Method and system for adjusting and controlling fascia pain point massage robot

Abstract

The invention belongs to the technical field of massage control, and discloses a fascia pain point massage robot adjusting and controlling method and system. The method comprises the steps of obtaining initial information of a user, and generating a massage path; acquiring parameter information of key point positions on the massage path, and generating massage pressure of each key point position; feedback information and parameter information of each key point in the massage process are obtained, and the reasonability of massage pressure of the key points is judged; dynamically adjusting the massage pressure based on the judgment result; when the massage path is determined, the appropriate meridian channel can be selected as the massage path according to the association degree condition of each meridian channel and all the pain points, the traditional Chinese medicine meridians and the muscle pain points are integrated to determine the massage path, the treatment accuracy can be improved, and symptom recovery is facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of massage control, more particularly, the present application relates to a fascia trigger point massage robot adjustment control method and system. BACKGROUND

[0002] In modern society, with the acceleration of people's life pace and the increase of work pressure, muscle fatigue and fascia trigger point related problems are increasingly common. Fascia is a connective tissue that covers the surface of muscles and other tissues. When fascia appears trigger points, it will cause local pain, muscle stiffness and limited activity, etc. Symptoms seriously affect people's quality of life and health level. Therefore, it is of great practical significance to seek effective fascia trigger point massage treatment means.

[0003] With the development of science and technology, massage robots gradually enter people's vision. Massage robots can provide standardized and repeatable massage operations, effectively avoiding the subjectivity and instability of manual massage. However, most existing massage robots can only massage according to the preset fixed program, and cannot dynamically adjust according to the individual differences of users and the real-time feedback in the massage process, which is easy to lead to poor massage effect and even cause discomfort to users.

[0004] In view of this, the present application proposes a fascia trigger point massage robot adjustment control method and system to solve the above problems. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned purposes, the present application provides the following technical solutions: A fascia trigger point massage robot adjustment control method, comprising: Constructing a 3D virtual dissection layer, obtaining initial information of a user, mapping the initial information to the 3D virtual dissection layer, and generating a massage path; Determining key points on the massage path, obtaining parameter information of each key point, and generating massage pressure of each key point based on the parameter information; Obtaining feedback information and parameter information of each key point during the massage process, generating a judgment coefficient based on the feedback information and the parameter information, and judging the rationality of the massage pressure corresponding to each key point according to the judgment coefficient; Based on the judgment result, dynamically adjusting the massage pressure.

[0006] Further, the method for generating the massage path is: The initial information includes body surface temperature information of the user and appeal information, a subjective pain point is determined based on the appeal information of the user, a user pain point area is determined based on the appeal information of the user, and is recorded as a user target area, an infrared thermal imager technology is integrated to capture a body surface temperature abnormal point in the user target area and mark it as an objective pain point, and the subjective pain point and the objective pain point are collectively referred to as a user pain point; A plurality of meridian circulation routes are drawn in the 3D virtual dissection layer, and the circulation route of each meridian is modeled as a meridian channel with a width, and all user pain points are sequentially mapped into the 3D virtual dissection layer; The spatial correlation degree of each user pain point and all meridian channels is calculated, and a channel pain point matrix is constructed, wherein the rows in the channel pain point matrix represent all user pain points, the columns represent all meridian channels, and the matrix elements are correlation degrees; The correlation degrees of the meridian channels are summed to obtain the total contribution degree of each meridian channel; The meridian channel with the highest total contribution degree is selected as the massage path.

[0007] Further, the correlation degree calculation method is: The coverage range of each meridian channel is set, and the core radius and the influence radius of each meridian channel are set, and the vertical distance from each user pain point to the center line of each meridian channel is calculated; If the vertical distance is less than or equal to the core radius, the correlation degree is 1; If the vertical distance is greater than the core radius and less than or equal to the influence radius, the correlation degree is 1 minus the ratio coefficient, and the ratio coefficient is the ratio of the vertical distance minus the core radius to the influence radius minus the core radius; If the vertical distance is greater than the influence radius, the correlation degree is 0.

[0008] Further, the massage pressure generation method is: A plurality of key points are set on the massage path, parameter information of the key points is obtained, the parameter information includes muscle hardness parameters, muscle electrical amplitude parameters and skin deformation parameters, and state values are generated by fusing and analyzing the muscle hardness parameters, the muscle electrical amplitude parameters and the skin deformation parameters; Each key point is taken as the center to set an influence circle, and the number of user pain points in each influence circle is obtained; the massage value of each key point is calculated through exponential operation with the state value as the base and the number of user pain points as the exponent; A massage pressure reference table is constructed, the massage pressure reference table includes the massage value threshold interval corresponding to each massage pressure; the massage pressure of each key point is determined according to the massage value threshold interval matched with the massage value, and is recorded as the initial massage pressure; The historical injury conditions of each key point are obtained, and the initial massage pressure of each key point is corrected in combination with the position of each key point to obtain the massage pressure of each key point.

