Self-adaptive meridian-reflection region XR visualization system based on multi-modal perception

Abstract

The invention relates to the cross technical field of augmented reality (XR) and traditional Chinese medicine digital diagnosis and treatment, in particular to a multi-modal perception-based self-adaptive meridian-reflecting region XR visualization system, which comprises a multi-source data acquisition module, a self-adaptive mapping engine, an XR dynamic rendering module and a diagnosis and treatment closed-loop feedback module, the multi-source data acquisition module is used for acquiring basic data and real-time biological signals, and the basic data comprises an auricular point positioning database, a palm acupuncture point database and a sole reflecting region database; the method has the advantages that the virtual meridian model is driven in real time through biological signals to dynamically adapt to individual differences, and the problems of model stiffness, feedback hysteresis and area splitting of an existing XR meridian system are solved; and an intelligent diagnosis and treatment system for driving adaptive matching of the meridian model through the multi-source biological signals is constructed, and the clinical precision and individuation requirements are well met.

Description

Technical Field

[0001] The present invention relates to the technical field of the intersection of augmented reality (XR) and digital diagnosis and treatment in traditional Chinese medicine, and specifically to an adaptive meridian-reflex zone XR visualization system based on multimodal perception. Background Art

[0002] Currently, the existing meridian-reflex zone XR visualization system has the following technical defects:

[0003] 1. Model rigidity: The standard meridian model cannot adapt to individual anatomical differences (e.g., acupoint offset >5mm in obese individuals);

[0004] 2. Reflex zone fragmentation: The auricular acupoint, palm, and sole positioning systems operate independently and lack a linkage mechanism;

[0005] 3. Lack of dynamic feedback: Traditional XR only statically marks acupoints and does not associate them with real-time updates of biological signals.

[0006] To this end, an adaptive meridian-reflection zone XR visualization system based on multimodal perception is proposed to overcome the above technical defects and solve the above technical problems. Summary of the Invention

[0007] In order to overcome the technical defects in the prior art and effectively solve the technical problems in the background technology, the present invention provides the following technical solutions:

[0008] An adaptive meridian-reflex zone XR visualization system based on multimodal perception, including a multi-source data acquisition module, an adaptive mapping engine, an XR dynamic rendering module, and a diagnosis and treatment closed-loop feedback module;

[0009] The multi-source data acquisition module is used to collect basic data and real-time biological signals, wherein the basic data includes an ear acupoint positioning database, a palm acupoint database, and a foot reflexology database;

[0010] The adaptive mapping engine is used for dynamic coordinate system conversion algorithm and nerve conduction simulation;

[0011] The XR dynamic rendering module is used for the linkage visualization of ear acupoints, palms and soles reflex zones;

[0012] The diagnosis and treatment closed-loop feedback module includes a five-step diagnosis and treatment path, which specifically includes the following steps:

[0013] S1: Real-time matching of user basic biological data through XR engine;

[0014] S2: Generate adaptive acupoint coordinates from the data collected by the XR engine through the adaptive mapping engine;

[0015] S3: Adaptive acupoint coordinates generated by the adaptive mapping engine are used to push the best intervention plan to the physician;

[0016] S4: The physician performed acupuncture using a force feedback glove (depth 12 ± 0.5 mm);

[0017] S5: The force feedback gloves dynamically adjust parameters and complete diagnosis and treatment through the patient's real-time pain feedback.

[0018] As a preferred technical solution of the present invention, the auricular point positioning data is collected through a multi-task shared feature model (i.e., 91-point high frame rate recognition and real-time detection with a frame rate of 30fps).

[0019] As a preferred technical solution of the present invention, the palm acupoint data is collected through 21-node MediaPipe hand landmark mapping + Euclidean distance calculation with an error of ≤1.2mm.

[0020] As a preferred technical solution of the present invention, the plantar reflex zone data is collected through a dynamic foot length / foot width ratio coordinate system (foot length ratio m=l / L, foot width ratio n=w / W) that can adapt to body shape differences.

[0021] As a preferred technical solution of the present invention, the real-time biological signal is collected through accelerometer meridian vibration + EDA pain feedback with a conduction velocity quantization error of <5%.

