frontal region, central region and parietal region of the head can be collected. Refer to Figure 3 , which is a schematic diagram of a signal acquisition position provided by an embodiment of the present application. Electrodes can be placed at positions 1-8 in the figure respectively, and reference electrodes can be placed at positions A1 and A2 (earlobe positions) in the figure to obtain electroencephalogram signals.

[0089] Refer to Figure 4, This figure is a schematic diagram of obtaining electroencephalogram (EEG) signals provided by an embodiment of the present application. After obtaining the initial position of the target acupoint, the resting EEG signals of the initial position and its surrounding points can be obtained first. It can be understood that the surrounding points can be obtained by drawing a circle with the initial position as the center and a radius of 1 centimeter (i.e., the target distance), and taking a surrounding point every 45° (i.e., the target angle) on the circle, that is, a total of 8 surrounding points are taken. Subsequently, by pressing the initial position and its surrounding points, the pain EEG signals of the initial position and its surrounding points can be obtained.

[0090] It should be noted that moxibustion, acupuncture, etc. can also be performed on the initial position and its surrounding points to obtain EEG signals. The present application does not limit the specific processing methods.

[0091] S104: Preprocess the EEG signals and perform frequency domain extraction to obtain EEG information.

[0092] After collecting the EEG signals, one or more preprocessing operations such as filtering, rereferencing, and artifact removal need to be performed on the EEG signals to obtain the processed EEG signals. Subsequently, frequency domain extraction is performed on the processed EEG signals to obtain EEG information, which includes resting EEG information and pain EEG information.

[0093] In some specific implementation manners, band-pass filtering of 0.5 - 49 Hz (Hertz) can be performed on the resting EEG signals and the pain EEG signals respectively, and the independent component analysis algorithm can be used to remove the artifacts in the resting EEG signals and the pain EEG signals, so as to obtain clean EEG signals, that is, the processed EEG signals.

[0094] Subsequently, frequency domain extraction is performed on the processed EEG signals based on one or more feature extraction methods such as fast Fourier transform, short-time Fourier transform, wavelet transform, and Hilbert transform to obtain EEG information.

[0095] S105: Classify the initial position and its surrounding points according to the resting EEG information and the pain EEG information to obtain the pain acupoint positions and non-pain acupoint positions.

[0096] See Figure 5 , This figure is a topographic map of pain differences provided by an embodiment of the present application. Select the positions where the difference between the pain EEG information and the resting EEG information is positive, or the position corresponding to the EEG information with the largest difference, in three frequency bands: the delta band (1 - 4 Hz), the theta band (4 - 8 Hz), and the beta band (13 - 30 Hz) as the pain acupoint positions, and set the positions other than the pain acupoint positions among the initial position and its surrounding points as non-pain acupoint positions.

[0097] S106: Train a machine learning model based on the pain acupoint positions and non-pain acupoint positions to obtain an acupoint localization model.

[0098] Based on the pain acupoint positions and non-pain acupoint positions obtained in step S105, and the user image obtained in step S101, train a support vector machines (SVM) model for binary classification to obtain an acupoint localization model. Through this acupoint localization model, the accurate acupoint position information in the image can be obtained.

[0099] Among them, the support vector machine model is a supervised learning model, which has good training effects for small samples. The basic idea of the support vector machine model is to solve the separation hyperplane that can correctly divide the training data set and has the largest geometric margin, and its main optimization parameters are the regularization coefficient C and the kernel function.

[0100] S107: Input the to-be-localized image of the user and the collected electroencephalogram signals into the acupoint localization model, so that the acupoint localization model outputs the acupoint position information in the to-be-localized image.

[0101] At any time after constructing the acupoint localization model, the to-be-localized image of the user can be obtained, and this to-be-localized image is the image of the to-be-localized body surface of the user. By inputting this to-be-localized image and the corresponding collected electroencephalogram signals into the acupoint localization model constructed in step S106, the acupoint position information in the image can be obtained.

[0102] S108: Project the acupoint position information onto the body surface of the user to perform acupoint localization.

[0103] After obtaining the acupoint position information, acupoint localization can be performed by projecting the acupoint position information onto the to-be-localized body surface of the user. It should be noted that the body surface of the user is the body surface in the to-be-localized image of the user in step S107.

[0104] In summary, the present application discloses an acupoint localization method. After using the combination of the collected user image and the human body three-dimensional model to determine the rough localization of the user's acupoints, the pain sensitivity characteristics of the acupoints are combined with the recognition of the pain degree by electroencephalogram signals. Using the recognition of pain signals by electroencephalogram signals to replace the subjective evaluation of pain by the user, and using the pain-sensitive characteristics of the acupoints to determine the precise positions of the acupoints, which improves the accuracy and efficiency of acupoint localization.

[0105] See Figure 6 , this figure is a schematic diagram of an acupoint localization device provided by an embodiment of the present application. The acupoint localization device 200 includes: an image acquisition module 201, a position acquisition module 202, and an acupoint localization module 203.