[0009] Further, the initial massage pressure correction method is: The distance between the key point and the nearest bone is obtained to generate a first correction coefficient; the second correction coefficient is generated according to the latest time interval of the historical injury and the injury severity level; the massage pressure is obtained by fusion analysis based on the first correction coefficient, the second correction coefficient and the initial massage pressure.

[0010] Further, the method for judging the rationality of the massage pressure corresponding to each key point is: A detection period is set, and an electromyographic amplitude change function is formulated according to the continuously obtained electromyographic amplitude parameters in the detection period; a muscle hardness change function is formulated according to the continuously obtained muscle hardness parameters; an electromyographic amplitude change standard function and a muscle hardness change standard function are set, the integral between the electromyographic amplitude change function and the electromyographic amplitude change standard function is calculated, and is recorded as an electromyographic amplitude change value; the integral difference between the muscle hardness change function and the muscle hardness change standard function is calculated, and is recorded as a muscle hardness change value; the first judgment value is obtained by adding the normalized electromyographic amplitude change value and the normalized muscle change value; The feedback information of the user is obtained to obtain a feedback value, and the second judgment value is obtained by normalizing the feedback value; The first judgment value and the second judgment value are added to obtain a judgment coefficient; if the judgment coefficient is within the set judgment coefficient threshold interval, it is judged that the massage pressure is reasonable, and the current massage pressure is maintained; otherwise, it is judged that the massage pressure is unreasonable, and needs to be adjusted.

[0011] Further, the feedback value acquisition method is: According to the current massage scene, a feedback value generation model is constructed, the feedback value generation model is a neural network training model, the feedback value generation model is provided with ten levels of feedback values, the obtained feedback information is input into the feedback value generation model, and the current feedback value is output.

[0012] Further, the method for dynamically adjusting the massage pressure is: The two end point thresholds of the set judgment coefficient threshold interval are recorded as judgment coefficient threshold A and judgment coefficient threshold B; When the judgment coefficient is less than the judgment coefficient threshold A, the massage pressure is increased by K1 pressure; when the judgment coefficient is greater than the judgment coefficient threshold B, the massage pressure is decreased by K2 pressure; The method for obtaining K1 and K2 is: obtaining the physiological parameters of the user, analyzing the physiological parameters to obtain an influence value, multiplying the difference between the judgment coefficient threshold A and the judgment coefficient by the influence value to obtain K1, and multiplying the difference between the judgment coefficient and the judgment coefficient threshold B by the influence value to obtain K2.

[0013] Further, the influence value acquisition method is: Collect the physiological parameters of the user during massage, denoted as first physiological parameters; standardize each first physiological parameter, and construct a first physiological parameter vector; collect the physiological parameters of the user in a normal state, denoted as second physiological parameters; standardize each second physiological parameter, and construct a second physiological parameter vector; calculate the Euclidean distance between the first physiological parameter vector and the second physiological parameter vector, normalize the Euclidean distance, obtain an influence coefficient, and obtain an influence value by subtracting the influence coefficient from 1.

[0014] A fascial trigger point massage robot adjustment control system implements the fascial trigger point massage robot adjustment control method, and comprises: A massage path generation module is configured to construct a 3D virtual anatomical layer, obtain initial information of a user, map the initial information to the 3D virtual anatomical layer, and generate a massage path. A massage pressure determination module is configured to determine key points on the massage path, obtain parameter information of the key points, generate massage pressures of the key points based on the parameter information, and massage the corresponding key points according to the massage pressures. A massage pressure judgment module is configured to obtain feedback information and parameter information of the key points during massage, generate a judgment coefficient based on the feedback information and the parameter information, and judge the rationality of the massage pressures of the key points according to the judgment coefficient. A massage pressure adjustment module is configured to dynamically adjust the massage pressures based on the judgment results.

[0015] The fascial trigger point massage robot adjustment control method and system have the following technical effects and advantages: When the massage track is determined, the fascial trigger point massage robot adjustment control method and system can select a suitable meridian channel as the massage path according to the correlation degrees of the meridian channels and all the trigger points, integrate the meridian channels and muscle trigger points to determine the massage path, improve the accuracy of treatment, and facilitate the recovery of symptoms.

