Portable human knee acupoint detector and augmented reality application method thereof

Abstract

The invention discloses a portable human knee acupoint detector and an augmented reality application method thereof, the detector comprises multiple groups of electrode arrays based on a two-electrode method, a signal processing module, a main control module, a wireless communication module and a power supply module, and the detector is integrated on a portable elastic bandage and is used for collecting knee electrical impedance data and identifying low-resistance points (acupoints). According to the application method, data are sent to the intelligent device APP through wireless communication; the APP utilizes an augmented reality (AR) technology, combines a camera image and a preset mark code, displays the knee anatomical model and the identified acupoint position and impedance information on a device screen in real time in an overlapping manner, and supports gesture zooming and UI interaction. According to the invention, the traditional Chinese medicine acupoint electrical characteristics and the modern AR technology are combined, a portable, visual and accurate knee acupoint positioning and visualization scheme is provided, the accuracy of knee disease treatment (such as acupuncture and massage), the patient experience and the operation convenience are remarkably improved, and the system is suitable for clinical, household and rehabilitation scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical devices and augmented reality (AR) technology, in particular to a portable human knee acupoint detector and its augmented reality application method, specifically to an electrical impedance detection device dedicated to human knee acupoints and its visualization application method based on augmented reality technology, which is particularly suitable for the auxiliary diagnosis of knee diseases, precise acupoint positioning for traditional Chinese medicine acupuncture / massage therapy and patient rehabilitation guidance. BACKGROUND

[0002] With the intensification of social aging and the increase of sports injuries, the incidence of knee diseases continues to rise, seriously affecting the quality of life of patients. Traditional Chinese medicine acupoint therapy (such as acupuncture, moxibustion, and massage) is an effective means of treating knee diseases, and its efficacy is highly dependent on accurate positioning of acupoints. Traditional acupoint positioning mainly relies on the experience and anatomical landmarks of physicians, which has strong subjectivity and poor consistency. Especially for knee acupoints (such as external knee eye, internal knee eye, foot Sanyinjiao, and Yanglingquan), precise positioning is more challenging for non-professional physicians or patient self-care.

[0003] In the prior art, acupoint detection devices based on skin electrical characteristics have been applied, but generally have the disadvantages of large size, complex operation, lack of intuitive visual feedback, etc. Most devices only provide numerical values or simple indicator light prompts, and cannot visually display the accurate position of acupoints on the patient's body surface and their relationship with the anatomical structure, resulting in low understanding and participation of users (especially patients).

[0004] Augmented reality (AR) technology can superimpose virtual information onto real-world views and has shown great potential in the medical field. However, applying AR technology to portable acupoint detection, especially for the knee area, to achieve real-time acquisition of electrical impedance data, acupoint recognition, and accurate fusion of AR scenes, has not yet seen a mature and portable system solution.

[0005] Although the spatial distribution of human acupoints and the meridian system has been systematically recorded in traditional Chinese medicine theory, existing acupoint knowledge is mainly based on body surface proportions and empirical judgments, which has the following limitations: Significant individual differences: Differences in body size, muscle thickness, fat layer, and age can lead to significant positional deviations of acupoints among different individuals. Traditional proportional measurement methods cannot reflect such individual differences, for example, when there is a lesion or trauma in the knee, it may be difficult to locate the acupoint.

[0006] Strong subjectivity, lack of quantitative standards: Traditional positioning relies on the experience and tactile sensation (such as skin temperature and indentation) of physicians, and the positioning accuracy is greatly influenced by experience, which lacks repeatability and digital standards.

[0007] Static theory and dynamic body posture do not match: there is a relative displacement of acupoints in space during knee flexion and extension, while traditional literature is mostly static body position description, lacking dynamic mapping.

[0008] It is difficult to meet the needs of modern intelligent medical applications: with the development of intelligent acupuncture and wearable rehabilitation devices, there is an urgent need for a digital, individualized and real-time acupoint positioning and display method to support machine acupuncture, precise massage and patient self-rehabilitation guidance.

[0009] Therefore, a portable, easy-to-use device and method that can accurately locate knee acupoints and provide real-time visual feedback are needed to improve the accuracy, convenience and patient experience of traditional Chinese medicine knee treatment. SUMMARY

[0010] The present application provides a portable human knee acupoint detector and its augmented reality application method, which includes an individualized acupoint positioning method combining electrical impedance detection, spatial registration and augmented reality technology. Based on traditional Chinese medicine meridian theory, this method realizes the objective, visualization and dynamic redefinition of acupoints on the individual's body surface through electrophysiological parameter detection and three-dimensional space mapping, thereby meeting the needs of modern digital and intelligent development of traditional Chinese medicine physiotherapy. It overcomes the shortcomings of existing technology, such as the reliance on experience for knee acupoint positioning, the bulkiness of traditional detection equipment and the lack of intuitive visual feedback. The present application provides a portable human knee acupoint detector and its augmented reality application method, which can achieve accurate, portable and intuitive detection and visualization of knee acupoints.

[0011] The present application adopts the following technical solutions.

[0012] A portable human knee acupoint detector, comprising: An electrode measurement module for collecting and measuring electrical impedance data of the human knee region, including multiple sets of measurement electrodes (101), which use a two-electrode measurement principle, including one shared excitation electrode (101a) and multiple independently distributed measurement electrodes (101b). The electrodes (101) are arranged on an elastic band (102) and are specially designed to fit the knee surface. A signal processing module (103) electrically connected to the electrical impedance measurement module for conditioning, filtering, amplifying and analog-to-digital conversion of the collected electrical impedance signals. A main control module (104) electrically connected to the signal processing module (103) for controlling the electrical impedance measurement process, processing measurement data and identifying low electrical impedance regions. A wireless communication module (105) electrically connected to the main control module (104) for wirelessly transmitting processed electrical impedance data and identification results to external smart devices. The detection instrument further comprises a power module (106) and an augmented reality (AR) visualization module.

[0013] The augmented reality visualization module comprises an external smart device for running a related application; the augmented reality visualization module is used to receive data from the wireless communication module (105) and display a knee-specific anatomical structure model, an identified acupoint position and an electrical impedance information in an overlaid manner at a screen of the smart device in combination with a real-time image of the knee captured by a camera of the external smart device.

[0014] The augmented reality (AR) visualization module is developed based on a Unity engine and a Vuforia AR SDK, and when used, a spatial correspondence between a virtual model and a real knee is first established by recognizing a marker code (107) arranged on the elastic bandage (102) or a skin near the knee, an augmented reality coordinate system is calibrated in combination with preset key anatomical point data to realize initial positioning, and an accurate overlaid display of an acupoint position of the knee on a real image is realized in combination with the electrical impedance data.

