A kind of assisted system for searching acupuncture point based on needle sensitivity

Abstract

The present application relates to the technical field of acupoint detection auxiliary, disclose a kind of based on needle's sensitized acupoint searching auxiliary system, including sensitized acupoint detection module, auxiliary evaluation module and man-machine interaction module;Wherein, sensitized acupoint detection module includes baseline calibration module, frequency domain impedance measurement module and sensitized acupoint identification module;Auxiliary evaluation module includes needle treatment scheme determination module, needle data recording module and treatment method evaluation module.The present application not only can accurately locate sensitized acupoint by double frequency domain threshold determination method and support vector machine algorithm, but also can recommend needle treatment scheme according to the disease symptom of patient, and evaluate needle treatment effect, to realize the full-process automation of sensitized acupoint detection and auxiliary treatment, solve the core problem that traditional method relies on experience, operation is not transparent.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of acupoint detection assistance, in particular to a sensitized acupoint exploration auxiliary system based on a bronze needle. BACKGROUND

[0002] At present, simple acupuncture of conventional acupoints is a traditional treatment method of traditional Chinese medicine. It selects specific conventional acupoints of the human body, such as Hegu, Zusanli, Neiguan, etc., according to the meridian theory of traditional Chinese medicine. By stimulating acupoints through acupuncture, meridian qi is stimulated, and the functions of qi and blood and viscera of the human body are regulated. In terms of treating diseases, for some pain diseases such as headache, back pain, joint pain, etc., acupuncture can dredge meridians and regulate qi and blood, thereby effectively relieving pain. For internal diseases such as cold, insomnia, indigestion, etc., acupuncture can adjust the balance of yin and yang of viscera and improve symptoms. For gynecological diseases such as irregular menstruation, acupuncture can regulate the Chong and Ren meridians and play a conditioning role. However, simple acupuncture therapy also has certain limitations. For some acute and critical conditions, it is difficult to work alone, and the effect is affected by factors such as strong subjectivity of acupoint selection, non-visual display of acupuncture techniques, and individual differences of patients, so the specific effect cannot be objectively and accurately evaluated.

[0003] Bronze needle is one of the nine needles in ancient China, which is an important part of traditional acupuncture tools, and embodies the diversity and flexibility of traditional Chinese medicine acupuncture treatment. It provides a non-invasive treatment option for patients through mild stimulation, and demonstrates the concept of "preventing disease" and "harmonizing yin and yang" of traditional Chinese medicine. Its needle body is thicker and the needle tip is blunt, unlike other sharp needles. Its shape is similar to modern round head needles or blunt head needles; bronze needle is mainly used for pressing and massaging acupoints, not for piercing the skin, and it stimulates meridians by pressing acupoints to regulate qi and blood, achieving the purpose of treatment. Bronze needle is commonly used to treat superficial diseases or patients sensitive to acupuncture; the treatment principle is that bronze needle produces mild stimulation by pressing acupoints to promote local blood circulation, relieve muscle tension, and regulate meridian function.

[0004] Traditional acupoint positioning method is a key technology for determining acupoint position in traditional Chinese medicine. It includes: anatomical landmark positioning method (body surface markers and active markers), bone degree and inch positioning method, finger and body inch positioning method, simple acupoint selection method, etc. There are also experience acupoint selection method, meridian positioning method, reaction point positioning method, and body surface projection method, etc. Such acupoint positioning method is too subjective and not accurate enough.

[0005] In the current medical field, there are many instruments for acupuncture treatment, which have significant defects. One of the prominent problems is that the current instruments are insufficient in exploring sensitive points and are difficult to accurately locate. The sensitive point is a special response point of the disease on the body surface, and accurate exploration of the sensitive point is crucial to improve the effect of acupuncture treatment. However, the existing instruments lack advanced detection technology and algorithms, and often rely on traditional and rough methods, resulting in a large error in determining the location of the sensitive point.