[0016] In the massage process, the fascial trigger point massage robot adjustment control method and system generate a judgment coefficient according to the muscle hardness parameter, the muscle electrical amplitude parameter change condition, and the user feedback condition, judge whether the massage pressure is reasonable according to the judgment coefficient, dynamically adjust the massage pressure when the massage pressure is unreasonable, and correct the adjusted massage pressure according to the physiological parameter change condition of the user, so that the massage pressure can be more accurately adjusted, and the massage effect can be ensured. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of a fascial trigger point massage robot adjustment control method according to Embodiment 1 of the present application is shown in the figure. Figure 2 A fascial trigger point massage robot adjustment control system module block diagram for embodiment 1 of the application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the application.

[0019] Embodiment 1 Please refer to Figure 1 As shown in the drawings, the embodiment discloses a fascial trigger point massage robot adjustment control method and system, the method comprising: constructing a 3D virtual dissection layer, obtaining initial information of a user, mapping the initial information to the 3D virtual dissection layer, and generating a massage path; determining key points on the massage path, obtaining parameter information of each key point, and generating massage pressure of each key point based on the parameter information; obtaining feedback information and parameter information of each key point in the massage process, generating a judgment coefficient based on the feedback information and the parameter information, and judging the rationality of the massage pressure corresponding to each key point according to the judgment coefficient; based on the judgment result, dynamically adjusting the massage pressure.

[0020] The above solution provides a specific method for adjusting and controlling the myofascial trigger point massage robot. First, a 3D virtual anatomical layer is constructed, which can be built based on human anatomy and traditional Chinese medicine meridian theory. Initial user information, including body surface temperature and user requests, is obtained and mapped onto the 3D virtual anatomical layer to generate a massage path. For example, by integrating infrared thermal imaging technology to capture locations of abnormal body surface temperatures, these abnormal locations are mapped to corresponding positions on the 3D virtual anatomical layer. The user's requests are descriptions of the abnormal locations, such as right scapular pain. These requests are then converted into multiple key points for massage, and parameter information for these key points is obtained. Based on this parameter information, each key point is generated. The massage pressure is adjusted by applying pressure to specific key points, allowing for customized pressure settings based on the specific symptoms at each point. During the massage, feedback and parameter information from each key point are collected. A judgment coefficient is generated based on this information and used to assess the appropriateness of the massage pressure. If the pressure is deemed inappropriate, it is adjusted. Finally, the massage pressure is dynamically adjusted based on the assessment results: maintaining the current pressure when it is appropriate, increasing it when necessary, and decreasing it when needed, thus ensuring the massage intensity meets the user's actual needs.

