Acupuncture point massage method and device through brain computer monitoring feedback and electronic equipment

Abstract

The invention discloses an acupoint massage method and device through brain computer monitoring feedback and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring acupoint distribution information of a face to be massaged; collecting electroencephalogram signals of a user through a brain-computer interface, and matching corresponding physiological parameters in a preset physiological parameter database according to the electroencephalogram signals; according to the physiological parameters, a corresponding first massage scheme is matched in a preset massage scheme database; performing preliminary massage on the face to be massaged according to the first massage scheme and the acupoint distribution information; in the preliminary massage process, judging whether the physiological parameters of the user are changed or not; if it is determined that the physiological parameters of the user are changed, a second massage scheme corresponding to the changed physiological parameters is obtained; according to the second massage scheme, secondary massage is conducted on the face to be massaged till the preset massage duration is reached. By implementing the technical scheme provided by the invention, the experience feeling of the user on face massage is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and specifically to an acupoint massage method, device and electronic equipment through brain-computer monitoring feedback. Background Art

[0002] With the rapid development of modern science and technology, people are paying more and more attention to improving their health and quality of life. Especially in daily life, facial care and stress relief have become the focus of people's attention. Acupoint massage, as a traditional and effective method, has long been used to improve facial blood circulation, relieve muscle tension and pain. However, traditional massage methods often rely on the operator's experience and lack scientific quantitative means to monitor and optimize the massage effect.

[0003] At present, although some devices and technologies have tried to combine modern sensing technology with traditional massage methods, most of these technologies are still using simple pressure sensing or fixed massage combinations. These methods cannot provide in-depth analysis of the user's real-time physiological state, and it is difficult to dynamically adjust the massage plan according to the user's physiological changes, resulting in a poor user experience.

[0004] Therefore, there is an urgent need for an acupoint massage method, device and electronic equipment with brain-computer monitoring feedback. Summary of the invention

[0005] The present application provides an acupoint massage method, device and electronic device through brain-computer monitoring feedback, which enhances the user's experience of facial massage.

[0006] In a first aspect of the present application, a method for acupoint massage through brain-computer monitoring feedback is provided, the method comprising: obtaining acupoint distribution information of a face to be massaged; collecting EEG signals of a user through a brain-computer interface, and matching corresponding physiological parameters in a preset physiological parameter database according to the EEG signals, the preset physiological parameter database including the correspondence between EEG signals and physiological parameters; matching a corresponding first massage scheme in a preset massage scheme database according to the physiological parameters, the first massage scheme including massage sequence, massage technique, massage intensity and massage frequency, the preset massage scheme database including the correspondence between physiological parameters and massage schemes; performing a preliminary massage on the face to be massaged according to the first massage scheme and the acupoint distribution information; during the preliminary massage, determining whether the physiological parameters of the user have changed; if it is determined that the physiological parameters of the user have changed, obtaining a second massage scheme corresponding to the changed physiological parameters; performing a secondary massage on the face to be massaged according to the second massage scheme until a preset massage duration is reached.

[0007] By adopting the above technical scheme, through the acupoint massage method with brain-computer monitoring feedback, by obtaining the acupoint distribution information of the face to be massaged, collecting the user's EEG signals, matching the corresponding physiological parameters according to the EEG signals, and then matching the corresponding massage scheme according to the physiological parameters, accurate and personalized acupoint massage can be achieved. During the massage process, by judging whether the user's physiological parameters have changed, the massage scheme is dynamically adjusted, which can adapt to the changes in the user's physiological state in real time and provide a more intelligent and comfortable massage experience. At the same time, the method uses brain-computer interface technology to collect EEG signals, which can directly reflect the user's physiological and psychological state, which is more objective and reliable than traditional subjective feedback. By establishing a mapping relationship between EEG signals, physiological parameters, and massage schemes, closed-loop control and dynamic optimization of the massage process are achieved, which improves the pertinence and effectiveness of massage. In addition, the method also introduces the link of secondary massage, which can adjust the massage strategy in time according to the changes in the user's physiological parameters after the initial massage, and further improve the massage effect.

[0008] Optionally, the obtaining of acupoint distribution information of the face to be massaged specifically includes: obtaining a three-dimensional human body model, the three-dimensional human body model including spatial coordinate information of various parts of the human body; determining a local three-dimensional model corresponding to the face to be massaged according to the position of the face to be massaged in the three-dimensional human body model; obtaining acupoint names and acupoint coordinates corresponding to the local three-dimensional model in a preset acupoint database; mapping the acupoint coordinates to the coordinate system of the local three-dimensional model to obtain relative coordinates of the acupoints of the face to be massaged in the local three-dimensional model, and using the relative coordinates as the acupoint distribution information of the face to be massaged.

[0009] By adopting the above technical solution, by introducing the human body three-dimensional model and the preset acupoint database, the acupoint position of the face to be massaged can be quickly and accurately determined, providing a reliable spatial positioning basis for subsequent precision massage. Specifically, the method first obtains a human body three-dimensional model containing the spatial coordinates of various parts of the human body, and then extracts the corresponding local three-dimensional model according to the position of the face to be massaged in the human body three-dimensional model. On this basis, the acupoint name and acupoint coordinates corresponding to the local three-dimensional model are obtained from the preset acupoint database, and the acupoint coordinates are mapped to the coordinate system of the local three-dimensional model to obtain the precise position of the acupoint relative to the face. Through the preset acupoint database, the acupoint distribution corresponding to different facial areas can be quickly matched without manual measurement and calculation, which greatly improves the positioning efficiency. In addition, the acupoint distribution information obtained by the method is expressed in the form of three-dimensional coordinates, which can be directly used to control the movement of the massage equipment, achieve accurate spatial positioning and force control, and improve the comfort and effectiveness of massage.