[0015] The augmented reality visualization module is an AR visualization module providing a user interaction function, and the user interaction function comprises: (1) adjusting a display size of the knee anatomical structure model and the acupoint by a gesture (such as a two-finger zooming); (2) triggering display / hide of specific information, switching a view or resetting display by a UI button.

[0016] The electrode measurement module comprises six groups of measurement electrodes (101), and the six groups of measurement electrodes share one excitation electrode (101a), and the seven electrodes are vertically arranged on the elastic bandage (102) according to a distribution feature of acupoints of the knee.

[0017] The signal processing module (103) comprises an impedance detection chip and an analog-to-digital converter (ADC), the impedance detection chip is used to generate an excitation signal and measure a voltage response, and the analog-to-digital converter (ADC) is used to convert an analog voltage signal into a digital signal; the power module is used to supply power to the electrode measurement module, the signal processing module, the main control module and the wireless communication module; The wireless communication module (105) is a Bluetooth low energy (BLE) module.

[0018] An augmented reality application method of a portable human knee acupoint detection instrument, using the above-mentioned portable human knee acupoint detection instrument, comprising the following steps: Step S1: skin preparation and device wearing: ensuring that the skin of the knee to be measured is clean and dry, wearing the elastic bandage (102) of the detection instrument on the human knee, ensuring that the electrodes (101) are in good contact with the skin, and turning on the power of the detection instrument; To ensure the accuracy and reliability of electrical impedance measurement, the skin of the measured area should be kept clean and dry. The specific requirements and principles are as follows: Importance of dry skin: Conductive substances such as sweat and oil on the surface of the skin can significantly affect the results of electrical impedance measurement, leading to deviations in acupoint recognition. Treatment method: Before use, gently clean the skin on the knee with alcohol cotton, and wear the device after it is completely dry. The detection instrument has device adaptability, i.e., the system is optimized for relatively dry skin conditions to ensure stable and reliable measurement results under normal physiological conditions.

[0019] Step S2: Augmented reality scene initialization: Start the application on the external smart device, activate the camera, and identify the pre-set marker code (107) to establish the spatial correspondence between the virtual model and the real knee, and calibrate the coordinate system in combination with key anatomical points (such as the lower edge of the patella and the small head of the fibula). The Vuforia engine on the smart device calculates the conversion relationship between the virtual model coordinate system and the real world coordinate system based on the recognition results, achieving initial positioning and calibration. Step S3: Electrical impedance data acquisition and processing: Collect electrical impedance data of the specific area of the knee after visual positioning through the electrical impedance measurement module, process through the signal processing module (103) and the main control module (104), and identify low electrical impedance points. Specifically, the detection instrument collects electrical impedance data of different areas of the knee through multiple electrodes (101) at a set time interval (such as every 2 seconds). The signal processing module (103) performs signal conditioning and ADC conversion. The main control module (104) processes the data, calculates the impedance values of each point, and applies algorithms (such as filtering the minimum impedance value point) to identify potential knee acupoints (low electrical impedance points). Step S4: Wireless data transmission: The main control module (104) sends electrical impedance data and low electrical impedance point position information to the external smart device through the wireless communication module (105); that is, it sends measurement data (impedance values of each point) and / or recognition results (such as acupoint coordinate index) to the paired smart device APP. Step S5: Acupoint visualization superposition: The application APP receives the data transmitted in step S4, and based on the established visual positioning, in combination with the established coordinate correspondence, displays the three-dimensional anatomical model of the knee and the position of the identified acupoint on the real-time image of the knee captured by the camera, and displays the corresponding electrical impedance information in association. Step S6: User interaction and operation: The user performs gesture operations (such as zooming the model) or point-clicks UI buttons for interaction through the smart device screen to adjust the display content. Step S7: Real-time updating and feedback: Continuously receive new electrical impedance data, update the display information, and provide real-time visual feedback of acupoint positioning and treatment effect.

[0020] Step S8: Data recording and evaluation: The application records the measurement data, recognition results and user operation information for subsequent analysis or report generation.

[0021] In step S3, based on the method of visual positioning first and electrical precise positioning, multi-level recognition mechanism is used to ensure the accurate positioning of the knee acupoint dense area, including the areas of outer knee eye, inner knee eye, Sanyinjiao and Yanglingquan, and the electrical characteristics of different acupoints in the knee acupoint dense area are similar; The special solution for the knee acupoint dense area is: The knee is a relatively dense area of acupoint distribution (such as outer knee eye, inner knee eye, Sanyinjiao and Yanglingquan, etc.), and the electrical characteristics of different acupoints may be similar. The visual positioning first and electrical precise positioning technology route effectively solves this problem: The method of visual positioning first is as follows: first, through AR marker code recognition and key anatomical point calibration, an accurate coordinate system is established to determine the expected approximate area of each acupoint; The method of electrical precise positioning is as follows: in the approximate area determined by visual positioning, the lowest impedance point is found through electrical impedance measurement to realize the precise positioning of the acupoint; The space matching algorithm is as follows: the candidate low impedance point is compared with the standard acupoint template through the space matching algorithm, combined with the anatomical position information, to effectively distinguish different acupoints in the dense area.