[0021] The method for generating massage paths is as follows: initial information includes the user's body surface temperature information and needs information; subjective pain points are determined based on the user's needs information; the user's pain point area is determined based on the user's needs information and recorded as the user's target area; infrared thermal imaging technology is integrated to capture abnormal body surface temperature points in the user's target area and mark them as objective pain points; subjective pain points and objective pain points are collectively referred to as user pain points. The 3D virtual anatomy layer contains multiple meridian pathways, and each meridian's circulation path is modeled as a meridian channel with width, mapping all user pain points sequentially onto the 3D virtual anatomy layer. Calculate the spatial correlation between each user's pain point and all meridian channels, and construct a channel-pain point matrix; where the rows in the channel-pain point matrix represent all user pain points, the columns represent all meridian channels, and the matrix elements are the correlation degrees; The total contribution of each meridian channel is obtained by summing the correlation of each meridian channel. Select the meridian channel with the highest total contribution as the massage path; The correlation degree is calculated as follows: Set the coverage area of ​​each meridian channel, and set the core radius and influence radius for each meridian channel. Calculate the vertical distance from each user's pain point to the center line of each meridian channel. If the vertical distance is less than or equal to the core radius, the correlation degree is 1; If the vertical distance is greater than the core radius and less than or equal to the influence radius, the correlation degree is 1 minus the ratio coefficient, and the ratio coefficient is the ratio of the vertical distance minus the core radius to the influence radius minus the core radius; If the vertical distance is greater than the influence radius, the correlation degree is 0; The above technical solution provides a specific method for determining a massage path. Traditional Chinese medicine meridian theory believes that the human body is an interconnected whole, and stimulating meridians can regulate the circulation of blood and qi throughout the body. Therefore, associating the massage path with the meridians can better facilitate symptom recovery. Therefore, the present application integrates infrared thermal imaging technology to capture the body surface temperature in the user's target area. The user's target area can be determined by the user's appeal information to determine the approximate location of the user's pain point area. Then, the pain point area is recorded as the user's target area. For example, if the user complains of shoulder pain, a pain point area is automatically set according to the location of the shoulder pain based on clinical experience, and is recorded as the user's target area. The temperature is compared with the standard human temperature interval of the user's target area to determine whether the temperature is abnormal. In this way, temperature anomalies are identified and marked as objective pain points. Based on the user's appeal information, such as the user's description of the symptoms and location, the location point described by the user is determined to determine the subjective pain point. The subjective pain point and the objective pain point are collectively referred to as the user's pain point, and the user's pain point is uniformly mapped to the constructed 3D virtual dissection layer. According to traditional Chinese medicine theory, a plurality of meridian circulation routes are drawn in the 3D virtual dissection layer. Each meridian circulation route is modeled as a meridian channel with a width. Each meridian channel is determined based on professional knowledge in the field of traditional Chinese medicine meridians. The correlation degree of each objective pain point and subjective pain point with all meridian channels in space is calculated. The correlation degree indicates the degree of correlation between each user pain point and each meridian. The greater the correlation degree, the higher the degree of correlation between the user pain point and the corresponding meridian. Specifically, the core radius and the influence radius of each meridian channel are set according to the coverage range of each meridian channel. The core radius and the influence radius are determined based on anatomical basics and traditional Chinese medicine meridian theory. The vertical distance from each user pain point to the center line of each meridian channel is obtained. If the vertical distance is less than or equal to the core radius, the user pain point is 100% correlated with the corresponding meridian channel, and the correlation degree is set to 1. If the vertical distance is greater than the core radius and less than or equal to the influence radius, the correlation degree is 1 minus the ratio coefficient, and the ratio coefficient is the ratio of the vertical distance minus the core radius to the influence radius minus the core radius. The specific mathematical expression is: ; In the formula, For the correlation degree, at this time, although the user pain point and the corresponding meridian channel are not 100% related, the user pain point is within the influence range of the meridian channel, so the correlation degree is linearly attenuated according to the vertical distance; when the vertical distance is greater than the influence radius, at this time, it indicates that the user pain point and the corresponding meridian channel are completely irrelevant, and the correlation degree is 0; After calculating the spatial correlation degree of each user pain point and all meridian channels, a channel pain point matrix is constructed, all user pain points are in the row, all meridian channels are in the column, and the correlation degree is the matrix element; the correlation degrees of each meridian channel are summed to obtain the total contribution degree of each meridian channel. The higher the total contribution degree, the more relevant user pain points exist on the meridian channel, and the more important the meridian channel is. Therefore, the meridian channel with the highest total contribution degree is selected as the massage path. In this way, the appropriate meridian channel can be selected as the massage path according to the correlation degree of each meridian channel and all user pain points. This integration of traditional Chinese meridians and muscle pain points to determine the massage path can not only improve the accuracy of treatment, but also help the recovery of symptoms.

[0022] The massage pressure generation method is: setting a plurality of key points on the massage path, obtaining parameter information of the key points, the parameter information including muscle hardness parameter, muscle electrical amplitude parameter and skin deformation parameter, and fusing the muscle hardness parameter, muscle electrical amplitude parameter and skin deformation parameter to analyze and generate a state value; In turn, each key point is taken as the center to set an influence circle, and the number of user pain points in each influence circle is obtained; the massage value of each key point is calculated by exponential operation with the state value as the base and the number of user pain points as the index; A massage pressure reference table is constructed, which includes the massage value threshold interval corresponding to each massage pressure; according to the massage value threshold interval matched by the massage value, the massage pressure of each key point is determined and recorded as the initial massage pressure; The historical injury situation of each key point is obtained, and the initial massage pressure of each key point is corrected in combination with the position of each key point to obtain the massage pressure of each key point; The initial massage pressure correction method is: obtaining the distance between the key point and the nearest bone to generate a first correction coefficient; according to the nearest occurrence time interval and the injury severity level of the historical injury in the historical injury situation, a second correction coefficient is generated; the first correction coefficient, the second correction coefficient and the initial massage pressure are fused and analyzed to obtain the massage pressure.