[0010] Optionally, matching corresponding physiological parameters in a preset physiological parameter database based on the EEG signal specifically includes: preprocessing the collected EEG signal to obtain preprocessed EEG signal segments, the preprocessing including denoising, filtering and segmentation; extracting characteristic parameters of the EEG signal segments, the characteristic parameters including frequency domain features, time domain features and time-frequency domain features; inputting the characteristic parameters into a preset physiological state classification model, and determining the corresponding physiological state category through the preset physiological state classification model, the preset physiological state classification model being constructed by a machine learning algorithm; and obtaining physiological parameters corresponding to the physiological state category in the preset physiological parameter database.

[0011] By adopting the above technical solution, through preprocessing, feature extraction and physiological state classification of the collected EEG signals, the user's physiological state can be objectively and accurately evaluated, providing a reliable basis for subsequent massage plan matching.

[0012] Optionally, before inputting the characteristic parameters into the preset physiological state classification model, the method also includes: obtaining a training data set for constructing the preset physiological state classification model, the training data set including EEG signal samples of multiple users and corresponding physiological state labels; performing feature extraction on the training data set to obtain a training feature set; training the training feature set using a machine learning algorithm to obtain the preset physiological state classification model; testing the preset physiological state classification model using a test data set, and optimizing the preset physiological state classification model according to the test results until the classification accuracy is greater than or equal to a preset threshold.

[0013] By adopting the above technical solution and introducing a machine learning algorithm based on large-scale EEG signal samples, the accuracy and robustness of the classification model can be effectively improved, providing more reliable support for subsequent physiological parameter matching and massage scheme optimization. Specifically, the method first obtains a training data set consisting of EEG signal samples and corresponding physiological state labels of multiple users to ensure the diversity of samples and the accuracy of labels. Then, feature extraction is performed on the training data set to obtain a training feature set containing rich physiological information. On this basis, a machine learning algorithm is used to train the training feature set to construct a preset physiological state classification model. In order to further optimize the performance of the model, the method also introduces a test data set to compare the prediction results of the preset physiological state classification model on the test set with the true label to evaluate the classification accuracy of the model. Through the automatic classification and optimization of the machine learning algorithm, an efficient preset physiological state recognition model is established, which provides important data support and algorithm guarantee for the realization of intelligent and personalized acupoint massage.

[0014] Optionally, performing a preliminary massage on the face to be massaged according to the first massage scheme and the acupoint distribution information specifically includes: controlling the massage head to move to the relative position of the corresponding acupoint according to the massage sequence, and controlling the massage head to perform corresponding massage actions at the corresponding acupoints; in the process of performing the massage actions, controlling the massage head to apply corresponding pressure and speed according to the massage intensity and the massage frequency.

[0015] By adopting the above technical solution, the movement trajectory, massage intensity and frequency of the massage head can be accurately controlled according to the massage plan and acupoint distribution information, so as to achieve highly simulated and personalized acupoint massage and provide a comfortable, safe and effective massage experience. At the same time, the method converts the abstract parameters in the massage plan into specific control instructions, realizes seamless connection from plan to execution, and improves the safety and stability of massage.

[0016] Optionally, after determining whether the physiological parameters of the user have changed, the method further includes: if it is determined that the physiological parameters of the user have not changed, determining whether the current massage duration has reached the preset massage duration; if it is determined that the current massage duration has not reached the preset massage duration, continuing to massage according to the first massage plan; if it is determined that the current massage duration has reached the preset massage duration, stopping the massage and recording massage data, the massage data including massage duration, massage intensity and massage effect score.

[0017] By adopting the above technical solution, through real-time judgment of changes in user physiological parameters and compliance with massage duration standards, massage strategies can be flexibly and dynamically adjusted, and massage data can be recorded completely and accurately to achieve intelligent and personalized full-process massage management.

[0018] Optionally, after matching the corresponding first massage plan according to the physiological parameters, the method also includes: generating a three-dimensional massage animation according to the first massage plan; displaying the three-dimensional massage animation to the user, and receiving the user's modification suggestions for the three-dimensional massage animation; if the user's modification suggestions are received, adjusting the first massage plan according to the modification suggestions to obtain a modified first massage plan, and replacing the first massage plan according to the modified first massage plan.

[0019] By adopting the above technical solution, by converting the first massage plan into an intuitive and realistic three-dimensional massage animation and supporting users to modify and provide feedback on the animation in real time, the user's understanding and acceptance of the massage plan can be significantly improved, and the massage process can be visualized and personalized.