[0022] The electrical characteristics of the acupoint are determined by the skin electrical impedance value at the acupoint being 20%~60% lower than that at the adjacent non-acupoint area, with significant difference. The specific steps are as follows: Step A1, visual recognition and space registration: first, the virtual model is established by the pre-set AR marker code on the strap to correspond to the space of the real knee, and then the key anatomical points (such as the lower edge of the patella and the small head of the fibula) are used to calibrate the coordinate system to determine the approximate distribution area of the main acupoints of the knee, thereby establishing an individualized acupoint expected position template to provide spatial constraints for electrical detection; Step A2, electrical impedance acquisition stage: using two-electrode method, a safe micro-current is applied between different areas of the knee, and the voltage response and impedance value of each point are collected; Step A3, signal processing and feature extraction: the collected weak alternating voltage signal first passes through the voltage buffer circuit for isolation and impedance matching to prevent signal attenuation; then, the signal enters the differential amplification circuit, which provides a high common-mode rejection ratio, amplifies the voltage difference, and suppresses common-mode noise such as power frequency interference; the amplification factor is adjusted by external resistance to ensure that the signal amplitude is suitable for subsequent processing; the amplified signal is processed by a filter circuit, combining hardware filtering and software digital filtering to further remove random noise, and using an analog switch (MAX306) to realize multi-channel time division multiplexing and reduce electrode crosstalk; the main control module digitizes the amplified signal and converts the voltage values of each measurement point into a relative impedance ratio through software algorithms to eliminate individual differences and environmental fluctuations; the normalization is based on the amplitude of the reference signal, and a logarithmic detection circuit is used to convert the AC signal amplitude ratio into a DC voltage output, which is then quantized by an A / D conversion module; Step A4, the main control module reads the A / D converted digital signal (through the SPI interface) and calculates the electrical impedance value of each measurement point based on the two-electrode method. The calculation formula is: wherein, is the measurement electrode voltage, is the excitation current, is the standard resistance value; The main control module maps the impedance values of multiple electrodes (such as 6 measurement points) to the knee space coordinates, constructs a two-dimensional grid graph through interpolation algorithms (such as bilinear interpolation), and visualizes the impedance distribution of the knee region by taking the electrode position as the node and the impedance value as the height, highlighting the low resistance area. The distribution map is updated every 2 seconds to ensure real-time performance. The main control module performs gradient analysis on the two-dimensional impedance distribution map and calculates the neighborhood impedance gradient of each point through a sliding window.

[0023] If the impedance value of a certain point is lower than that of its eight neighboring points and the absolute value of the difference exceeds the threshold, i.e., 20%-60% lower than the adjacent non-acupoint area, it is marked as a candidate minimum point. The system continuously collects multiple rounds of data and compares historical measurement results. When the minimum point remains stable in three consecutive samplings, i.e., the position deviation is less than 5 mm and the impedance change is less than 10%, it is determined as an effective candidate acupoint; If the detection area changes or the human posture changes, the system automatically compensates for the position through AR tracking to ensure that the displayed acupoints dynamically correspond to the actual physiological structure.

[0024] In step S5, the identified acupoint position is dynamically updated according to real-time electrical impedance data; and the following virtual content is accurately superimposed and rendered: a three-dimensional anatomical structure model of the knee (such as bones and main ligament illustrations); At the position of the identified low impedance point, a highlight icon or marker point (such as the identification of acupoints such as outer knee eye, inner knee eye, foot Sanyinjiao, Yanglingquan, etc.) is rendered; The real-time resistance and impedance values or states of the corresponding position (such as "acupoint") are displayed beside the marker point or on the information panel.

[0025] The present application uses a comprehensive judgment mechanism, and the final acupoint judgment is based on the triple mechanism of "minimum resistance and impedance point + spatial position calibration + dynamic posture compensation", which ensures high positioning accuracy, good repeatability and stable visualization effect. Experimental verification shows that the average positioning error is less than 15mm, and the accuracy is more than 94%.

[0026] The present application can effectively deal with the problem of acupoint positioning difficulty caused by the appearance change due to lesions or trauma in the knee.

[0027] The present application has the following advantages: 1. Precise positioning: based on resistance and impedance measurement (consistent with the low resistance characteristics of traditional Chinese medicine acupoints), the knee acupoints are objectively identified, the human error is reduced, and the positioning accuracy is significantly improved (experimental results show that the average positioning error is less than 15mm, and the average accuracy is 94.9%).

[0028] 2. Portable and easy to use: the detector is integrated into the elastic bandage, which is small in size and convenient to wear; the operation process is simple, and the user (including the patient) can easily operate.

[0029] 3. Intuitive visualization: through AR technology, abstract resistance and impedance data and identification results are directly superimposed on the real knee image, users can directly "see" the acupoint position and its relationship with the anatomical structure, greatly improving the understanding and treatment confidence.

[0030] 4. Enhanced interaction: users can interact with virtual information through gestures and UI, with strong participation. Real-time feedback makes the treatment process (such as acupuncture needle insertion point, massage position) more transparent.

[0031] 5. Improve efficiency and experience: simplify the acupoint searching process, save time; friendly visualization interface improves patient treatment or self-care experience.

[0032] 6. Effectively solve the problem of acupoint identification in dense area: through the technical route of visual guide and accurate electrical positioning, combined with spatial matching algorithm, the accurate positioning of knee dense acupoints is effectively distinguished, and the technical difficulty of similar electrical characteristics of different acupoints is solved; 7. Wide application: suitable for traditional Chinese medicine knee treatment (acupuncture, massage auxiliary positioning) in hospitals and clinics, community and family health management (patient self-monitoring, rehabilitation training guidance), and traditional Chinese medicine teaching demonstration.

[0033] The technical solution proposed in this invention achieves a modern expansion based on traditional acupoint theory. It achieves objectification of acupoint identification through electrical signal analysis, visualization of acupoints through AR spatial calibration and 3D anatomical modeling, and personalized, precise positioning through real-time feedback and dynamic update mechanisms. This invention can be applied not only to clinical positioning assistance in traditional Chinese medicine acupuncture and massage, but also to rehabilitation training, meridian therapy equipment, and visualization demonstrations in traditional Chinese medicine teaching, providing a new technical path for the digital development of traditional Chinese medicine. Attached Figure Description

[0034] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Appendix Figure 1 This is a schematic diagram of the detector electrode array structure in an embodiment of the present invention.

[0035] Appendix Figure 2 This is a schematic diagram of the use scenario of the detector in an embodiment of the present invention (the left area of ​​the figure is a schematic diagram of the elastic strap (102), and its electrode array arrangement in the top view shows 7 electrode points (1 excitation electrode 101a and 6 measurement electrodes 101b). The right area of ​​the image shows the operation interface of the augmented reality application (APP), including the APP main interface: displaying the live camera image, with a virtual model and acupoint markings superimposed on the knee, and UI buttons around it (such as "Hide Panel", "Zoom Reset", etc.), as well as a gesture zoom interaction area: the screen displays the action of touching and separating two fingers, and the virtual model is zoomed in accordingly. Appendix Figure 3 This is a schematic diagram illustrating the principle of the present invention; In the figure: excitation electrode 101a, measuring electrode 101b, elastic strap 102, electrode 101. Detailed Implementation

[0036] As shown in the figure, a portable human knee acupoint detector includes: Electrode measurement module: used to collect and measure the electrical impedance data of the human knee area, including multiple sets of measuring electrodes 101. The electrodes 101 adopt the two-electrode method measurement principle, including a common excitation electrode 101a and multiple independently distributed measuring electrodes 101b. The electrodes 101 are set on the elastic band 102 and are specifically designed to conform to the curved surface of the knee. Signal processing module 103: Electrically connected to the impedance measurement module, used to condition, filter, amplify and convert the acquired impedance signal into digital signal; Main control module 104: Electrically connected to signal processing module 103, used to control the impedance measurement process, process measurement data and identify low impedance regions; Wireless communication module 105: electrically connected with the main control module 104, for wireless transmission of processed electrical impedance data and identification results to external smart devices; The detection instrument also includes a power module 106 and an augmented reality AR visualization module.