[0006] In addition, there is almost no visual treatment instrument on the market. This makes it impossible for doctors to observe the stimulation of acupoints and the real-time feedback of treatment effects during treatment. For patients, it is also difficult to clearly understand the progress of treatment. Without the aid of visualization, both the adjustment of treatment plans by doctors and the establishment of confidence in treatment by patients are greatly hindered. This not only affects the accuracy of treatment, but also reduces the efficiency of treatment and the compliance of patients to a certain extent.

[0007] Furthermore, the current treatment instrument has significant limitations in selecting acupoints. Its acupoint selection mainly relies on traditional experience, and it cannot cover all treatment needs in the face of complex and diverse diseases. The pathogenesis of different diseases is complex, and it is difficult to accurately match acupoints relying on experience. In addition, there are individual differences in human anatomical structure, and the technical level of doctors is uneven, which leads to deviation in the use of treatment instruments for acupoint positioning, affecting the treatment effect.

[0008] In view of the problems in the related art, no effective solution has been proposed so far. SUMMARY

[0009] In view of the problems in the related art, the present application proposes a sensitive acupoint exploration auxiliary system based on a needle, to overcome the above technical problems existing in the prior art.

[0010] To this end, the specific technical solutions adopted by the present application are as follows:

[0011] A sensitive acupoint exploration auxiliary system based on a needle, comprising a sensitive acupoint detection module, an auxiliary evaluation module and a human-computer interaction module;

[0012] Among them, the sensitive acupoint detection module comprises a baseline calibration module, a multi-frequency sweep impedance measurement module and a sensitive acupoint recognition module;

[0013] The baseline calibration module is used to calibrate the resistance measurement value of the acupoint to eliminate the interference of non-acupoint related factors on the measurement result;

[0014] The frequency domain impedance measurement module is used to generate a multi-frequency measurement signal of human body impedance and obtain the real and imaginary parts of impedance at each frequency point to form frequency domain impedance data;

[0015] The formula for calculating impedance is:

[0016]

[0017] In the formula, Z(f) represents the impedance value, V rms (f) represents the voltage amplitude, I rms (f) represents the current amplitude, θ(f) represents the phase difference between voltage and current, j represents the imaginary unit, and e represents the Euler number;

[0018] The sensitized acupoint identification module is used to analyze the patient's acupoint impedance data using the dual-frequency domain threshold determination method and the support vector machine algorithm respectively, to obtain a first identification result and a second identification result, and to accurately locate the sensitized acupoints based on the first identification result and the second identification result.

[0019] The auxiliary assessment module includes a needle acupuncture treatment plan determination module, a needle acupuncture data recording module, and a treatment technique assessment module;

[0020] The acupuncture treatment plan determination module is used to recommend acupuncture treatment plans for the disease from the treatment plan database based on the patient's disease symptoms. The acupuncture treatment plan includes the treatment acupoints, the direction of acupoint treatment, posture, force and time.

[0021] The needle data recording module is used to monitor in real time the magnitude, time, direction and posture data of the force applied when the needle treatment head comes into contact with the skin;

[0022] The treatment technique evaluation module is used to compare the magnitude, direction and posture data of the force when the needle treatment head comes into contact with the skin in real time with the standard data in the needle treatment plan, and evaluate the treatment technique score on a 100-point scale.

[0023] The human-computer interaction module is used to display impedance cloud diagrams, acupoint coordinates, acupuncture treatment plans, acupuncture treatment data, and treatment technique scores.

[0024] Furthermore, the baseline calibration module, when calibrating the resistance measurement values ​​of acupoints to eliminate interference from non-acupoint-related factors on the measurement results, includes:

[0025] Obtain the baseline impedance value measured in the non-acupoint area of ​​the patient, and randomly obtain the resistance value measured in any acupoint area according to the acupoint map;

[0026] Based on the baseline impedance value of non-acupoint areas and the resistance value of acupoint areas, the calibrated resistance value is determined to eliminate interference from the individual skin baseline impedance.

[0027] Furthermore, the calibrated resistance value is used to eliminate the influence of the human body surface on impedance measurement, improve the time-varying effect of human body impedance caused by the long contact time between the human body impedance measurement probe and the human body, and put the calibrated resistance value into the frequency domain impedance measurement value.