[0106] Specifically, the image acquisition module 201 is configured to acquire the user's real-time electroencephalogram signal and the image of the body surface to be located; the position acquisition module 202 is configured to input the real-time electroencephalogram signal and the image into the acupoint positioning model to obtain the acupoint position information in the image; the acupoint positioning module 203 is configured to project the acupoint position information onto the body surface to be located to perform acupoint positioning.

[0107] In some specific implementation manners, the apparatus for constructing the acupoint positioning model includes: a first positioning module, a second positioning module, a third positioning module, a fourth positioning module, and a fifth positioning module;

[0108] Specifically, the first positioning module is configured to acquire a user image, which includes target acupoints; the second positioning module is configured to acquire the initial positions of the target acupoints on the user image; the third positioning module is configured to acquire the electroencephalogram signals of the initial positions and the surrounding points of the initial positions; the fourth positioning module is configured to classify the initial positions and the surrounding points of the initial positions into pain acupoint positions and non-pain acupoint positions according to the electroencephalogram signals; the fifth positioning module is configured to train a machine learning model according to the user image, the pain acupoint positions, and the non-pain acupoint positions to construct the acupoint positioning model.

[0109] In some specific implementation manners, the second positioning module includes: a first positioning sub-module, a second positioning sub-module, a third positioning sub-module, and a fourth positioning sub-module;

[0110] Specifically, the first positioning sub-module is configured to acquire a human body image marked with target acupoints, and the human body image is a two-dimensional image; the second positioning sub-module is configured to acquire the two-dimensional coordinates of the target acupoints in the human body image according to the circular detection algorithm; the third positioning sub-module is configured to acquire the reference points in the user image and the human body image; the fourth positioning sub-module is configured to align the reference points in the user image with the reference points in the human body image and affine the two-dimensional coordinates to the user image to acquire the initial positions of the target acupoints on the user image.

[0111] In some specific implementation manners, the apparatus for determining the surrounding points of the initial positions includes: a surrounding positioning module;

[0112] Specifically, the surrounding positioning module is configured to draw a circle with the initial position as the center and the target distance as the radius, and determine the surrounding points of the initial position at intervals of the target angle on the circle.

[0113] In some specific implementation manners, the electroencephalogram signals include resting electroencephalogram signals and pain electroencephalogram signals, and the fourth positioning module includes: a fifth positioning sub-module, a sixth positioning sub-module, and a seventh positioning sub-module;

[0114] Specifically, a fifth positioning sub-module is configured to perform frequency-domain extraction processing on the resting EEG signal and the pain EEG signal respectively to obtain resting EEG information and pain EEG information; a sixth positioning sub-module is configured to use the position corresponding to the positive difference between the pain EEG information and the resting EEG information, or the position corresponding to the maximum difference between the pain EEG information and the resting EEG information as the pain acupoint position; a seventh positioning sub-module is configured to use the positions other than the pain acupoint position among the initial position and the surrounding points of the initial position as non-pain acupoint positions.

[0115] In some specific implementation manners, the fifth positioning sub-module includes: a preprocessing module and a frequency-domain extraction module;

[0116] Specifically, the preprocessing module is configured to perform preprocessing on the resting EEG signal and the pain EEG signal respectively to obtain the processed resting EEG signal and the processed pain EEG signal. The preprocessing includes one or more of filtering processing, re-referencing processing, and artifact removal processing; the frequency-domain extraction module is configured to perform frequency-domain extraction processing on the processed resting EEG signal and the processed pain EEG signal respectively to obtain resting EEG information and pain EEG information.

[0117] In summary, the present application discloses an acupoint positioning device. After determining the rough positioning of the user's acupoints by combining the collected user image with the human body three-dimensional model, the pain sensitivity characteristics of the acupoints are combined with the recognition of the pain degree by the EEG signal. The recognition of the pain signal by the EEG signal is used to replace the subjective pain evaluation of the user, and the pain sensitivity characteristics of the acupoints are used to determine the precise position of the acupoints, improving the accuracy and efficiency of acupoint positioning.

[0118] See Figure 7 , which is a schematic diagram of a computer-readable medium provided by an embodiment of the present application. A computer program 311 is stored on the computer-readable medium 300. When the computer program 311 is executed by a processor, the steps of the acupoint positioning method described above are implemented. Figure 1 of the acupoint positioning method.

[0119] Note that in the context of this application, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0120] Note that the machine-readable medium described above in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can, for example, be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device. And in this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

[0121] The above computer-readable medium can be included in the above electronic device; or it can exist separately without being assembled into the electronic device.

[0122] See Figure 8 , which is a schematic diagram of the hardware structure of a server provided by an embodiment of the present application. The server 400 may vary greatly due to configuration or performance differences, and may include one or more central processing units (CPUs) 422 (for example, one or more processors) and a memory 432, and one or more storage media 430 (for example, one or more mass storage devices) for storing application programs 440 or data 444. Among them, the memory 432 and the storage media 430 may be transient storage or persistent storage. The program stored in the storage media 430 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the server. Further, the central processing unit 422 may be configured to communicate with the storage media 430 and execute a series of instruction operations in the storage media 430 on the server 400.