[0037] The augmented reality visualization module includes an external smart device for running related applications; the augmented reality visualization module is used to receive data from the wireless communication module 105, and in combination with the real-time image of the knee captured by the camera of the external smart device, to display the knee-specific anatomical structure model, the identified acupoint position and the electrical impedance information in an overlay manner on the screen of the smart device.

[0038] The augmented reality AR visualization module is developed based on the Unity engine and Vuforia AR SDK. When used, first, the spatial correspondence between the virtual model and the real knee is established by recognizing the marker code 107 provided on the elastic bandage 102 or the skin near the knee, the coordinate system of the augmented reality is calibrated in combination with the preset key anatomical point data to realize initial positioning, and the accurate overlay display of the acupoint position of the knee on the real image is realized in combination with the electrical impedance data.

[0039] The augmented reality visualization module is an AR visualization module that provides user interaction functions, and the user interaction functions thereof include: 1. Adjust the display size of the knee anatomical structure model and acupoint by gestures such as two-finger zooming; 2. Trigger display / hide specific information, switch views, or reset display through UI buttons.

[0040] The electrode measurement module includes six groups of measurement electrodes 101, which share one excitation electrode 101a. The seven electrodes are vertically arranged on the elastic bandage 102 according to the distribution characteristics of the acupoints of the knee.

[0041] The signal processing module 103 includes an impedance detection chip and an analog-to-digital converter. The impedance detection chip is used to generate an excitation signal and measure the voltage response. The analog-to-digital converter (ADC) is used to convert the analog voltage signal to a digital signal. The power module is used to power the electrode measurement module, the signal processing module, the main control module, and the wireless communication module; The wireless communication module 105 is a Bluetooth Low Energy (BLE) module.

[0042] The augmented reality application method of the portable human knee acupoint detection instrument uses the above-mentioned portable human knee acupoint detection instrument, which includes the following steps: Step S1: Skin preparation and device wearing: Ensure the skin of the knee to be tested is clean and dry, wear the elastic bandage 102 of the detection instrument on the human knee, ensure that the electrodes 101 are in good contact with the skin, and turn on the power of the detection instrument; To ensure the accuracy and reliability of the electrical impedance measurement, the skin of the measured area should be kept clean and dry. The specific requirements and principles are as follows: Importance of dry skin: Conductive substances such as sweat and oil on the surface of the skin can significantly affect the electrical impedance measurement results, leading to deviation in acupoint identification; Treatment method: Before use, gently clean the skin of the knee with alcohol cotton, and wear the device after it is completely dry; The detection instrument has device adaptability, i.e., the system is optimized for relatively dry skin conditions to ensure stable and reliable measurement results under normal physiological conditions.

[0043] Step S2: Augmented reality scene initialization: Start the application program on the external smart device, activate the camera, identify the preset marker code 107 to establish the spatial correspondence between the virtual model and the real knee, and calibrate the coordinate system in combination with key anatomical points (such as the lower edge of the patella and the small head of the fibula); Based on the identification result, the Vuforia engine carried by the smart device calculates the conversion relationship between the virtual model coordinate system and the real world coordinate system to realize initial positioning and calibration; Step S3: Electrical impedance data acquisition and processing: Collect the electrical impedance data of the specific area of the knee after visual positioning through the electrical impedance measurement module, process through the signal processing module 103 and the main control module 104, and identify the low electrical impedance point; Specifically: The detection instrument collects the electrical impedance data of different areas of the knee through multiple electrodes 101 at a set time interval such as every 2 seconds. The signal processing module 103 performs signal conditioning and ADC conversion. The main control module 104 processes the data, calculates the impedance values of each point, and applies algorithms such as filtering the minimum impedance value point to identify potential low electrical impedance points of the knee acupoints; Step S4: Wireless data transmission: The main control module 104 sends the electrical impedance data and low electrical impedance point position information to the external smart device through the wireless communication module 105; That is, the measurement data point impedance values and / or identification results such as acupoint coordinate indexes are sent to the paired smart device APP; Step S5: Acupoint visualization superposition: The application program APP receives the data transmitted in step S4, and based on the established visual positioning, in combination with the established coordinate correspondence, displays the three-dimensional anatomical model of the knee and the position of the identified acupoint on the real-time image of the knee captured by the camera, and displays the corresponding electrical impedance information in association; Step S6: User interaction and operation: The user performs gesture operations such as zooming in and out of the model or tapping the UI button on the screen of the smart device to interact and adjust the display content; Step S7: Real-time updating and feedback: Continuously receive new electrical impedance data, update display information, and provide real-time visual feedback of acupoint positioning and treatment effect.

[0044] Step S8: Data recording and evaluation: The application records the measurement data, identification results, and user operation information for subsequent analysis or report generation.

[0045] In step S3, based on the visual positioning guide and electrical precise positioning method, multi-level identification mechanism is used to ensure accurate positioning of the knee acupoint dense area, including the outer knee eye, inner knee eye, Sanyinjiao, and Yanglingquan regions. The electrical characteristics of different acupoints in the knee acupoint dense area may be similar. The special solution for the knee acupoint dense area is: The knee is a relatively dense area of acupoint distribution, such as the outer knee eye, inner knee eye, Sanyinjiao, and Yanglingquan, with close acupoint spacing. The electrical characteristics of different acupoints may be similar. The visual positioning guide and electrical precise positioning technology effectively solve this problem: The visual positioning guide method specifically includes: first, through AR marker code recognition and key anatomical point calibration, an accurate coordinate system is established to determine the expected approximate area of each acupoint. The electrical precise positioning method specifically includes: within the approximate area determined by visual positioning, the lowest impedance point is found through electrical impedance measurement to achieve precise positioning of the acupoint. The spatial matching algorithm specifically includes: the candidate low impedance point is compared with the standard acupoint template through the spatial matching algorithm, combined with the anatomical position information, to effectively distinguish different acupoints in the dense area.