[0028] The formula for calculating the calibrated resistance value is as follows:

[0029]

[0030] In the formula, Z norm Z represents the calibrated resistance value. meas Z represents the resistance value of the acupoint area. base This represents the baseline impedance value for non-acupuncture areas.

[0031] Furthermore, the impedance change rate is used to determine the sensitization point, and the impedance change rate at each frequency point includes the measured impedance and the calibrated resistance value.

[0032] The rates of change of impedance modulus, resistance, and reactance relative to reference values ​​were calculated using the measured impedance values ​​as follows:

[0033]

[0034] In the formula, ΔZ f Represents the rate of change of frequency domain impedance, ΔR f ΔX represents the rate of change of resistance in the frequency domain. f Z represents the rate of change of frequency domain reactance. ref R represents the reference impedance value. ref X represents the reference resistance value. ref Z represents the reference reactance value. f R represents the frequency domain impedance value. f X represents the frequency domain resistance value. f This represents the frequency domain reactance value.

[0035] Furthermore, the sensitized acupoint identification module includes a sensitized region determination module, a dual-frequency domain threshold determination module, a support vector machine determination module, and a comprehensive determination module;

[0036] The sensitization area determination module is used to determine the initial area of ​​the sensitization acupoints based on the patient's symptoms and in conjunction with the acupoint map.

[0037] The dual-frequency domain threshold determination module is used to evaluate the frequency domain impedance data of each acupoint in the sensitized acupoint area obtained by measurement using a preset frequency domain impedance threshold evaluation standard, and obtain the first identification result.

[0038] The support vector machine determination module is used to identify the impedance data of each acupoint in the initial region of the sensitized acupoint using the support vector machine algorithm, and obtain a second identification result;

[0039] The comprehensive judgment module is used to accurately locate the sensitized acupoints based on the first identification result and the second identification result.

[0040] Furthermore, the dual-frequency domain threshold determination module, when evaluating the impedance data of each acupoint in the initial region of the sensitized acupoint using a preset dual-frequency domain threshold evaluation standard to obtain the first identification result, includes:

[0041] Determine whether the impedance values ​​of each acupoint in the initial area of ​​the sensitization acupoint simultaneously meet the same-body comparison standard and the population statistical standard. If so, the acupoint is determined to be a sensitization acupoint; otherwise, the acupoint is determined to be a non-sensitization acupoint.

[0042] Among them, the self-comparison standard is that the deviation of the impedance value of a certain acupoint from the average impedance value of other acupoints of the patient is greater than or equal to a preset percentage threshold.

[0043] The population statistical standard is defined as the deviation of the impedance value of a certain acupoint from the normal fluctuation range of that acupoint in healthy people.

[0044] Furthermore, the support vector machine (SVM) determination module, when using the SVM algorithm to identify the impedance data of each acupoint in the initial region of the sensitized acupoints and obtain the second identification result, includes:

[0045] Obtain the impedance values ​​of each acupoint at different frequencies and the baseline impedance values ​​of non-acupoint areas from the database, and use the baseline impedance values ​​of non-acupoint areas to calibrate the impedance values ​​of each acupoint at different frequencies.

[0046] A sample matrix is ​​constructed based on the calibrated impedance values, and the sample matrix is ​​standardized. The standardized sample matrix is ​​then used to train the support vector machine model.

[0047] Using a trained support vector machine model, the category of each acupoint in the initial region of the sensitized acupoints is predicted, and a probability score is output.

[0048] Furthermore, the comprehensive determination module, when accurately locating the sensitized acupoints based on the first identification result and the second identification result, includes:

[0049] When both the first and second identification results determine that the acupoint is a sensitized acupoint, then the acupoint is confirmed as a sensitized acupoint.

[0050] When both the first and second identification results determine that the acupoint is a non-sensitized acupoint, then the acupoint is determined to be a non-sensitized acupoint.

[0051] When the first identification result determines that the acupoint is a sensitized acupoint, the second identification result determines that it is a non-sensitized acupoint, and the probability score is less than or equal to the first threshold, then the acupoint is determined to be a non-sensitized acupoint.