[0123] The server 400 may further include one or more power supplies 426, one or more wired or wireless network interfaces 450, one or more input / output interfaces 458, and / or one or more operating systems 441, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and so on.

[0124] The steps performed by the acupoint location method in the above embodiment may be based on the Figure 8 server structure shown.

[0125] It should also be noted that, according to the embodiments of the present application, the process of the acupoint location method described in the above Figure 1 flow chart may be implemented as a computer software program. For example, the embodiments of the present application include a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes program codes for executing the method shown in the Figure 1 flow chart above.

[0126] Although the subject matter has been described in language specific to structural features and / or method logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. On the contrary, the specific features and acts described above are merely example forms of implementing the claims.

[0127] Although several specific implementation details are included in the above description, these should not be construed as limiting the scope of the present application. Certain features described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment can also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0128] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present application.

Claims

1. A method for acupoint location, characterized in that, the method includes: acquiring the real-time electroencephalogram (EEG) signal of the user and the image of the body surface to be located; inputting the real-time EEG signal and the image into an acupoint location model to obtain the acupoint position information in the image; projecting the acupoint position information onto the body surface to be located to perform acupoint location.

2. The method according to claim 1, characterized in that, the method for constructing the acupoint location model includes: acquiring a user image, where the user image includes target acupoints; acquiring the initial position of the target acupoint on the user image; acquiring the EEG signals of the initial position and the surrounding points of the initial position; classifying the initial position and the surrounding points of the initial position into pain acupoint positions and non-pain acupoint positions according to the EEG signals; training a machine learning model according to the user image, the pain acupoint positions and the non-pain acupoint positions to construct an acupoint location model.

3. The method according to claim 2, characterized in that, acquiring the initial position of the target acupoint on the user image includes: acquiring a human body image marked with target acupoints, where the human body image is a two-dimensional image; acquiring the two-dimensional coordinates of the target acupoint in the human body image according to a circular detection algorithm; acquiring the reference points in the user image and the human body image; affinely transforming the two-dimensional coordinates into the user image by aligning the reference points in the user image and the reference points in the human body image to obtain the initial position of the target acupoint on the user image.

4. The method according to claim 2, characterized in that, the method for determining the surrounding points of the initial position includes: drawing a circle with the initial position as the center and a target distance as the radius, and determining the surrounding points of the initial position at intervals of a target angle on the circle.

5. The method according to claim 2, characterized in that, the EEG signals include resting EEG signals and pain EEG signals, and classifying the initial position and the surrounding points of the initial position into pain acupoint positions and non-pain acupoint positions according to the EEG signals includes: performing frequency domain extraction processing on the resting EEG signals and the pain EEG signals respectively to obtain resting EEG information and pain EEG information; taking the position where the difference between the pain EEG information and the resting EEG information is positive, or the position corresponding to the maximum difference between the pain EEG information and the resting EEG information, as the pain acupoint position; taking the positions other than the pain acupoint positions among the initial position and the surrounding points of the initial position as non-pain acupoint positions.

6. The method according to claim 5, characterized in that, performing frequency domain extraction processing on the resting EEG signals and the pain EEG signals respectively to obtain resting EEG information and pain EEG information includes: performing preprocessing on the resting EEG signals and the pain EEG signals respectively to obtain the processed resting EEG signals and the processed pain EEG signals, where the preprocessing includes one or more of filtering processing, re-referencing processing and artifact removal processing; Perform frequency domain extraction processing on the processed resting EEG signals and the processed pain EEG signals respectively to obtain resting EEG information and pain EEG information.

7. An acupoint positioning device Characterized in that The device includes: an image acquisition module, a position acquisition module, and an acupoint positioning module; The image acquisition module is used to acquire the real-time EEG signals of the user and the image of the body surface to be positioned; The position acquisition module is used to input the real-time EEG signals and the image into an acupoint positioning model to obtain the acupoint position information in the image; The acupoint positioning module is used to project the acupoint position information onto the body surface to be positioned to perform acupoint positioning.

8. The device according to claim 7, Characterized in that The device for constructing the acupoint positioning model includes: a first positioning module, a second positioning module, a third positioning module, a fourth positioning module, and a fifth positioning module; The first positioning module is used to acquire a user image, and the target acupoint is included in the user image; The second positioning module is used to acquire the initial position of the target acupoint on the user image; The third positioning module is used to acquire the EEG signals of the initial position and the surrounding points of the initial position; The fourth positioning module is used to classify the initial position and the surrounding points of the initial position into pain acupoint positions and non-pain acupoint positions according to the EEG signals; The fifth positioning module is used to train a machine learning model according to the user image, the pain acupoint positions and the non-pain acupoint positions to construct an acupoint positioning model.

9. An acupoint positioning device Characterized in that It includes: A memory and a processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the method according to any one of claims 1 to 6.

10. A computer storage medium, on which a computer program is stored, Characterized in that When the computer program is executed by a processor, each step of the method according to any one of claims 1 to 6 is implemented.

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