[0020] In a second aspect of the present application, there is provided an acupoint massage device through brain-computer monitoring feedback, the device comprising an acquisition module and a processing module, wherein: the acquisition module is used to acquire acupoint distribution information of a face to be massaged; the processing module is used to collect the user's electroencephalogram signal through a brain-computer interface, and match the corresponding physiological parameters in a preset physiological parameter database according to the electroencephalogram signal, and the preset physiological parameter database includes the correspondence between the electroencephalogram signal and the physiological parameter; the processing module is also used to match the corresponding first massage scheme in a preset massage scheme database according to the physiological parameters, the first massage scheme includes a massage sequence, a massage technique, a massage intensity and a massage frequency, and the preset massage scheme database includes the correspondence between the physiological parameters and the massage scheme; the processing module is also used to perform a preliminary massage on the face to be massaged according to the first massage scheme and the acupoint distribution information; the processing module is also used to determine whether the physiological parameters of the user have changed during the preliminary massage; the processing module is also used to obtain a second massage scheme corresponding to the changed physiological parameters if it is determined that the physiological parameters of the user have changed; the processing module is also used to perform a secondary massage on the face to be massaged according to the second massage scheme until a preset massage duration is reached.

[0021] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The acupoint massage method based on brain-computer monitoring feedback can achieve accurate and personalized acupoint massage by obtaining the acupoint distribution information of the face to be massaged, collecting the user's EEG signals, matching the corresponding physiological parameters according to the EEG signals, and then matching the corresponding massage scheme according to the physiological parameters. During the massage process, by judging whether the user's physiological parameters have changed, the massage scheme can be dynamically adjusted to adapt to the changes in the user's physiological state in real time, providing a more intelligent and comfortable massage experience. At the same time, this method uses brain-computer interface technology to collect EEG signals, which can directly reflect the user's physiological and psychological state, which is more objective and reliable than traditional subjective feedback. By establishing a mapping relationship between EEG signals, physiological parameters, and massage schemes, closed-loop control and dynamic optimization of the massage process are achieved, which improves the pertinence and effectiveness of massage. In addition, this method also introduces the link of secondary massage, which can adjust the massage strategy in time according to the changes in the user's physiological parameters after the initial massage, and further improve the massage effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a method for acupoint massage through brain-computer monitoring feedback disclosed in an embodiment of the present application; Figure 2 It is a module schematic diagram of an acupoint massage device through brain-computer monitoring feedback disclosed in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0025] Explanation of the reference numerals: 201, acquisition module; 202, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0028] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] This application provides a method of acupoint massage through brain-computer monitoring feedback, referring to Figure 1 , Figure 1 1 is a flow chart of a method for acupoint massage based on brain-computer monitoring feedback provided by an embodiment of the present application. The method is applied to a server, and the server is used to execute an acupoint massage program based on brain-computer monitoring feedback. The method includes steps S101 to S107, and the steps are as follows: Step S101: Obtaining acupuncture point distribution information of the face to be massaged.

[0030] In step S101, the acupoint distribution information of the face to be massaged is obtained, specifically including: obtaining a three-dimensional human body model, the three-dimensional human body model including spatial coordinate information of various parts of the human body; determining a local three-dimensional model corresponding to the face to be massaged according to the position of the face to be massaged in the three-dimensional human body model; obtaining the acupoint names and acupoint coordinates corresponding to the local three-dimensional model in a preset acupoint database; mapping the acupoint coordinates to the coordinate system of the local three-dimensional model to obtain the relative coordinates of the acupoints of the face to be massaged in the local three-dimensional model, and using the relative coordinates as the acupoint distribution information of the face to be massaged.

[0031] Specifically, the server obtains the acupuncture point distribution information of the face to be massaged. First, the server obtains a general human body three-dimensional model, which contains the spatial coordinate information of various parts of the human body. The human body three-dimensional model can be obtained by scanning a real human body, and needs to be standardized to ensure that the proportion and size of the model conform to the general characteristics of the human body.

[0032] Next, the server determines the corresponding local 3D model based on the position of the face to be massaged in the 3D model of the human body. This step can be achieved in the following ways: first, collect the user's facial image or point cloud data through a camera or other sensor; then, use a face recognition algorithm to process the collected data and locate the position of the face in the image or point cloud; finally, map the located facial position to the 3D model of the human body and extract the local 3D model corresponding to the face.

[0033] For example, if a user wants to massage his left cheek, the server first collects the user's frontal facial image through the camera, and then uses the deep learning-based face recognition algorithm to locate the position of the left cheek in the image and calculate the coordinates of the position in three-dimensional space. Next, the server maps the coordinates to a general human 3D model and extracts the local 3D model corresponding to the left cheek, which usually includes the skin, muscles, bones and other structures of the left cheek and its surroundings within a certain range.

[0034] After obtaining the local 3D model, the server queries the preset acupoint database for the acupoint name and coordinates corresponding to the local model. The preset acupoint database is pre-built and contains the acupoint distribution information of various parts of the human body. Each acupoint has a corresponding name and 3D coordinates. The server can use the features of the local 3D model, such as the topological structure, to match it in the preset acupoint database and find the corresponding acupoint.

[0035] Finally, the server maps the queried acupoint coordinates to the coordinate system of the local three-dimensional model to obtain the relative coordinates of the acupoint in the local three-dimensional model. The transformed relative coordinates represent the position of the acupoint relative to the local three-dimensional model, and the server uses it as the acupoint distribution information of the face to be massaged for subsequent massage operations.