[0023] The technical scheme provides a specific method for determining the massage pressure, a plurality of key points are set on a massage path, the key points are determined according to clinical experience and professional knowledge, the muscle hardness parameter reflects the tension of the muscle, the muscle electrical amplitude parameter reflects the electrical activity level of the muscle, and the skin deformation parameter reflects the deformation of the skin, and the three parameters can well reflect the current symptom condition; therefore, the massage robot quickly massages the key points to obtain the muscle hardness parameter, the muscle electrical amplitude parameter and the skin deformation parameter of the key points, the muscle hardness parameter can be obtained through a micro pressure sensor installed on the massage robot, the muscle electrical amplitude parameter can be obtained through a patch type electromyography sensor installed on the massage robot, and the skin deformation parameter can be obtained through a visual sensor installed on the massage robot; the muscle hardness parameter, the muscle electrical amplitude parameter and the skin deformation parameter are fused to analyze and generate a state value; specifically, the muscle hardness parameter is denoted as KP, the muscle electrical amplitude parameter is denoted as KV, the skin deformation parameter is denoted as KM, and the state value is denoted as K, and the state value expression is: ; In the formula, KP is a muscle hardness parameter comparison value, KV is a muscle electrical amplitude parameter comparison value, KM is a skin deformation parameter comparison value, and all can be determined according to the related professional knowledge and experience of the person skilled in the art and are used for dimensionless; an influence circle is set with the key point as the center, the influence circle is set according to clinical experience, the number of user pain points in the influence circle is obtained; the massage value is obtained by performing exponential operation with the state value as the base and the number of pain points as the index, and the specific massage value is ; wherein K is the massage value, the number of user pain points is the number of pain points; According to clinical experience and a large number of massage values corresponding to massage pressures, a massage pressure table is constructed, each massage pressure corresponds to a massage value threshold interval, it should be noted that different body types and age groups of users have corresponding massage pressure tables, the massage value of the user is obtained and input into the corresponding massage pressure table, and the massage pressure is determined according to the matched massage value threshold interval, which is recorded as the initial massage pressure; since the positions of the key points and the historical injury conditions of the user will affect the massage pressure, for example, if the key point is very close to the skeleton, in order to avoid damaging the skeleton, the massage pressure needs to be corrected, and if the key point has an injury, the massage pressure also needs to be corrected; therefore, the historical injury conditions of each key point are obtained, and the initial massage pressure of each key point is corrected in combination with the positions of the key points, to obtain the massage pressure; the specific correction method is: the distance between the key point and the nearest skeleton is obtained, and normalized processing is performed to obtain a first correction coefficient s1; the historical injury condition of the key point is obtained, the historical injury condition includes the nearest occurrence time interval and the injury severity level, and the injury severity level can be determined according to the standard identification rules in the related field; the nearest occurrence time interval is multiplied by the reciprocal of the injury severity level to obtain an injury state value, and the injury state value is normalized to obtain a second correction coefficient s2; based on the fusion analysis of the first correction coefficient, the second correction coefficient and the initial massage pressure, the massage pressure is obtained, and the expression is: ; In the formula, is the initial massage pressure, is the massage pressure; the closer the distance between the key point and the nearest skeleton, the greater the influence on the skeleton, and the more the massage pressure needs to be corrected, the closer the nearest occurrence time interval of the injury and the greater the injury severity level (the smaller the reciprocal of the injury severity level), the more serious the injury, and the more the massage pressure needs to be corrected, so the smaller the first correction coefficient and the second correction coefficient, the more the massage pressure needs to be corrected. In this way, the distance between the key point and the skeleton and the injury condition at the key point can be comprehensively analyzed to correct the massage pressure, so that the user's body can be massaged normally without affecting the user's body.

[0024] The method for judging the rationality of the massage pressure corresponding to each key point is: setting a detection period, a muscle electrical amplitude change function is formulated according to the continuously obtained muscle electrical amplitude parameters, and a muscle hardness change function is formulated according to the continuously obtained muscle hardness parameters; a muscle electrical amplitude change standard function and a muscle hardness change standard function are set, the integral between the muscle electrical amplitude change function and the muscle electrical amplitude change standard function is calculated, which is recorded as a muscle electrical amplitude change value; the integral difference between the muscle hardness change function and the muscle hardness change standard function is calculated, which is recorded as a muscle hardness change value; the muscle electrical amplitude change value and the muscle change value are added after normalized processing, to obtain a first judgment value; obtaining feedback information of the user, obtaining a feedback value, and obtaining a second judgment value after normalizing the feedback value; adding the first judgment value and the second judgment value to obtain a judgment coefficient; if the judgment coefficient is located in a set judgment coefficient threshold interval, it is judged that the massage pressure is reasonable, and the current massage pressure is maintained; otherwise, it is judged that the massage pressure is unreasonable, and needs to be adjusted; The feedback value acquisition method is: constructing a feedback value generation model according to the current massage scene, the feedback value generation model is a neural network training model, the feedback value generation model is provided with ten levels of feedback values, and the obtained feedback information is input into the feedback value generation model to output the current feedback value.