[0052] When the first identification result determines that the acupoint is a non-sensitized acupoint, the second identification result determines that it is a sensitized acupoint, and the probability score is greater than or equal to the second threshold, then the acupoint is determined to be a sensitized acupoint.

[0053] Furthermore, the needle data recording module includes a treatment time recording module, a treatment force measurement module, and a needle posture measurement module;

[0054] The treatment time recording module is used to record the treatment time of the acupuncture needle.

[0055] The treatment force measurement module is used to monitor the magnitude and direction of the force when the needle treatment head comes into contact with the skin in real time using a six-axis force sensor.

[0056] The needle posture measurement module is used to monitor the posture data of the needle treatment head in real time when it comes into contact with the skin using a posture sensor.

[0057] Furthermore, the needle posture measurement module, when using a posture sensor to monitor the posture data of the needle treatment head in real time when it contacts the skin, includes:

[0058] Based on accelerometers, gyroscopes, and magnetometers, the acceleration, angular velocity, and magnetic field strength data of the acupuncture head when it comes into contact with the skin are acquired in real time.

[0059] The three-dimensional attitude angle of the needle treatment head when it comes into contact with the skin is obtained by fusing triaxial acceleration, angular velocity and magnetic field strength data using the Kalman filter algorithm and then solving the data using the quaternion transformation algorithm.

[0060] (III) Beneficial Effects

[0061] Compared with the prior art, the present invention provides a needle-based sensitized acupoint exploration auxiliary system, which has the following beneficial effects:

[0062] (1) This invention can not only accurately locate sensitized acupoints through dual-frequency domain threshold determination method and support vector machine algorithm, but also recommend acupuncture treatment plan according to the patient's disease symptoms and evaluate the effect of acupuncture treatment, thereby realizing the full-process automation of sensitized acupoint detection and auxiliary treatment, and solving the core problems of traditional methods relying on experience and lack of operational transparency.

[0063] (2) This invention can not only help medical staff to locate acupoints more accurately, thereby effectively improving the accuracy of acupoint location, ensuring treatment effect, and assisting medical staff in learning and improving acupuncture skills, but also enable medical staff to intuitively view the treatment process, making it easier to adjust the treatment plan in a timely manner. At the same time, it also enables patients to clearly understand the treatment process, enhancing their confidence and compliance with treatment. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a structural block diagram of a needle-based sensitized acupoint exploration auxiliary system according to an embodiment of the present invention;

[0066] Figure 2 This is an interface diagram of the human-computer interaction module according to an embodiment of the present invention;

[0067] Figure 3 This is a physical image of the sensitized acupoint detection module according to an embodiment of the present invention. Detailed Implementation

[0068] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0069] According to an embodiment of the present invention, a sensitized acupoint exploration auxiliary system based on acupuncture needles is provided.

[0070] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figures 1-3 As shown, the sensitized acupoint detection auxiliary system based on the scalpel needle according to an embodiment of the present invention includes a sensitized acupoint detection module, an auxiliary evaluation module, and a human-computer interaction module;

[0071] In this embodiment, the main control chip in the sensitized hole detection module is an STM32H750 microcontroller. This processor uses the ARM Cortex-M7 core, with a maximum clock speed of 480MHz, supports a double-precision floating-point unit (Double Precision FPU) and DSP instruction extensions, and has a computing power of 1024 DMIPS and 856 CoreMark.

[0072] Specifically, the sensitized hole detection module includes a baseline calibration module, a multi-frequency sweep impedance measurement module, and a sensitized hole identification module;

[0073] The baseline calibration module is used to calibrate the resistance measurement values ​​of acupoints to eliminate interference from non-acupoint-related factors on the measurement results; specifically, it includes:

[0074] Obtain the baseline impedance value measured in non-acupoint areas of the patient (such as the back of the hand) (repeat 3 times and take the average value to reduce contact error), and randomly obtain the resistance value measured in any acupoint area according to the acupoint chart (single or multiple average values).

[0075] Based on the baseline impedance value of non-acupoint areas and the resistance value of acupoint areas, the calibrated resistance value is determined to eliminate interference from individual skin baseline impedance.