[0046] The electrical characteristics of the acupoint are determined by the skin electrical impedance value at the acupoint being 20% to 60% lower than that of the adjacent non-acupoint area, with significant differences. The specific steps are as follows: Step A1, visual recognition and spatial registration: first, establish the spatial correspondence between the virtual model and the real knee through the pre-set AR marker code on the strap, then calibrate the coordinate system combined with key anatomical points such as the lower edge of the patella and the head of the fibula to determine the approximate distribution area of the main acupoints of the knee, and establish an individualized acupoint expected position template to provide spatial constraints for electrical detection; Step A2, electrical impedance acquisition stage: using the two-electrode method, a safe micro-current is applied between different areas of the knee, the voltage response of each point is collected, and the impedance value is calculated. Step A3, signal processing and feature extraction: the collected weak alternating voltage signal first passes through the voltage buffer circuit for isolation and impedance matching to prevent signal attenuation; then, the signal enters the differential amplification circuit, which provides a high common-mode rejection ratio, amplifies the voltage difference, and suppresses common-mode noise such as power frequency interference; the amplification factor is adjusted by external resistance to ensure that the signal amplitude is suitable for subsequent processing; the amplified signal is processed by a filter circuit, combining hardware filtering and software digital filtering to further remove random noise, and using an analog switch MAX306 to realize multi-channel time division multiplexing to reduce electrode crosstalk; the main control module digitizes the amplified signal and converts the voltage values of each measurement point into a relative impedance ratio through software algorithms to eliminate individual differences and environmental fluctuations; the normalization is based on the amplitude of the reference signal, and a logarithmic detection circuit is used to convert the AC signal amplitude ratio into a DC voltage output, which is then quantized by an A / D conversion module; Step A4, the main control module reads the A / D converted digital signal through the SPI interface and calculates the resistance impedance value of each measurement point based on the two-electrode method. The calculation formula is: wherein, is the measurement electrode voltage, is the excitation current, is the standard resistance value; The main control module maps the impedance values of multiple electrodes such as 6 measurement points to the knee spatial coordinates, constructs a two-dimensional grid graph through interpolation algorithms such as bilinear interpolation, and visualizes the impedance distribution of the knee region by taking the electrode position as the node and the impedance value as the height. The distribution map is updated every 2 seconds to ensure real-time performance. The main control module performs gradient analysis on the two-dimensional impedance distribution map and calculates the neighborhood impedance gradient of each point through a sliding window.

[0047] If the impedance value of a certain point is lower than that of its eight neighboring points and the absolute value of the difference exceeds the threshold, i.e., 20%-60% lower than the adjacent non-acupoint area, it is marked as a candidate minimum point. The system continuously collects multiple rounds of data and compares historical measurement results. When the minimum point remains stable in three consecutive samplings, i.e., the position deviation is less than 5mm and the impedance change is less than 10%, it is determined as an effective candidate acupoint; If the detection area changes or the human posture changes, the system automatically compensates the position through AR tracking to ensure that the displayed acupoints dynamically correspond to the actual physiological structure.

[0048] In step S5, the identified acupoint position is dynamically updated according to real-time resistance impedance data; and the following virtual content is accurately superimposed and rendered: a three-dimensional anatomical structure model of the knee, such as bones and main ligaments; At the identified low impedance points, highlight icons or markers such as acupoints like the outer knee eye, inner knee eye, Zusanli, and Yanglingquan are rendered. The real-time impedance value or status of the corresponding location, such as "acupoint," is displayed next to the marker or on the information panel.

[0049] Example 1: In this example, the detector hardware implementation is specifically as follows: Figure 1 , Figure 2 As shown, a highly elastic, breathable strap 102 is used as the main body. Seven conductive electrodes 101 are fixed inside the strap at designed intervals (e.g., 2-3 cm between each group of measuring electrodes). One of these is an excitation electrode 101a, located in the middle or at one end of the strap; the remaining six are measuring electrodes (101b), arranged in one or more rows. The electrode materials are preferably biocompatible Ag / AgCl or gold-plated electrodes.

[0050] like Figure 3 As shown, the signal processing module (103) uses an integrated impedance detection chip (such as NNC-BMS002) and its peripheral circuits to generate an AC excitation signal (such as 50kHz, 100μA) within a safe range, receive the voltage signal from the electrode (101b), and amplify and filter it (to remove power frequency interference, etc.). The ADC (such as AD7171) converts the conditioned analog signal into a digital signal.

[0051] The main control module (104) uses a low-power microcontroller based on the ARM Cortex-M series (such as the STM32 series or the Arduino-compatible BLE-UNO development board) and programs it to: control the signal processing module (103) to perform multi-channel cyclic measurement, read ADC data, calculate the impedance value of each point, run a simple algorithm (such as finding the minimum value point) to identify the acupoint index, and control the BLE module (105) to perform data transmission (such as sending an impedance value array or the identified acupoint index every 2 seconds).

[0052] The wireless communication module (105) uses a chip conforming to the BLE4.2 / 5.0 standard (such as the Nordic RF52 series or CH573). It communicates with the main controller (104) via a UART or SPI interface. A service UUID and a characteristic UUID need to be configured for the mobile APP to connect and subscribe to data.

[0053] The power module (106) uses a 3.7V, 500mAh or larger rechargeable lithium battery, and a low dropout regulator (LDO) provides a stable 3.3V or 5V voltage to each module. A charging management chip and a MicroUSB / USB-C charging interface can be added.

[0054] A pattern containing a unique pattern (such as) Figure 1A marker code (107) is printed on the outside of the bandage (102) in a prominent location (e.g., as shown in FIG. 1). The marker code needs to be uploaded to the Vuforia Developer Portal and generate a Database for Unity project import.

[0055] 2. Augmented Reality Application (APP) Implementation: Development Environment: Use Unity Engine (version such as 2020 LTS or newer), import Vuforia Engine SDK and plugins for Bluetooth communication (such as UnityBluetoothLEPlugin).

[0056] Scene Setup: Create an AR scene in Unity and add a Vuforia AR Camera.

[0057] Import the marker code Database generated by Vuforia. Create an ImageTarget object and associate the corresponding marker code pattern.

[0058] Import or create a 3D knee dissection model (which can be simplified, such as the lower end of the femur, the upper end of the tibia, and the patella), and set it as a child object of the ImageTarget, ensuring that the model can be correctly superimposed on the bandage / knee position when the marker code is recognized.

[0059] Create a prefab for acupoint marking (such as a colored sphere or icon).