[0025] The technical solution provides a specific method for judging the rationality of massage pressure on key points. First, a detection period is set. Within the detection period, an electromyographic amplitude variation function is formulated based on continuously obtained electromyographic amplitude parameters, a muscle hardness variation function is formulated based on continuously obtained muscle hardness parameters, an electromyographic amplitude variation standard function and a muscle hardness variation standard function are set, an integral between the electromyographic amplitude variation function and the electromyographic amplitude variation standard function is calculated, and is recorded as an electromyographic amplitude variation value. An integral difference between the muscle hardness variation function and the muscle hardness variation standard function is calculated, and is recorded as a muscle hardness variation value. The electromyographic amplitude variation standard function and the muscle hardness variation standard function can be formulated in combination with the experience and professional knowledge of people in the field. After normalization processing, the electromyographic amplitude variation value and the muscle hardness variation value are added to obtain a first judgment value. As the massage proceeds, the electromyographic amplitude and the muscle hardness will generally decrease slowly. Therefore, when the integral difference is too large, it indicates that the electromyographic amplitude and the muscle hardness decrease too quickly, and the massage pressure needs to be adjusted. Conversely, when the integral difference is too small, it indicates that the electromyographic amplitude and the muscle hardness decrease too slowly, and the massage pressure also needs to be adjusted. Feedback information of the user is obtained, the feedback information is voice information of the user, and a feedback value is obtained. Specifically, a feedback value generation model can be constructed according to the current massage scene, the feedback value generation model is a neural network training model, the feedback value generation model is provided with ten feedback values of 1-10 levels, the obtained feedback information is input into the feedback value generation model, and a current feedback value is output. The larger the feedback value, the greater the massage pressure. For example, keywords such as high pressure and pain can be recognized from the voice information of the user, and are input into the feedback value generation model. A large number of corresponding keywords are trained in each feedback value in the feedback value generation model. In this way, the current feedback value can be output according to the feedback information of the user, and a second judgment value is obtained after normalization processing of the feedback value. Finally, the first judgment value and the second judgment value are added to obtain a judgment coefficient, and the judgment coefficient is compared with a preset judgment coefficient threshold interval. The judgment coefficient threshold interval can be determined according to experience data and professional knowledge. When the judgment coefficient is within the set judgment coefficient threshold interval, it is judged that the massage pressure is reasonable, and the current massage pressure is maintained. Otherwise, it is judged that the massage pressure is unreasonable, and needs to be adjusted.

[0026] The method for dynamically adjusting the massage pressure is as follows: two end point thresholds of the set judgment coefficient threshold interval are recorded as a judgment coefficient threshold A and a judgment coefficient threshold B. When the judgment coefficient is less than the judgment coefficient threshold A, the massage pressure is increased by K1 pressure. When the judgment coefficient is greater than the judgment coefficient threshold B, the massage pressure is decreased by K2 pressure. The method for obtaining K1 and K2 is as follows: physiological parameters of the user are obtained, the physiological parameters are analyzed, and an influence value is obtained. The difference between the judgment coefficient threshold A and the judgment coefficient is multiplied by the influence value to obtain K1. The difference between the judgment coefficient and the judgment coefficient threshold B is multiplied by the influence value to obtain K2. The influence value acquisition method is: collecting physiological parameters of the user during massage, denoted as first physiological parameters; performing standardization processing on each first physiological parameter to construct a first physiological parameter vector; collecting physiological parameters of the user in a normal state, denoted as second physiological parameters; performing standardization processing on each second physiological parameter to construct a second physiological parameter vector; calculating the Euclidean distance between the first physiological parameter vector and the second physiological parameter vector, normalizing the Euclidean distance to obtain an influence coefficient, and subtracting the influence coefficient from 1 to obtain the influence value.

[0027] The technical solution provides a specific method for dynamically adjusting the massage pressure. First, the two end point thresholds of the set judgment coefficient threshold interval are denoted as judgment coefficient threshold A and judgment coefficient threshold B. When the judgment coefficient is less than the judgment coefficient threshold A, it indicates that the massage pressure is insufficient, and the massage pressure needs to be increased by K1 pressure to better relieve symptoms. The difference between the judgment coefficient threshold A and the judgment coefficient is multiplied by the influence value to obtain K1 after pressure value conversion. It is known that the greater the difference, the more pressure needs to be adjusted. When the judgment coefficient is greater than the judgment coefficient threshold B, it indicates that the pressure is too large, and the pressure needs to be reduced. The massage pressure is reduced by K2 pressure. The difference between the judgment coefficient and the judgment coefficient threshold B is multiplied by the influence value to obtain K2 after pressure value conversion. The pressure value conversion method can be determined in advance according to the control experiment data of the person skilled in the art. It is known that the greater the difference, the more pressure needs to be adjusted. The physiological parameters of the user also affect the massage state of the user. Therefore, in order to more accurately adjust the massage pressure, the physiological parameters of the user during massage are collected, including heartbeat, blood pressure, blood oxygen saturation, etc., denoted as first physiological parameters. Each first physiological parameter is standardized to construct a first physiological parameter vector. The physiological parameters of the user in a normal state are collected, denoted as second physiological parameters. Each second physiological parameter is standardized to construct a second physiological parameter vector. The Euclidean distance between the first physiological parameter vector and the second physiological parameter vector is calculated. After normalization processing of the Euclidean distance, an influence coefficient is obtained. The influence value is obtained by subtracting the influence coefficient from 1. It is known that the smaller the Euclidean distance, the smaller the influence of the physiological condition of the user on the adjustment of the massage pressure. Through this method, the massage pressure can be dynamically adjusted according to the difference between the judgment coefficient and the judgment coefficient threshold, and the massage pressure can be corrected according to the physiological parameter change of the user. This can more accurately dynamically adjust the massage pressure according to the parameter change during massage to ensure the massage effect.