[0036] Step S102: Collect the user's EEG signals through the brain-computer interface, and match corresponding physiological parameters in a preset physiological parameter database based on the EEG signals.

[0037] In step S102, according to the EEG signal, the corresponding physiological parameters are matched in the preset physiological parameter database, specifically including: preprocessing the collected EEG signal to obtain preprocessed EEG signal segments, the preprocessing including denoising, filtering and segmentation; extracting characteristic parameters of the EEG signal segments, the characteristic parameters including frequency domain features, time domain features and time-frequency domain features; inputting the characteristic parameters into a preset physiological state classification model, and determining the corresponding physiological state category through the preset physiological state classification model, the preset physiological state classification model is constructed by a machine learning algorithm; in the preset physiological parameter database, obtaining the physiological parameters corresponding to the physiological state category.

[0038] Specifically, the server preprocesses the collected EEG signals. EEG signals contain various noises and interferences, such as power supply interference and electromyographic interference, and the server uses a denoising algorithm to clean them up. Denoising methods include wavelet transform and adaptive filtering. After denoising, the server also filters the EEG signals to remove irrelevant frequency components. Filtering methods include bandpass filtering, low-pass filtering, etc. Finally, the server divides the filtered EEG signals according to a certain time window to obtain a series of EEG signal segments, each of which corresponds to an EEG signal of a certain duration.

[0039] Next, the server extracts the characteristic parameters of the EEG signal fragment. The characteristic parameters include frequency domain features, time domain features, and time-frequency domain features. Frequency domain features reflect the energy distribution of EEG signals in different frequency components, including power spectrum density, band energy, etc.; time domain features reflect the statistical characteristics of EEG signals in time, including mean, variance, skewness, etc.; time-frequency domain features integrate information in time domain and frequency domain, including short-time Fourier transform, wavelet transform, etc.

[0040] After extracting the characteristic parameters, the server inputs them into the preset physiological state classification model to determine the user's current physiological state category. The preset physiological state classification model is pre-trained using machine learning algorithms, including support vector machines, random forests, convolutional neural networks, etc. The input of the preset physiological state classification model is the characteristic parameters of the EEG signal, and the output is the corresponding physiological state category, such as relaxation, excitement, fatigue, etc.

[0041] For example, suppose the server collects a segment of the user's EEG signal at a certain moment, and after preprocessing and feature extraction, obtains a set of frequency domain features and time domain features. Then, the server inputs this set of features into the preset physiological state classification model, which determines that the user is currently in a relaxed state based on the characteristic parameters. Finally, the server queries the preset physiological parameter database for the physiological parameters corresponding to the user's current physiological state category. The preset physiological parameter database stores various physiological indicators of the human body under different physiological states. The server finds matching physiological parameters in the preset physiological parameter database based on the physiological state category identified by the classification model, which serves as the basis for generating subsequent massage plans.

[0042] In a possible implementation, before inputting the characteristic parameters into the preset physiological state classification model, the method also includes: obtaining a training data set for constructing the preset physiological state classification model, the training data set including EEG signal samples of multiple users and corresponding physiological state labels; performing feature extraction on the training data set to obtain a training feature set; training the training feature set using a machine learning algorithm to obtain a preset physiological state classification model; testing the preset physiological state classification model using a test data set, and optimizing the preset physiological state classification model according to the test results until the classification accuracy is greater than or equal to a preset threshold.

[0043] Specifically, the server obtains a training data set for building a classification model. The training data set includes EEG signal samples of multiple users and corresponding physiological state labels. EEG signal samples can be collected through a brain-computer interface and obtained after preprocessing and segmentation. Physiological state labels need to be obtained by other means. By recruiting volunteers, they are asked to conduct a series of EEG signal collection experiments in different physiological states (such as relaxation, excitement, fatigue, etc.). During the experiment, volunteers need to fill out a self-assessment scale to describe their subjective feelings. At the same time, professional medical personnel will evaluate and label the physiological state of the volunteers. In the end, the server can obtain a data set containing hundreds or thousands of EEG signal samples, each with a corresponding physiological state label. Then, the server extracts features from the training data set to obtain a training feature set. Feature extraction methods include frequency domain analysis, time domain analysis, and time-frequency analysis.

[0044] After obtaining the training feature set, the server uses a machine learning algorithm to train it and obtains a preset physiological state classification model. Finally, the server uses an independent test data set to test the trained preset physiological state classification model to evaluate its performance and generalization ability. The test data set has the same data format and label system as the training data set, but the sample data is completely non-overlapping. The server inputs the features of the test data set into the preset physiological state classification model to obtain the predicted physiological state category, which is then compared with the actual label to calculate the classification accuracy. If the test result does not meet the performance requirements of the preset accuracy threshold, the server will further optimize the preset physiological state classification model, such as adjusting algorithm parameters, adding training data, and modifying feature extraction methods, until the classification accuracy is greater than or equal to the preset accuracy threshold.

[0045] Step S103: According to the physiological parameters, a corresponding first massage program is matched in a preset massage program database, wherein the first massage program includes a massage sequence, massage techniques, massage intensity, and massage frequency, and the preset massage program database includes a correspondence between the physiological parameters and the massage program.