[0060] Design a UICanvas containing buttons (such as "Start", "Acupoint Recognition", "Hide Panel", "Reset", etc.) and text / image elements for displaying data.

[0061] Bluetooth Communication Implementation: Use BluetoothLEPlugin. Initialize Bluetooth in the script and scan for a device named "ble-uno4.2" (or the actual name).

[0062] Connect to the device and subscribe to the specified service UUID and characteristic value UUID to receive impedance data (for example, an array containing 6 impedance values).

[0063] Parse the received byte stream data in the Update loop and convert it to impedance values.

[0064] Acupoint Visualization: According to the received data (such as the impedance value array), analyze it in real time in the script (such as finding the minimum value point).

[0065] According to the analysis results (such as the index), the acupoint point marker prefabricated body is instantiated at the corresponding anatomical position of the knee 3D model (the acupoint point empty object needs to be set in the model in advance).

[0066] The script can be written to dynamically update the marker position (if fine-tuning is needed according to continuous data) or update the UI text to display the impedance value.

[0067] User interaction: Gesture scaling: Write a script (such as ModelScaler) and hang it on the knee 3D model. In Update, detect Input.touchCount == 2, calculate the distance difference between the two fingers, scale the transform.localScale of the model by a certain proportion, and limit it between minScale and maxScale.

[0068] UI button: Add a Button component to the button, and write an OnClick event response function (such as setting panel.SetActive(false) in HidePanelonClick).

[0069] Data fusion: Ensure that when the marker code is stably tracked, the received impedance data can drive the virtual acupoint to display at the correct position in the real-world view. Rely on the spatial positioning accuracy of Vuforia and the real-time nature of Bluetooth data.

[0070] 3. Method of use: The user fixes the elastic band 102 on the knee, and the electrode surface is close to the skin. Press the power switch of the detector to start the device. Open the special APP on the phone. Align the phone camera with the marker code 107 on the band to perform initial calibration (a few seconds). At this time, the knee 3D model should appear at the band position. The APP automatically connects the detector Bluetooth. After successful connection, the screen starts to display the real-time knee image, and superimposes the knee model and the acupoint position identified according to the resistance impedance data (such as highlighted small round dots). The user can zoom in and out of the knee 3D model on the screen by opening and closing it with two fingers, making it easier to observe details. The user can click on the acupoint to view detailed information (such as name, resistance impedance value), or click on the UI button to hide the information panel for clearer observation.

[0071] During treatment (such as when the doctor performs acupuncture), the APP continuously updates and displays, providing intuitive feedback.

[0072] After use, turn off the power of the APP and the detector.

[0073] Example 2: A portable human knee acupoint detector: Electrode module: the core is a multi-group electrode array 101, which works on the principle of two-electrode method. The preferred solution contains 6 independent measurement electrodes 101b and 1 common excitation electrode 101a, a total of 7 electrodes, which are arranged vertically on an elastic band 102 at appropriate intervals, so as to closely fit the knee curve of the human body. The electrode 101 is responsible for applying a safe excitation current to the knee and measuring the response voltage.

[0074] Signal processing module 103: connected with the electrode array. It contains a dedicated impedance detection chip such as NNC-BMS002 or similar to generate excitation signals, amplify, filter and preliminarily process voltage signals. An analog-to-digital converter ADC such as AD7171 or similar converts the processed analog voltage signal into a digital signal for the host processor.

[0075] Host module 104: such as an Arduino-based microcontroller or MCU. It controls the entire measurement process such as channel switching, excitation frequency setting, receives ADC data, processes data such as calculating impedance values, runs algorithms to identify low-impedance points corresponding to acupoint regions, and manages data transmission.

[0076] Wireless communication module 105: such as a low-power Bluetooth BLE module, for example, based on CH573 chip module. It receives instructions and data from the host module 104 and establishes a wireless connection with external smart devices such as mobile phones and tablets, and transmits impedance raw data, processing results such as identified acupoint coordinates and device status information.

[0077] Power module 106: such as a rechargeable lithium battery. It provides stable operating voltage for all the above-mentioned electronic modules.

[0078] Augmented reality AR visualization module: this module runs in the form of an application on external smart devices such as smartphones or tablets, and is an indispensable part of the system. Its main functions are: Receive real-time data impedance values and identified low-resistance point positions from the probe wireless communication module 105 through the Bluetooth interface of the device.

[0079] Call the device camera to capture real-time images of the knee.

[0080] Use the AR engine, preferably the Unity engine, to integrate the Vuforia SDK to identify the special marker code 107 pre-set on the elastic band 102 or the skin near the knee for initial spatial registration, establishing an accurate correspondence between the virtual coordinate system and the real knee position.

[0081] Real-time overlay and rendering of the three-dimensional anatomical structure model of the knee on the real knee image captured by the camera.

[0082] According to the received impedance data and processing results, overlay and render the identified acupoint positions at the corresponding positions.

[0083] The resistance impedance value or state information can be associated and displayed.

[0084] A user interface (UI) and gesture interaction are provided: Gesture operation: support for adjusting the size of the virtual model, such as two-finger zoom.

[0085] UI button: for displaying / hiding the information panel, switching the display mode such as only displaying the acupoint, resetting the view, etc.

[0086] Measurement data and operation logs can be recorded.

[0087] An augmented reality application method of a portable human knee acupoint detector: the method utilizes the detector and the smart device APP as claimed in claims 1-6 to work together, and the steps are as follows: S1: device wearing and starting: the user wraps the elastic band 102 of the detector around the knee to ensure good contact between the electrode 101 and the skin. Turn on the power of the detector.

[0088] S2: resistance impedance data acquisition and processing: the detector acquires resistance impedance data of different areas of the knee through multiple electrodes 101 at a set time interval, such as every 2 seconds. The signal processing module 103 performs signal conditioning and ADC conversion. The main control module 104 processes the data, calculates the impedance values of each point, and applies algorithms such as screening the minimum impedance points to identify potential low impedance points of the knee acupoints.

[0089] S3: wireless data transmission: the main control module 104 transmits the measurement data, such as the impedance values of each point and / or the identification results such as the acupoint coordinate index, to the paired smart device APP through the BLE module 105.

[0090] S4: augmented reality scene initialization: the user starts the special APP on the smart device. The APP activates the device camera, scans and identifies the pre-set marker code 107 on the band 102 or near the knee. Based on the identification result, the Vuforia engine calculates the conversion relationship between the virtual model coordinate system and the real world coordinate system, and realizes the initial positioning calibration.