[0022] As a preferred technical solution of the present invention, the dynamic coordinate system conversion algorithm is based on a holographic mapping algorithm and is used for auricular point-palm-plantar coordinate fusion and dynamic calibration of biological signals.

[0023] As a preferred technical solution of the present invention, the signal attenuation formula of the nerve conduction simulation is: V_conduction=V0·e^(-β·d) (d is the distance between acupoints, β is the tissue damping coefficient).

[0024] As a preferred technical solution of the present invention, the linkage visualization of the ear acupoint reflex zone triggers the dynamic light flow of the heart meridian by clicking the ear Shenmen acupoint, and is presented in the form of an inverted fetal projection.

[0025] As a preferred technical solution of the present invention, the palm reflex zone linkage visualization triggers the hand Jueyin pericardium meridian thermal map by making a fist, and is presented in the form of lung area (thenar eminence) / heart area (hypothenar eminence).

[0026] As a preferred technical solution of the present invention, the linkage visualization of the plantar reflex zone is synchronized with the kidney meridian energy animation by pressing the Yongquan acupoint, and is presented in the form of the liver reflex zone (-34.4, -31.8).

[0027] In this invention, the origin and development of the model are as follows:

[0028] 1. Theoretical basis of ancient Chinese medicine: The theory of ear acupuncture points can be traced back to the Yellow Emperor's Classic of Internal Medicine. Although "ear acupuncture points" are not explicitly mentioned in it, the connection between the ear and internal organs and meridians is proposed. For example, "The ear is where the main meridians gather." In the Qing Dynasty, Zhang Zhenyun's "Li Zheng Anma Yao Shu" drew a "Diagram of Five Internal Organ Acupuncture Points on the Back of the Ear", which for the first time divided the back of the ear into five areas: heart, liver, spleen, lung, and kidney.

[0029] 2. Discoveries of modern medicine: In the 1950s, French physician Paul Nogier discovered the theory of inverted fetal projection of the auricle and developed a map of auricular reflexology. He proposed that auricular acupoint stimulation affects the central nervous system through the spinothalamic tract, regulating visceral function. In 1987, the World Health Organization (WHO) promulgated the "International Standardization Scheme for Auricular Acupoints," establishing 90 standardized auricular acupoint names and locations, marking the global recognition of auricular acupoint theory.

[0030] Compared with the existing technology, the beneficial effects of the present invention are: this adaptive meridian-reflex area XR visualization system based on multimodal perception uses biological signals to drive the virtual meridian model in real time to dynamically adapt to individual differences, solving the problems of "model rigidity, feedback hysteresis, and regional fragmentation" of the existing XR meridian system. By integrating the dynamic mapping technology of the three major reflex areas of ear acupoints, palms, and soles of the feet into the meridian reflex areas of the whole body, an intelligent diagnosis and treatment system driven by multi-source biological signals to adaptively match the meridian model is constructed, which well meets the clinical precision and personalization needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a structural block diagram of the system module of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] See also Figure 1 , the present invention provides a technical solution: an adaptive meridian-reflection zone XR visualization system based on multimodal perception, characterized by: comprising a multi-source data acquisition module, an adaptive mapping engine, an XR dynamic rendering module and a diagnosis and treatment closed-loop feedback module;

[0034] The multi-source data acquisition module is used to collect basic data and real-time biological signals. The basic data includes the ear acupoint positioning database, the palm acupoint database and the plantar reflexology database.

[0035] Adaptive mapping engine for dynamic coordinate system transformation algorithm and nerve conduction simulation;

[0036] The XR dynamic rendering module is used for the coordinated visualization of ear acupoints, palms, and soles reflex zones;

[0037] The diagnosis and treatment closed-loop feedback module includes a five-step diagnosis and treatment pathway, which specifically includes the following steps:

[0038] S1: Real-time matching of user basic biological data through XR engine;

[0039] S2: Generate adaptive acupoint coordinates from the data collected by the XR engine through the adaptive mapping engine;

[0040] S3: Adaptive acupoint coordinates generated by the adaptive mapping engine are used to recommend optimal intervention plans to physicians (e.g., acupuncture at Neiguan acupoint).