[0028] Embodiment 2 Please refer to Figure 2 As shown in the figure, the embodiment discloses a fascial trigger point massage robot adjustment control system, comprising: The massage path generation module is configured to construct a 3D virtual anatomical layer, acquire initial information of a user, map the initial information to the 3D virtual anatomical layer, and generate a massage path. The massage pressure determination module is configured to determine key point positions on the massage path, acquire parameter information of the key point positions, generate massage pressures of the key point positions based on the parameter information, and massage the corresponding key point positions according to the massage pressure sizes. The massage pressure determination module is configured to determine key point positions on the massage path, acquire parameter information of the key point positions, generate massage pressures of the key point positions based on the parameter information, and massage the corresponding key point positions according to the massage pressure sizes. The massage pressure adjustment module is configured to dynamically adjust the massage pressures based on the judgment results.

[0029] The above only describes the preferred embodiments of the present application and is not used to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can modify the technical solutions recorded in the foregoing embodiments or replace some technical features with equivalent ones without departing from the spirit and principles of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0030] The formulas in the present specification are dimensionless numerical calculations, the formulas are obtained by software simulation of a large amount of data to obtain a formula closest to the real situation, and the preset parameters and threshold values in the formulas are set by those skilled in the art according to actual conditions.

[0031] Although the embodiments of the present application have been shown and described, those skilled in the art can understand that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and purposes of the present application, and the scope of the present application is defined by the claims and their equivalents.

Claims

1. A method for adjusting and controlling a myofascial trigger point massage robot, characterized in that, include: Construct a 3D virtual anatomy layer, obtain the user's initial information, map the initial information onto the 3D virtual anatomy layer, and generate a massage path; Identify key points along the massage path, obtain parameter information for each key point, and generate massage pressure for each key point based on the parameter information. The system obtains feedback and parameter information from key points during the massage process, generates judgment coefficients based on the feedback and parameter information, and judges the rationality of the massage pressure corresponding to each key point according to the judgment coefficients. Based on the assessment results, the massage pressure is dynamically adjusted.

2. The adjustment and control method for a myofascial trigger point massage robot according to claim 1, characterized in that, The method for generating massage paths is as follows: The initial information includes the user's body surface temperature information and needs information. Subjective pain points are determined based on the user's needs information. The user's pain point area is determined based on the user's needs information and is recorded as the user's target area. Infrared thermal imaging technology is integrated to capture abnormal body surface temperature points in the user's target area and mark them as objective pain points. Subjective pain points and objective pain points are collectively referred to as user pain points. The 3D virtual anatomy layer contains multiple meridian pathways, and each meridian's circulation path is modeled as a meridian channel with width, mapping all user pain points sequentially onto the 3D virtual anatomy layer. Calculate the spatial correlation between each user's pain point and all meridian channels, and construct a channel-pain point matrix; where the rows in the channel-pain point matrix represent all user pain points, the columns represent all meridian channels, and the matrix elements are the correlation degrees; The total contribution of each meridian channel is obtained by summing the correlation of each meridian channel. Choose the meridian channel with the highest total contribution as the massage path.

3. The adjustment and control method for a myofascial trigger point massage robot according to claim 2, characterized in that, The correlation degree is calculated as follows: Set the coverage area of ​​each meridian channel, and set the core radius and influence radius for each meridian channel. Calculate the vertical distance from each user's pain point to the center line of each meridian channel. If the vertical distance is less than or equal to the core radius, the correlation degree is 1; If the vertical distance is greater than the core radius and less than or equal to the influence radius, the correlation degree is 1 minus the ratio coefficient, which is the ratio of the vertical distance minus the core radius to the influence radius minus the core radius. If the vertical distance is greater than the radius of influence, the correlation is 0.

4. The adjustment and control method for a myofascial trigger point massage robot according to claim 1, characterized in that, The method for generating massage pressure is as follows: Multiple key points are set on the massage path, and parameter information of the key points is obtained. The parameter information includes muscle stiffness parameters, electromyography amplitude parameters, and skin deformation parameters. The state value is generated by integrating the muscle stiffness parameters, electromyography amplitude parameters, and skin deformation parameters. Each key point is used as the center to define an influence circle, and the number of user pain points within each influence circle is obtained. Using the status value as the base and the number of user pain points as the exponent, the massage value of each key point is calculated through exponential calculation; Construct a massage pressure reference table, which includes the massage value threshold range corresponding to each massage pressure; determine the massage pressure of each key point based on the massage value threshold range matched with the massage value, and record it as the initial massage pressure; The historical injury information of each key point is obtained, and the initial massage pressure of each key point is corrected based on the location of each key point to obtain the massage pressure of each key point.