[0091] S5: acupoint visualization superposition: the APP receives the data transmitted in step S3. The APP combines the established coordinate correspondence to accurately superimpose and render the following virtual content on the real-time knee image captured by the camera: A three-dimensional anatomical structure model of the knee, such as bones and major ligaments.

[0092] At the position of the identified low impedance points, render highlighted icons or marker points, such as the outer knee eye, the inner knee eye, the Sanyinjiao point, and the Yanglingquan point.

[0093] The real-time impedance value or status of the corresponding location, such as "acupoint", can be displayed next to the marker or on the information panel.

[0094] S6: User Interaction and Operation: Users interact via the touchscreen. Gesture controls: such as pinching with two fingers to zoom in and out of the virtual model, making it easier to observe different details.

[0095] UI Buttons: Clicking the button can show / hide the detailed data panel, switch the display mode (e.g., show only acupoints), reset the AR view, etc.

[0096] S7: Real-time updates and feedback: The APP continuously receives new data from the detector and dynamically updates the impedance information superimposed on the screen accordingly. The position of acupoint markers can be fine-tuned based on continuous measurements, providing users, doctors, or patients with continuous and intuitive feedback on acupoint location and changes during the treatment process.

[0097] S8: Optional Data Recording and Evaluation: The app can record historical measurement data, identification results, and user actions. This data can be used to generate simple reports for doctors to assess trends or for patients to track their own progress.

[0098] Skin preparation requirements before testing To ensure the accuracy and reliability of impedance measurements, the skin in the test area should ideally be kept clean and dry. Specific requirements and principles are as follows: The importance of dry skin: Conductive substances such as sweat and oil on the skin surface can significantly affect the results of electrical impedance measurement, leading to errors in acupoint identification; Treatment: Before use, gently clean the skin on the knee with an alcohol swab, and wear the device after it is completely dry; Equipment adaptability: This system is optimized for relatively dry skin conditions to ensure stable and reliable measurement results under normal physiological conditions.

[0099] Specific methods for acupoint identification This invention is based on a technical approach of visual positioning guidance and precise electrical determination. It ensures accurate positioning of dense acupoint areas on the knee through a multi-level recognition mechanism. The specific steps are as follows: Visual recognition and spatial registration: First, the spatial correspondence between the virtual model and the real knee is established by using the preset AR marker code on the strap. Then, the coordinate system is calibrated by combining key anatomical points such as the lower edge of the patella and the fibular head to determine the approximate distribution area of ​​the main acupoints on the knee, so as to establish an individualized acupoint expected location template and provide spatial constraints for electrical detection.

[0100] Impedance acquisition: Using two-electrode method, a safe micro-current is applied between different regions of the knee, and the voltage response of each point is collected to calculate the impedance value. Experiments and literature show that the skin impedance at acupoints is usually 20%~60% lower than that at adjacent non-acupoint regions, with significant differences.

[0101] Signal processing and feature extraction: The collected weak alternating voltage signal first passes through the voltage buffer circuit for isolation and impedance matching to prevent signal attenuation. Then, the signal enters the differential amplification circuit, which provides a high common-mode rejection ratio, amplifies the voltage difference, and suppresses common-mode noise such as power frequency interference. The amplification factor can be adjusted by external resistance to ensure that the signal amplitude is suitable for subsequent processing. The amplified signal is processed by the filter circuit, combining hardware filtering and software digital filtering to further remove random noise. The analog switch MAX306 is used to realize multi-channel time division multiplexing to reduce cross-talk between electrodes. Then the main control module digitizes the amplified signal, and through software algorithm realizes amplitude normalization, converts the voltage value of each measurement point to relative impedance ratio, to eliminate individual differences and environmental fluctuations. Normalization is based on the amplitude of the reference signal, and the log detection circuit is used to convert the AC signal amplitude ratio to DC voltage output, and then quantized by A / D conversion module.

[0102] Augmented reality visualization superposition: The intelligent device APP receives the recognition result and superimposes the three-dimensional anatomical model and the determined acupoint on the real-time image of the knee captured by the camera, and displays the corresponding impedance information. Users can zoom in and rotate the model through gestures to achieve interactive observation.

[0103] The above embodiments demonstrate the basic principles and implementation approaches of the present application. Those skilled in the art can adjust and improve the specific device selection, software implementation details, and interaction methods within the scope defined by the claims, and these adjustments and improvements should fall within the protection scope of the present application.

Claims

1. A portable human knee acupoint detector, characterized in that: include: Electrode measurement module: used to collect and measure the electrical impedance data of the human knee area, including multiple sets of measuring electrodes (101). The electrodes (101) adopt the two-electrode method measurement principle, including a common excitation electrode (101a) and multiple independently distributed measuring electrodes (101b). The electrodes (101) are set on the elastic band (102) and are specifically designed to fit the curved surface of the knee. Signal processing module (103): Electrically connected to the impedance measurement module, used to condition, filter, amplify and convert the acquired impedance signal into digital signal; Main control module (104): electrically connected to signal processing module (103), used to control the impedance measurement process, process measurement data and identify low impedance regions; Wireless communication module (105): electrically connected to the main control module (104), used to wirelessly transmit the processed impedance data and identification results to external smart devices; The detector also includes a power module (106) and an augmented reality visualization module.

2. The portable human knee acupoint detector according to claim 1, characterized in that: The augmented reality visualization module includes a smart device for running related applications; the augmented reality visualization module is used to receive data from the wireless communication module (105) and, in combination with the real-time images of the knee captured by the smart device's camera, display the knee-specific anatomical structure model, the identified acupoint locations, and impedance information on the screen of the smart device in an overlay manner.

3. A portable human knee acupoint detector according to claim 2, characterized in that: The augmented reality visualization module is developed based on the Unity engine and Vuforia AR SDK. When in use, it first establishes a spatial correspondence between the virtual model and the real knee by identifying the marker code (107) set on the elastic band (102) or the skin near the knee. It then calibrates the coordinate system of the augmented reality in combination with the preset key anatomical point data to achieve initial positioning, and combines the impedance data to achieve accurate superposition display of the knee acupoints on the real image.

4. A portable human knee acupoint detector according to claim 3, characterized in that: The augmented reality visualization module is an AR visualization module that provides user interaction functions, including: (1) Adjust the display size of the knee anatomical structure model and acupoints by using gestures; (2) Trigger the display / hide of specific information, switch views or reset the display via UI buttons.

5. A portable human knee acupoint detector according to claim 1, characterized in that: The electrode measurement module includes six sets of measuring electrodes (101), which share one excitation electrode (101a). The seven electrodes are arranged vertically on the elastic band (102) according to the distribution characteristics of acupoints on the knee.