[0046] In step S103, the server constructs a preset massage program database. The preset massage program database stores the correspondence between various physiological parameters and massage programs. Physiological parameters may include multiple dimensions such as heart rate, blood pressure, skin electricity, and brain electricity, reflecting the overall physiological state of the user. The massage program includes elements such as massage sequence, massage technique, massage intensity, and massage frequency, defining a complete set of massage processes.

[0047] For example, the following correspondence may exist in the database: when the user's heart rate is 60-80 beats / min, blood pressure is 90-120 mmHg, and skin electrical level is low, the corresponding massage plan is to first massage the Baihui, Yintang and other acupoints on the head, then massage the Fengchi, Jianjing and other acupoints on the shoulders and neck, and finally massage the Hegu, Neiguan and other acupoints on the hands; the massage technique is mainly gentle; the massage intensity is controlled at level 1-2; the massage frequency is 2-3 times for each acupoint. These correspondences can be summarized based on traditional Chinese medicine theory, modern medical research and a large amount of clinical practice experience. The server builds a wide-coverage preset massage plan database through manual entry or automated data mining.

[0048] Secondly, the server matches the preset massage program database according to the user's real-time physiological parameters to find the most relevant first massage program. This step can use a rule-based matching algorithm or a similarity-based matching algorithm. The rule-based matching algorithm uses a decision tree and expert system to abstract the correspondence between physiological parameters and massage programs into a series of if-then rules. For example, if the user's heart rate is 60-80 beats / min and blood pressure is 90-120mmHg, select program A; if the heart rate is 80-100 beats / min and blood pressure is 120-140mmHg, select program B. The server judges the user's physiological parameters one by one according to these rules, and finally determines the matching massage program.

[0049] The similarity-based matching algorithm uses the classification and regression models in machine learning, takes physiological parameters as features, and massage plans as labels to train a mapping function. When new user physiological parameters are input, the function can calculate the similarity score with each massage plan in the preset massage plan database, and then select the plan with the highest score as the initial plan.

[0050] Finally, the server returns the first massage plan obtained by matching as a guide for subsequent massage steps. The initial plan includes key elements such as massage sequence, massage technique, massage intensity and massage frequency, providing a basis for a personalized massage experience.

[0051] In a possible implementation, after step S103, the method further includes: generating a three-dimensional massage animation according to the first massage plan; displaying the three-dimensional massage animation to the user, and receiving the user's modification suggestions for the three-dimensional massage animation; if the user's modification suggestions are received, adjusting the first massage plan according to the modification suggestions to obtain a modified first massage plan, and replacing the first massage plan according to the modified first massage plan.

[0052] Specifically, the server generates a corresponding three-dimensional massage animation according to the first massage scheme. For example, the server can use a game engine such as Unity3D, or a three-dimensional modeling software such as Maya, to build a virtual massage scene. In the scene, there is a three-dimensional human model and a pair of virtual massagers. The server controls the virtual massager to perform massage operations on the human model according to the sequence and essentials in the massage scheme, and renders the corresponding picture in real time.

[0053] Secondly, the server displays the generated 3D massage animation to the user and receives the user's modification suggestions. This step can be implemented in various forms such as mobile terminals, which is convenient for users to watch the animation and provide feedback. After receiving the user's modification suggestions, the server needs to adjust and optimize the first massage plan based on these suggestions. The adjustment process can be automatic or semi-automatic, and it is necessary to comprehensively consider the rationality, feasibility and coordination with the overall plan of the suggestions.

[0054] For example, if the user feels that the massage intensity is too strong, the server can automatically reduce the intensity parameter in the first massage plan by one level; if the user feels that the massage sequence is not reasonable, the server can rearrange the order of massage according to the distribution of acupuncture points. After the adjustment is completed, the server will replace the original plan with the modified first massage plan and regenerate the corresponding massage animation for the user to watch and confirm again. The server repeats the above steps until the user is completely satisfied with the plan.

[0055] Step S104: performing preliminary massage on the face to be massaged according to the first massage scheme and the acupoint distribution information.

[0056] In step S104, a preliminary massage is performed on the face to be massaged according to the first massage plan and the acupoint distribution information, specifically including: controlling the massage head to move to the relative position of the corresponding acupoint according to the massage sequence, and controlling the massage head to perform corresponding massage actions at the corresponding acupoints; in the process of performing the massage actions, controlling the massage head to apply corresponding pressure and speed according to the massage intensity and massage frequency.

[0057] Specifically, the server converts the scheme description in the first massage scheme into specific control instructions and sends them to the massage device. The massage device is usually composed of one or more massage heads, which can move and rotate freely in three-dimensional space. Each massage head has a certain pressure sensing and position tracking capability, and can accurately control the massage intensity and massage frequency according to the instructions of the server. The server determines the movement path of the massage head according to the order specified in the massage scheme. The server can use the three-dimensional facial model and the relative coordinates of the acupoints obtained in step S101, through coordinate transformation and path planning algorithm, to generate an optimal path covering all target acupoints.

[0058] For example, if the first massage plan requires massaging the Yintang point first, then massaging the temple, and finally massaging the Renzhong point, the server will control the massage head to move to the position of the Yintang point first, then along a smooth curve, pass the temple, and finally reach the Renzhong point.