[0041] S4: The physician performed acupuncture using a force feedback glove (depth 12 ± 0.5 mm);

[0042] S5: The force feedback gloves dynamically adjust parameters and complete diagnosis and treatment through the patient's real-time pain feedback.

[0043] Furthermore, the auricular point localization data are collected through a multi-task shared feature model (i.e., 91-point high-frame-rate recognition and real-time detection at a frame rate of 30fps).

[0044] Furthermore, palm acupoint data are collected through 21-node MediaPipe hand landmark mapping + Euclidean distance calculation with an error of ≤1.2mm.

[0045] Furthermore, the plantar reflex zone data are collected through a dynamic foot length / foot width ratio coordinate system that can adapt to body shape differences (foot length ratio m=l / L, foot width ratio n=w / W (positioning error compensation factor α=0.03l)).

[0046] Furthermore, real-time biosignals are collected through accelerometer meridian vibration + EDA pain feedback with a conduction velocity quantization error of <5%. Specifically, 2. Biosignal drive includes:

[0047] Pain feedback thermal field equation: R_heat=R0 / (1+γ·Pain_level) (γ=0.15);

[0048] Nerve conduction attenuation model: V=V_max·e^(-0.2d) (d is the conduction distance).

[0049] Furthermore, the dynamic coordinate system conversion algorithm is based on the holographic mapping algorithm (specifically Zhang Yong's holographic theory, whose principle is an existing mature technology and will not be described in detail here), which is used for auricular-palm-plantar coordinate fusion and dynamic calibration of biological signals.

[0050] Furthermore, the signal attenuation formula for nerve conduction simulation is: V_conduction=V0·e^(-β·d) (d is the distance between acupoints, β is the tissue damping coefficient).

[0051] Furthermore, the linkage visualization of the ear acupoint reflex zones triggers the dynamic light flow of the heart meridian by clicking on the ear Shenmen acupoint, which is presented in the form of an inverted fetal projection.

[0052] Furthermore, the linkage visualization of the palm reflex areas triggers the hand Jueyin pericardium meridian thermal map by clenching the fist, and presents it in the form of lung area (thenar eminence) / heart area (hypothenar eminence) and so on.

[0053] Furthermore, the linkage visualization of the plantar reflex zones is achieved by synchronizing the kidney meridian energy animation by pressing the Yongquan acupoint, and presenting it in the form of the liver reflex zone (-34.4, -31.8).

[0054] Specific application examples are as follows:

[0055] Example 1: Insomnia diagnosis and treatment (ear acupuncture-palm linkage)

[0056] 1. The patient wears XR glasses and the system detects:

[0057] Auricular acupoints: Abnormal HSV value in the Shenmen acupoint area (redness);

[0058] Palm: hypothenar EMG amplitude ↓30%;

[0059] 2.XR dynamic display:

[0060] The red light flow speed of the heart meridian path is reduced to 0.1m / s (normal>0.3m / s);

[0061] Automatically mark recommended acupoints: Ear Shenmen acupoint + Palm Shaofu acupoint;

[0062] 3. Physician operation:

[0063] Acupuncture Shaofu acupoint (depth 8 mm) was performed with force feedback gloves, and the pain level monitored by EDA was level 1;

[0064] XR real-time display of pulse signals uploaded to the thalamus (conduction velocity 0.25m / s→0.4m / s).

[0065] Example 2: Foot Reflexology (Obesity)

[0066] 1. 3D foot scanning:

[0067] Foot length l=250mm, foot width w=105mm (standard L=230mm, W=95mm);

[0068] Calculate the proportional coefficients: m=1.087, n=1.105;

[0069] 2. Locate the thyroid reflex area:

[0070] Standard coordinates (-15.2, -20.1) → actual coordinates (-16.5, -22.2);

[0071] XR projects the laser spot to the positioning point (error < 0.8mm);

[0072] 3. Moxibustion thermal simulation:

[0073] Thermal diffusion radius = basic value 3cm × (1 + 0.2 * ΔT) (ΔT is the rise in skin temperature).