6. A portable human knee acupoint detector according to claim 1, characterized in that: The signal processing module (103) includes an impedance detection chip and an analog-to-digital converter. The impedance detection chip is used to generate an excitation signal and measure the voltage response. The analog-to-digital converter is used to convert the analog voltage signal into a digital signal. The power supply module is used to supply power to the electrode measurement module, the signal processing module, the main control module, and the wireless communication module. The wireless communication module (105) is a low-power Bluetooth module.

7. An augmented reality application method for a portable human knee acupoint detector, using a portable human knee acupoint detector as described in claim 4, characterized in that: Includes the following steps: Step S1: Skin preparation and device wearing: Ensure that the skin of the knee to be tested is clean and dry, wear the elastic strap (102) of the detector on the knee, ensure that the electrode (101) is in good contact with the skin, and turn on the power of the detector; Step S2: Augmented Reality Scene Initialization: Launch the application on the smart device, activate the camera, identify the preset marker code (107) to establish the spatial correspondence between the virtual model and the real knee, and combine it with key anatomical points; Step S3: Electrical impedance data acquisition and processing: The electrical impedance data of a specific area of ​​the knee after visual positioning is acquired by the electrical impedance measurement module, and processed by the signal processing module (103) and the main control module (104) to identify low electrical impedance points; Step S4: Wireless data transmission: The main control module (104) sends the impedance data and low impedance point location information to the smart device through the wireless communication module (105); Step S5: Acupoint Visualization Overlay: The application receives the data transmitted in step S4, and based on the established visual positioning and the established coordinate correspondence, overlays and displays the three-dimensional anatomical model of the knee and the identified acupoint locations on the real-time image of the knee captured by the camera, and displays the corresponding electrical impedance information. Step S6: User interaction and operation: Users interact with the smart device screen by performing gesture operations or tapping UI buttons to adjust the displayed content; Step S7: Real-time update and feedback: Continuously receive new impedance data, update the display information, and provide real-time visual feedback on acupoint location and treatment effect; Step S8: Data Recording and Evaluation: The application records measurement data, identification results, and user operation information for subsequent analysis or report generation.

8. The augmented reality application method of the portable human knee acupoint detector according to claim 7, characterized in that: In step S3, based on the method of visual positioning guidance and electrical precise positioning, the accurate positioning of the dense area of ​​acupoints on the knee is ensured through a multi-level recognition mechanism. The dense area of ​​acupoints on the knee includes the area where the outer knee eye, inner knee eye, Zusanli, and Yanglingquan are located, and the electrical characteristics of different acupoints in the dense area of ​​acupoints on the knee are similar. The visual positioning-first method is as follows: First, an accurate coordinate system is established by recognizing AR marker codes and calibrating key anatomical points to determine the approximate expected area of ​​each acupoint. The specific method of precise electrical positioning is as follows: within the approximate area determined by visual positioning, the point of lowest impedance is found through electrical impedance measurement to achieve precise positioning of acupoints; Spatial matching algorithm: Candidate low impedance points are compared with standard acupoint templates using a spatial matching algorithm, and combined with anatomical location information, to effectively distinguish different acupoints in dense areas.

9. The augmented reality application method of the portable human knee acupoint detector according to claim 8, characterized in that: The electrical characteristics of acupoints are determined by the fact that the skin impedance at the acupoint is 20% to 60% lower than that of adjacent non-acupoint areas; the specific steps are as follows: Step A1, Visual Recognition and Spatial Registration: First, the spatial correspondence between the virtual model and the real knee is established by using the preset AR marker code on the strap. Then, the coordinate system is calibrated by key anatomical points to determine the approximate distribution area of ​​the main acupoints on the knee, so as to establish an individualized acupoint expected location template and provide spatial constraints for electrical detection. Step A2, Impedance Acquisition Stage: Using the two-electrode method, a safe microcurrent is applied between different areas of the knee, the voltage response at each point is acquired, and the impedance value is calculated; Step A3, Signal Processing and Feature Extraction: The acquired AC voltage signal is first isolated and impedance matched by a voltage buffer circuit. The signal then enters a differential amplifier circuit, which provides a high common-mode rejection ratio to amplify the voltage difference while suppressing common-mode noise such as power frequency interference. The amplification factor is adjusted by an external resistor to ensure that the signal amplitude is suitable for subsequent processing. The amplified signal is then processed by a filtering circuit, combining hardware filtering and software digital filtering to further remove random noise. Simultaneously, analog switches are used to achieve multi-channel time-division multiplexing to reduce crosstalk between electrodes. The main control module performs digital processing on the amplified signal and uses software algorithms to normalize the amplitude, converting the voltage values ​​at each measurement point into relative impedance ratios. The normalization is based on the amplitude of the reference signal, and a logarithmic detector circuit is used to convert the AC signal amplitude ratio into a DC voltage output, which is then quantized by the A / D conversion module. Step A4: The main control module reads the digital signal after A / D conversion and calculates the impedance value of each measurement point based on the two-electrode method principle; The calculation formula is: in, To measure the electrode voltage, To excite the current, This is the standard resistance value; The main control module maps the impedance values ​​of multiple sets of electrodes onto the spatial coordinates of the knee and constructs a two-dimensional mesh map through an interpolation algorithm. This map uses the electrode positions as nodes and the impedance values ​​as heights to visualize the impedance distribution in the knee region and highlight the low-resistance areas. If the impedance value of a certain point is lower than that of its eight neighboring points and the absolute value of the difference exceeds the threshold, that is, it is 20%-60% lower than that of the neighboring non-acupuncture area, then it is marked as a candidate minimum point. The system continuously collects data in multiple rounds and compares it with historical measurement results. When the minimum point remains stable in three consecutive samplings, i.e., the positional shift is <5mm and the impedance change is <10%, it is determined to be a valid candidate acupoint. If the detection area changes or the human posture changes, the system automatically performs position compensation through AR tracking to ensure that the displayed acupoints dynamically correspond to the actual physiological structure.

10. The augmented reality application method of the portable human knee acupoint detector according to claim 7, characterized in that: In step S5, the identified acupoint locations are dynamically updated based on real-time electrical impedance data; the following virtual content is then precisely overlaid and rendered: Three-dimensional anatomical model of the knee; At the identified low impedance points, render a highlight icon or marker. The real-time impedance value or status of the corresponding location is displayed next to the marker or on the information panel.

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