[0076] By using the calibrated resistance value, the influence of the human body surface on impedance measurement is eliminated, the time-varying effect of human body impedance caused by the long contact time between the human body impedance measurement probe and the human body is improved, and the calibrated resistance value is placed into the frequency domain impedance measurement value.

[0077] The formula for calculating the calibrated resistance value is as follows:

[0078]

[0079] In the formula, Z norm Z represents the calibrated resistance value. meas Z represents the resistance value of the acupoint area. base This represents the baseline impedance value for non-acupuncture areas.

[0080] The frequency domain impedance measurement module is used to generate multi-frequency measurement signals of human body impedance (for example, outputting three sets of frequency signals of 1kHz, 10kHz and 100kHz, each frequency point lasting 100ms), and to obtain the real and imaginary components of impedance at each frequency point, search for reference impedance values ​​in the database, and form frequency domain impedance data.

[0081] Specifically, the frequency domain impedance measurement module consists of a multi-frequency signal generator and a bioimpedance measurement module; in addition, this embodiment can also use temperature compensation technology (NTC thermistor) for compensation processing during impedance measurement.

[0082] Multi-frequency signal generator: Based on DDS (Direct Digital Synthesis) technology, it outputs a frequency range of 1kHz-100kHz (step size 0.1kHz) to generate measurement signals for multi-frequency measurement methods of human body impedance. DDS technology directly generates high-precision, high-stability analog signals through digital control. Its core modules and functions are shown in Table 1 below.

[0083] Table 1. Core Modules and Functions of the Multi-Frequency Signal Generator

[0084]

[0085] The working process of a DDS signal generator:

[0086] 1) Frequency Control Word (FTW) Setting:

[0087] Formula: FTW=(f out ×2 N ) / f clk , where f out f represents the target output frequency. clk N represents the reference clock frequency (e.g., 25MHz), and N represents the phase accumulator bit width (e.g., N=28 bits in AD9833).

[0088] 2) Phase accumulation process:

[0089] At each clock cycle, the phase accumulator adds the current phase value to the FTW (Focused Transition Wave) value, generating a new phase value. Phase resolution Δφ = f clk / 2 N (e.g., 25MHz / 2) 28 (≈0.093Hz);

[0090] 3) Waveform synthesis:

[0091] The output value of the phase accumulator is used as an address to read the corresponding amplitude value from the waveform lookup table (ROM). The lookup table and the digital bit width of the DAC determine the waveform detail reproduction; for example, a 10-bit width can divide the analog signal into 4096 parts. Typical waveforms available are: sine wave (default), square wave, and triangle wave (requires a custom lookup table).

[0092] 4) Digital-to-Analog Conversion (DAC):

[0093] Convert digital amplitude values ​​into stepped analog signals;

[0094] 5) Low-pass filter (LPF):

[0095] Filter out the high-frequency image components of the DAC output, and set the cutoff frequency slightly higher than the maximum output frequency (e.g., 120kHz for 100kHz).

[0096] Bioimpedance Measurement:

[0097] Bioimpedance measurement systems calculate the impedance characteristics of biological tissues by applying current or voltage signals of a specific frequency to the tissues and measuring their response signals. Their core components are shown in Table 2, and their functions and applications are shown in Table 3.

[0098] Table 2 Core Components of a Bioimpedance Measurement System

[0099]

[0100]

[0101] Table 3 Functions and roles of the bioimpedance measurement system

[0102]

[0103] Bioimpedance characteristics: Biological tissues are composed of cell membranes, extracellular fluid (ECW), and intracellular fluid (ICW), and their impedance is characterized as follows:

[0104] Low frequency (<50kHz): Current mainly flows through the extracellular fluid, and the impedance is dominated by the ECW resistance.

[0105] High frequency (>100kHz): Current penetrates the cell membrane, and the impedance is determined by both ECW and ICW.

[0106] Complex impedance model: Impedance is a complex number, which includes a real part (resistance) and an imaginary part (reactance, inductive reactance is greater than 0, capacitive reactance is less than 0).

[0107] The working process of bioimpedance measurement:

[0108] 1) Excitation signal application: A sinusoidal current is generated using a DDS (typical amplitude: 50μA-1mA, frequency 1kHz-100kHz). Current limiting (<1mA) and isolation circuitry ensure human safety.