[0074] The comparative data of the present invention and the traditional technology are shown in the following table:

[0075] In summary, the present invention deeply integrates core technologies such as real-time identification of ear acupoints, palm landmark mapping, and dynamic coordinate system of the sole of the foot, forming a complete patent barrier covering "ear-hand-foot-meridian", with significant clinical value and technological innovation.

[0076] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An adaptive meridian-reflex zone XR visualization system based on multimodal perception, characterized by: It includes a multi-source data acquisition module, an adaptive mapping engine, an XR dynamic rendering module, and a diagnosis and treatment closed-loop feedback module; The multi-source data acquisition module is used to collect basic data and real-time biological signals, wherein the basic data includes an ear acupoint positioning database, a palm acupoint database, and a foot reflexology database; The adaptive mapping engine is used for dynamic coordinate system conversion algorithm and nerve conduction simulation; The XR dynamic rendering module is used for the linkage visualization of ear acupoints, palms and soles reflex zones; The diagnosis and treatment closed-loop feedback module includes a five-step diagnosis and treatment path, which specifically includes the following steps: S1: Real-time matching of user basic biological data through XR engine; S2: Generate adaptive acupoint coordinates from the data collected by the XR engine through the adaptive mapping engine; S3: Adaptive acupoint coordinates generated by the adaptive mapping engine are used to push the best intervention plan to the physician; S4: The physician performed acupuncture using a force feedback glove (depth 12 ± 0.5 mm); S5: The force feedback gloves dynamically adjust parameters and complete diagnosis and treatment through the patient's real-time pain feedback.

2. The adaptive meridian-reflective zone XR visualization system based on multimodal perception according to claim 1 is characterized by: The auricular point positioning data is collected through a multi-task shared feature model (i.e., 91-point high frame rate recognition and real-time detection at a frame rate of 30fps).

3. The adaptive meridian-reflection zone XR visualization system based on multimodal perception according to claim 1 is characterized by: The palm acupoint data is collected through 21-node MediaPipe hand landmark mapping + Euclidean distance calculation with an error of ≤1.2mm.

4. The adaptive meridian-reflection zone XR visualization system based on multimodal perception according to claim 1, characterized in that: The plantar reflex zone data is collected through a foot length / foot width ratio dynamic coordinate system (foot length ratio m=l / L, foot width ratio n=w / W) that can adapt to body shape differences.

5. The adaptive meridian-reflection zone XR visualization system based on multimodal perception according to claim 1 is characterized by: The real-time biological signals are collected through accelerometer meridian vibration + EDA pain feedback with a conduction velocity quantization error of <5%.

6. The adaptive meridian-reflection zone XR visualization system based on multimodal perception according to claim 1, characterized in that: The dynamic coordinate system conversion algorithm is based on a holographic mapping algorithm and is used for auricular acupoint-palm-plantar coordinate fusion and dynamic calibration of biological signals.

7. The adaptive meridian-reflective zone XR visualization system based on multimodal perception according to claim 1, characterized in that: The signal attenuation formula of the nerve conduction simulation is: V_conduction=V0·e^(-β·d) (d is the distance between acupoints, β is the tissue damping coefficient).

8. The adaptive meridian-reflection zone XR visualization system based on multimodal perception according to claim 1, characterized in that: The ear acupoint reflex zone linkage visualization triggers the dynamic light flow of the heart meridian by clicking the ear Shenmen acupoint, and is presented in the form of an inverted fetal projection.

9. The adaptive meridian-reflective zone XR visualization system based on multimodal perception according to claim 1, characterized in that: The palm reflex zone linkage visualization triggers the hand Jueyin pericardium meridian thermal map by making a fist, and is presented in the form of lung area (thenar eminence) / heart area (hypothenar eminence).

10. The adaptive meridian-reflection zone XR visualization system based on multimodal perception according to claim 1, characterized in that: The linkage visualization of the plantar reflex zone is synchronized with the kidney meridian energy animation by pressing the Yongquan acupoint and is presented in the form of the liver reflex zone (-34.4, -31.8).

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