[0059] Secondly, the server controls the massage head to perform the corresponding massage action on each acupoint according to the massage technique specified in the massage plan. Massage techniques include light kneading, pressing, pushing, tapping, etc. Each technique has specific parameters such as strength, frequency, duration, etc. The server converts these parameters into control instructions and transmits them to the drive motor and pressure sensor of the massage head to achieve precise control of the massage process.

[0060] For example, if the massage plan requires gentle kneading of the Yintang acupoint, the server will control the massage head to rotate and slide on the acupoint in a small range, low pressure, and high frequency. If the massage plan requires pressing on the temple, the server will control the massage head to apply vertical pressure on the acupoint, hold it for a certain period of time, and then slowly release it.

[0061] Step S105: During the initial massage, determine whether the user's physiological parameters have changed.

[0062] In step S105, the server continuously obtains the user's real-time physiological data. After receiving multiple physiological data, the server needs to perform preprocessing and feature extraction to obtain a set of key indicators reflecting the user's current physiological state. These indicators may include heart rate, heart rate variability, blood pressure, skin electricity, brain electrical rhythm, etc., which are similar to the indicators used in step S102. The server can use the same signal processing algorithm and feature extraction method as S102 to convert the original physiological signal into a standardized feature vector.

[0063] Secondly, the server compares the extracted physiological features with the initial state to determine whether a significant change has occurred. The initial state here refers to the user's physiological state when the first massage plan is determined in step S103, which can be represented by a set of baseline feature vectors. The server quantifies the degree of change in the physiological state by calculating the similarity or distance between the current feature vector and the baseline feature vector. If it is determined that the degree of change is greater than a preset threshold, it is determined that the physiological parameter has changed.

[0064] For example, the server records that the user's heart rate is 70 beats / minute, blood pressure is 120 / 80 mmHg, and EEG is mainly alpha waves in the initial state. During the massage, if the heart rate suddenly rises to 90 beats / minute, blood pressure rises to 140 / 90 mmHg, and EEG has more beta waves, the server can determine that the user's physiological state has changed significantly, which may be due to excitement or tension caused by the massage.

[0065] In a possible implementation, after step S105, the method further includes: if it is determined that the user's physiological parameters have not changed, determining whether the current massage duration has reached the preset massage duration; if it is determined that the current massage duration has not reached the preset massage duration, continuing to massage according to the first massage plan; if it is determined that the current massage duration has reached the preset massage duration, stopping the massage and recording the massage data, which includes the massage duration, massage intensity and massage effect score.

[0066] Specifically, the server continuously records and updates the current massage duration during the entire massage process. The massage duration refers to the time elapsed from the start of the massage to the current moment, which can be expressed in units such as seconds and minutes. The server can use a timer or clock to accurately measure the time, and synchronously update the massage duration each time new physiological data is received. The preset massage duration is determined when the first massage plan is formulated, and represents the target duration of the entire massage process. The preset massage duration can be set according to factors such as the purpose, part, and technique of the massage, and can also be adjusted according to the user's preferences and tolerance, which is not limited in this application. Generally speaking, the duration of a facial massage can be set to about 10-20 minutes, which can fully exert the effect of the massage without being too tiring or irritating the skin.

[0067] Secondly, after determining that the user's physiological parameters have not changed, the server compares the current massage duration with the preset massage duration to determine whether the massage has reached the target duration. The server achieves this through a simple numerical comparison, that is, subtracting the preset duration from the current duration. If the result is greater than or equal to zero, it means that the preset massage duration has been reached or exceeded, otherwise it means that it has not been reached. If the current duration has not reached the preset massage duration, the server will control the massage device to continue to execute the first massage plan until the duration reaches the target. In this process, the server will dynamically adjust the position, speed, strength and other parameters of the massage head according to the requirements of the plan, and will also continue to monitor the user's physiological feedback to ensure the safety and comfort of the massage.

[0068] For example, if the preset massage duration is 15 minutes and the current massage duration is 10 minutes, and the user's physiological parameters remain stable, the server will control the massage device to continue for another 5 minutes and complete the remaining massage steps and techniques within these 5 minutes. If the user's physiological parameters suddenly change within these 5 minutes, the server will also initiate the corresponding processing and feedback mechanism.

[0069] If the current duration has reached or exceeded the preset duration, the server will control the massage device to stop all massage actions and enter the massage end state. At this time, the server will record and save the key data of the entire massage process, including the actual massage duration and massage intensity. These data can be used for subsequent statistical analysis and effect evaluation.

[0070] In addition to objective data indicators, the server can also actively request the user to rate the massage effect after the massage is over. The massage effect rating can be in the form of five-star or percentage. The server also uses the user's subjective rating as an important reference for the massage effect and stores it in the massage history record together with the objective data.

[0071] Step S106: If it is determined that the physiological parameters of the user have changed, a second massage program corresponding to the changed physiological parameters is obtained.

[0072] In step S106, the server uses the changed physiological parameters as new search conditions, performs matching and query in the preset massage program database, and obtains a second massage program.

[0073] Step S107: According to the second massage plan, a second massage is performed on the face to be massaged until the preset massage time is reached.

[0074] In step S107, the server converts the second massage plan into a series of specific control instructions and sends them to the massage device, so that the massage device massages the user according to the second massage plan.