[0109] 2) Signal Detection: Bioimpedance measurement can employ the four-electrode method, where current is injected into the outer electrode and voltage is measured at the inner electrode, eliminating the influence of contact impedance. The measurement circuit uses a high input impedance instrumentation amplifier (such as AD8421) to reduce signal attenuation.

[0110] 3) Signal Processing: Lock-in amplification is used to extract the amplitude and phase at specific frequencies and suppress noise. A digital demodulation algorithm is employed, and impedance is calculated using FFT or digital correlation algorithms after ADC sampling.

[0111] 4) Impedance calculation: Calculate the impedance using Ohm's law;

[0112] The formula for calculating impedance is:

[0113]

[0114] In the formula, Z(f) represents the impedance value, which consists of resistance and reactance components: Z(f) = R(f) + jX(f), where X... f V represents the frequency domain reactance value. rms (f) represents the voltage amplitude, I rms (f) represents the current amplitude, θ(f) represents the phase difference between voltage and current, j represents the imaginary unit, and e represents the Euler number (approximately 2.71828).

[0115] The impedance change rate is used to determine the sensitization point. The impedance change rate at each frequency point includes the measured impedance and the calibrated resistance value. The calculation method is the same. Taking the measured impedance as an example, the change rates of impedance magnitude, resistance, and reactance relative to the reference value are calculated as follows:

[0116]

[0117]

[0118] In the formula, ΔZ f Represents the rate of change of frequency domain impedance, ΔR f ΔX represents the rate of change of resistance in the frequency domain. f Z represents the rate of change of frequency domain reactance; ref Indicates the reference impedance value; R ref Indicates the reference resistance value; X ref Indicates the reference reactance value; Z f =Z(f) = R(f) + jX(f), where Z(f) is the frequency domain impedance value; R f =R(f), which is the frequency domain resistance value; X f =X(f), which is the frequency domain reactance value.

[0119] The sensitized acupoint identification module is used to analyze the patient's acupoint impedance data using the dual-frequency domain threshold determination method and the support vector machine algorithm respectively, to obtain a first identification result and a second identification result, and to accurately locate the sensitized acupoints based on the first identification result and the second identification result.

[0120] Specifically, the sensitized acupoint identification module includes a sensitized region determination module, a dual-frequency domain threshold determination module, a support vector machine determination module, and a comprehensive determination module;

[0121] The sensitization area determination module is used to determine the initial area of ​​the sensitization acupoints based on the patient's symptoms and in conjunction with the acupoint map.

[0122] The dual-frequency domain threshold determination module is used to evaluate the frequency domain impedance data of each acupoint in the sensitized acupoint region obtained by measurement using a preset frequency domain impedance threshold evaluation standard, and to obtain a first identification result; specifically including:

[0123] Determine whether the impedance values ​​of each acupoint in the initial area of ​​the sensitization acupoint simultaneously meet the same-body comparison standard and the population statistical standard. If so, the acupoint is determined to be a sensitization acupoint; otherwise, the acupoint is determined to be a non-sensitization acupoint.

[0124] Among them, the same body comparison standard is that the deviation of the impedance value of a certain acupoint from the average impedance value of other acupoints of the patient is greater than or equal to a preset percentage threshold (in this embodiment, the preset percentage threshold is 30%).

[0125] The population statistical standard is defined as the impedance value of a certain acupoint deviating from the normal fluctuation range of that acupoint in the healthy population (i.e., the impedance value of a certain acupoint exceeds the ±2σ range of the healthy population database).

[0126] This embodiment also includes database data acquisition and storage:

[0127] Establish a disease-acupoint feature database (storage format: JSON) to record multi-dimensional data such as impedance, temperature, and pressure;

[0128] Comparison of related acupoints within the same body: The measurement data of impedance changes are compared with those of other related acupoints within the same body, and the patterns of change are stored in a relevant database;

[0129] A database of acupoints shared by different populations: data from the general population and a database of typical patients;

[0130] The support vector machine (SVM) determination module is used to identify the impedance data of each acupoint in the initial region of the sensitized acupoint using the SVM algorithm, and obtain a second identification result; specifically, it includes:

[0131] Obtain the impedance values ​​of each acupoint at different frequencies and the baseline impedance values ​​of non-acupoint areas from the database, and use the baseline impedance values ​​of non-acupoint areas to calibrate the impedance values ​​of each acupoint at different frequencies.