5. The adjustment and control method for a myofascial trigger point massage robot according to claim 4, characterized in that, The method for correcting the initial massage pressure is as follows: Obtain the distance between key points and the nearest bone to generate a first correction coefficient; generate a second correction coefficient based on the most recent occurrence time interval and severity level of historical injuries and illnesses; perform a fusion analysis based on the first correction coefficient, the second correction coefficient, and the initial massage pressure to obtain the massage pressure.

6. The adjustment and control method and system for a myofascial trigger point massage robot according to claim 1, characterized in that, The method for judging the rationality of the massage pressure corresponding to each key point is as follows: Set a detection period, and within the detection period, formulate a function for the change of electromyographic amplitude based on the continuously acquired electromyographic amplitude parameters, and formulate a function for the change of muscle stiffness based on the continuously acquired muscle stiffness parameters. Define a standard function for the change in electromyographic amplitude and a standard function for the change in muscle stiffness. Calculate the integral between the electromyographic amplitude change function and the standard function for the change in electromyographic amplitude, and record it as the change in electromyographic amplitude. Calculate the integral difference between the muscle stiffness change function and the standard function of muscle stiffness change, and denote it as the muscle stiffness change value. The electromyography amplitude change value and the muscle change value are normalized and then summed to obtain the first judgment value; Obtain user feedback information, get feedback values, and normalize the feedback values ​​to obtain the second judgment value; The first judgment value and the second judgment value are added together to obtain the judgment coefficient. If the judgment coefficient is within the set judgment coefficient threshold range, the massage pressure is judged to be reasonable and the current massage pressure is maintained; otherwise, the massage pressure is judged to be unreasonable and needs to be adjusted.

7. The adjustment and control method for a myofascial trigger point massage robot according to claim 6, characterized in that, The method for obtaining feedback values ​​is as follows: Based on the current massage scenario, a feedback value generation model is constructed. The feedback value generation model is a neural network training model with ten levels of feedback values. The obtained feedback information is input into the feedback value generation model, and the current feedback value is output.

8. The adjustment and control method for a myofascial trigger point massage robot according to claim 7, characterized in that, The method for dynamically adjusting massage pressure is as follows: The two endpoints of the set judgment coefficient threshold interval are denoted as judgment coefficient threshold A and judgment coefficient threshold B; When the judgment coefficient is less than the judgment coefficient threshold A, the massage pressure is increased by K1 units; when the judgment coefficient is greater than the judgment coefficient threshold B, the massage pressure is decreased by K2 units. The methods for obtaining K1 and K2 are as follows: obtain the user's physiological parameters, analyze the physiological parameters, and obtain the impact values; The difference between the judgment coefficient threshold A and the judgment coefficient is multiplied by the influence value to obtain K1; The difference between the judgment coefficient and the judgment coefficient threshold B is multiplied by the influence value to obtain K2.

9. The adjustment and control method for a myofascial trigger point massage robot according to claim 8, characterized in that, The method for obtaining the influence value is as follows: Collect the user's physiological parameters during massage and record them as the first physiological parameters; standardize each first physiological parameter and construct the first physiological parameter vector. Collect the user's physiological parameters under normal conditions, and record them as the second physiological parameter; Each second physiological parameter is standardized to construct a vector of second physiological parameters; Calculate the Euclidean distance between the first physiological parameter vector and the second physiological parameter vector. After normalizing the Euclidean distance, obtain the influence coefficient. Subtract the influence coefficient from 1 to obtain the influence value.

10. A myofascial trigger point massage robot adjustment and control system, implementing the myofascial trigger point massage robot adjustment and control method according to any one of claims 1-9, characterized in that, include: The massage path generation module is used to construct a 3D virtual anatomy layer, obtain the user's initial information, map the initial information onto the 3D virtual anatomy layer, and generate a massage path. The massage pressure determination module is used to determine key points on the massage path, obtain parameter information of key points, generate massage pressure for each key point based on the parameter information, and massage the corresponding key points according to the massage pressure. The massage pressure judgment module is used to obtain feedback information and parameter information of each key point during the massage process, generate judgment coefficients based on the feedback information and parameter information, and judge the rationality of the massage pressure corresponding to each key point according to the judgment coefficients. The massage pressure adjustment module is used to dynamically adjust the massage pressure based on the judgment results.

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