[0075] Reference Figure 2 The present application also provides an acupoint massage method through brain-computer monitoring feedback, the device is a server, the server includes an acquisition module 201 and a processing module 202, wherein: the acquisition module 201 is used to obtain the acupoint distribution information of the face to be massaged; the processing module 202 is used to collect the user's EEG signal through the brain-computer interface, and according to the EEG signal, match the corresponding physiological parameters in the preset physiological parameter database, the preset physiological parameter database includes the correspondence between the EEG signal and the physiological parameters; the processing module 202 is also used to match the corresponding first massage plan in the preset massage plan database according to the physiological parameters, the first massage plan includes pressing The preset massage program database includes the correspondence between physiological parameters and massage programs; the processing module 202 is also used to perform preliminary massage on the face to be massaged according to the first massage program and the acupoint distribution information; the processing module 202 is also used to determine whether the user's physiological parameters have changed during the preliminary massage; the processing module 202 is also used to obtain a second massage program corresponding to the changed physiological parameters if it is determined that the user's physiological parameters have changed; the processing module 202 is also used to perform a secondary massage on the face to be massaged according to the second massage program until the preset massage time is reached.

[0076] In a possible implementation, the acquisition module 201 acquires the acupoint distribution information of the face to be massaged, specifically including: the acquisition module 201 acquires a three-dimensional model of the human body, and the three-dimensional model of the human body includes spatial coordinate information of various parts of the human body; the processing module 202 determines the local three-dimensional model corresponding to the face to be massaged according to the position of the face to be massaged in the three-dimensional model of the human body; the processing module 202 acquires the acupoint names and acupoint coordinates corresponding to the local three-dimensional model from a preset acupoint database; the processing module 202 maps the acupoint coordinates to the coordinate system of the local three-dimensional model, obtains the relative coordinates of the acupoints of the face to be massaged in the local three-dimensional model, and uses the relative coordinates as the acupoint distribution information of the face to be massaged.

[0077] In a possible implementation, the processing module 202 matches corresponding physiological parameters in a preset physiological parameter database according to the EEG signal, specifically including: the processing module 202 preprocesses the collected EEG signal to obtain preprocessed EEG signal segments, the preprocessing including denoising, filtering and segmentation; the processing module 202 extracts characteristic parameters of the EEG signal segments, the characteristic parameters including frequency domain features, time domain features and time-frequency domain features; the processing module 202 inputs the characteristic parameters into a preset physiological state classification model, and determines the corresponding physiological state category through the preset physiological state classification model, the preset physiological state classification model is constructed by a machine learning algorithm; the processing module 202 obtains the physiological parameters corresponding to the physiological state category in the preset physiological parameter database.

[0078] In a possible implementation, the processing module 202 performs a preliminary massage on the face to be massaged according to the first massage plan and the acupoint distribution information, specifically including: the processing module 202 controls the massage head to move to the relative position of the corresponding acupoint according to the massage sequence, and controls the massage head to perform corresponding massage actions at the corresponding acupoint; in the process of performing the massage action, the processing module 202 controls the massage head to apply corresponding pressure and speed according to the massage intensity and massage frequency.

[0079] In a possible implementation, the processing module 202 performs a preliminary massage on the face to be massaged according to the first massage plan and the acupoint distribution information, specifically including: the processing module 202 controls the massage head to move to the relative position of the corresponding acupoint according to the massage sequence, and controls the massage head to perform corresponding massage actions at the corresponding acupoint; in the process of performing the massage action, the processing module 202 controls the massage head to apply corresponding pressure and speed according to the massage intensity and massage frequency.

[0080] In a possible implementation, after the processing module 202 determines whether the user's physiological parameters have changed, the method further includes: if the processing module 202 determines that the user's physiological parameters have not changed, then determining whether the current massage duration has reached the preset massage duration; if the processing module 202 determines that the current massage duration has not reached the preset massage duration, then continuing to massage according to the first massage plan; if the processing module 202 determines that the current massage duration has reached the preset massage duration, then stopping the massage and recording the massage data, the massage data including the massage duration, the massage intensity and the massage effect score.

[0081] In a possible implementation, after the processing module 202 matches the corresponding first massage plan according to the physiological parameters, the method further includes: the processing module 202 generates a three-dimensional massage animation according to the first massage plan; the processing module 202 displays the three-dimensional massage animation to the user, and receives the user's modification opinions on the three-dimensional massage animation; if the processing module 202 receives the user's modification opinions, it adjusts the first massage plan according to the modification opinions to obtain a modified first massage plan, and replaces the first massage plan according to the modified first massage plan.

[0082] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0083] The present application also provides an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0084] The communication bus 302 is used to realize the connection and communication between these components.

[0085] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0086] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0087] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0088] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program for an acupoint massage method through brain-computer monitoring feedback.

[0089] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the memory 305 to store an application program of an acupoint massage method through brain-computer monitoring feedback. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0090] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments.

[0091] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0093] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0095] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0096] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0097] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method of acupoint massage through brain-computer monitoring feedback, characterized in that: The method comprises: Obtaining acupuncture point distribution information of the face to be massaged; Collecting the user's EEG signals through a brain-computer interface, and matching corresponding physiological parameters in a preset physiological parameter database according to the EEG signals, wherein the preset physiological parameter database includes a correspondence between the EEG signals and the physiological parameters; According to the physiological parameters, a corresponding first massage program is matched in a preset massage program database, wherein the first massage program includes a massage sequence, a massage technique, a massage intensity, and a massage frequency, and the preset massage program database includes a correspondence between the physiological parameters and the massage program; Performing a preliminary massage on the face to be massaged according to the first massage scheme and the acupoint distribution information; During the preliminary massage, determining whether a physiological parameter of the user changes; If it is determined that the physiological parameter of the user has changed, obtaining a second massage plan corresponding to the changed physiological parameter; According to the second massage scheme, a second massage is performed on the face to be massaged until a preset massage time is reached.

2. The method according to claim 1, characterized in that The step of obtaining the acupuncture point distribution information of the face to be massaged specifically includes: Acquire a three-dimensional human body model, wherein the three-dimensional human body model includes spatial coordinate information of various parts of the human body; Determining a local three-dimensional model corresponding to the face to be massaged according to the position of the face to be massaged in the three-dimensional human body model; In a preset acupoint database, obtaining the acupoint name and acupoint coordinates corresponding to the local three-dimensional model; The acupoint coordinates are mapped to the coordinate system of the local three-dimensional model to obtain the relative coordinates of the acupoints of the face to be massaged in the local three-dimensional model, and the relative coordinates are used as the acupoint distribution information of the face to be massaged.

3. The method according to claim 1, characterized in that: The step of matching the corresponding physiological parameters in a preset physiological parameter database according to the EEG signal specifically includes: Preprocessing the collected EEG signals to obtain preprocessed EEG signal segments, wherein the preprocessing includes denoising, filtering and segmentation; Extracting characteristic parameters of the EEG signal segment, wherein the characteristic parameters include frequency domain features, time domain features, and time-frequency domain features; Inputting the characteristic parameters into a preset physiological state classification model, and determining the corresponding physiological state category through the preset physiological state classification model, wherein the preset physiological state classification model is constructed by a machine learning algorithm; In the preset physiological parameter database, the physiological parameters corresponding to the physiological state category are obtained.

4. The method according to claim 3, characterized in that Before inputting the characteristic parameters into a preset physiological state classification model, the method further includes: Acquire a training data set for constructing the preset physiological state classification model, the training data set comprising EEG signal samples and corresponding physiological state labels of multiple users; Performing feature extraction on the training data set to obtain a training feature set; Using a machine learning algorithm to train the training feature set to obtain the preset physiological state classification model; The preset physiological state classification model is tested using a test data set, and the preset physiological state classification model is optimized according to the test result until the classification accuracy is greater than or equal to a preset threshold.

5. The method according to claim 1, characterized in that The step of performing preliminary massage on the face to be massaged according to the first massage scheme and the acupoint distribution information specifically includes: According to the massage sequence, the massage head is controlled to move to the relative position of the corresponding acupuncture point, and the massage head is controlled to perform the corresponding massage action at the corresponding acupuncture point; During the massage action, the massage head is controlled to apply corresponding pressure and speed according to the massage intensity and the massage frequency.

6. The method according to claim 1, characterized in that After determining whether the physiological parameters of the user have changed, the method further includes: If it is determined that the physiological parameters of the user have not changed, determining whether the current massage duration reaches the preset massage duration; If it is determined that the current massage duration has not reached the preset massage duration, continue to perform massage according to the first massage plan; If it is determined that the current massage duration has reached the preset massage duration, the massage is stopped and massage data is recorded, wherein the massage data includes massage duration, massage intensity and massage effect score.

7. The method according to claim 1, characterized in that After matching the corresponding first massage scheme according to the physiological parameters, the method further includes: generating a three-dimensional massage animation according to the first massage plan; Displaying the three-dimensional massage animation to the user, and receiving the user's modification suggestions on the three-dimensional massage animation; If the modification suggestion of the user is received, the first massage plan is adjusted according to the modification suggestion to obtain a modified first massage plan, and the first massage plan is replaced according to the modified first massage plan.

8. An acupoint massage device with brain-computer monitoring feedback, characterized in that: The device comprises an acquisition module (201) and a processing module (202), wherein: The acquisition module (201) is used to acquire acupuncture point distribution information of the face to be massaged; The processing module (202) is used to collect the user's brain electrical signals through the brain-computer interface, and match the corresponding physiological parameters in a preset physiological parameter database according to the brain electrical signals, wherein the preset physiological parameter database includes the corresponding relationship between the brain electrical signals and the physiological parameters; The processing module (202) is further used to match a corresponding first massage scheme in a preset massage scheme database according to the physiological parameters, wherein the first massage scheme includes a massage sequence, a massage technique, a massage intensity and a massage frequency, and the preset massage scheme database includes a correspondence between the physiological parameters and the massage scheme; The processing module (202) is further used to perform a preliminary massage on the face to be massaged according to the first massage scheme and the acupoint distribution information; The processing module (202) is further used to determine whether the physiological parameters of the user have changed during the preliminary massage; The processing module (202) is further configured to obtain a second massage plan corresponding to the changed physiological parameter if it is determined that the physiological parameter of the user has changed; The processing module (202) is further used to perform a second massage on the face to be massaged according to the second massage plan until a preset massage time is reached.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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