[0132] A sample matrix is ​​constructed based on the calibrated impedance values, and the sample matrix is ​​standardized. The standardized sample matrix is ​​then used to train the support vector machine model.

[0133] Using a trained support vector machine model, the category of each acupoint in the initial region of the sensitized acupoints is predicted, and a probability score is output.

[0134] The comprehensive judgment module is used to accurately locate the sensitized acupoints based on the first identification result and the second identification result; specifically, it includes:

[0135] When both the first and second identification results determine that the acupoint is a sensitized acupoint, then the acupoint is confirmed as a sensitized acupoint.

[0136] When both the first and second identification results determine that the acupoint is a non-sensitized acupoint, then the acupoint is determined to be a non-sensitized acupoint.

[0137] When the first identification result determines that the acupoint is a sensitized acupoint, the second identification result determines that it is a non-sensitized acupoint, and the probability score is less than or equal to the first threshold, then the acupoint is determined to be a non-sensitized acupoint.

[0138] When the first identification result determines that the acupoint is a non-sensitized acupoint, the second identification result determines that it is a sensitized acupoint, and the probability score is greater than or equal to the second threshold, then the acupoint is determined to be a sensitized acupoint.

[0139] Based on the above technical solution, the present invention has the following effects:

[0140] 1) Fusion of multimodal sensing technologies

[0141] Employing a multi-frequency signal generator (based on DDS technology) and a bioimpedance measurement chip (AD5941), multi-frequency sweep measurement of human body impedance (1kHz-100kHz) is achieved. Combined with multi-dimensional data such as resistance, temperature, and pressure, the accuracy and reliability of sensitized acupoint detection are significantly improved. A six-axis force sensor monitors the three-dimensional force (Fx, Fy, Fz) and torque (Mx, My, Mz) in real time when the treatment head contacts the skin. Combined with an inertial measurement unit (accelerometer, gyroscope, magnetometer), the force application posture is dynamically fed back to ensure standardized treatment techniques.

[0142] 2) Precise location of sensitization points

[0143] This invention can not only accurately locate sensitized acupoints through dual-frequency domain threshold determination method and support vector machine algorithm, but also recommend acupuncture treatment plan according to the patient's disease symptoms and evaluate the effect of acupuncture treatment. Thus, it realizes full automation of the sensitized acupoint detection and auxiliary treatment process, and solves the core problems of traditional methods that rely on experience and lack of operational transparency.

[0144] The auxiliary assessment module includes a needle acupuncture treatment plan determination module, a needle acupuncture data recording module, and a treatment technique assessment module;

[0145] The acupuncture treatment plan determination module is used to recommend acupuncture treatment plans for the disease from the treatment plan database based on the patient's disease symptoms. The acupuncture treatment plan includes parameters such as treatment acupoints, acupoint treatment direction, posture, force and time, and is displayed on the LCD screen.

[0146] In this embodiment, a disease-acupoint feature database is integrated, and clinical data and disease mechanisms are analyzed through artificial intelligence to automatically match the optimal acupoint treatment plan. Subsequently, medical staff can manually use specific techniques to treat selected acupoints, and the data such as needle posture and force during the treatment process are automatically recorded and displayed on the screen. In addition, this embodiment can also intelligently adjust electrical pulse parameters (waveform, frequency, voltage) and implement treatment technique evaluation algorithms.

[0147] The needle data recording module is used to monitor in real time the magnitude, time, direction and posture data of the force applied when the needle treatment head comes into contact with the skin;

[0148] Specifically, the needle data recording module includes a treatment time recording module, a treatment force measurement module, and a needle posture measurement module;

[0149] The treatment time recording module is used to record the treatment time of the acupuncture needle.

[0150] The treatment force measurement module is used to monitor the magnitude and direction of the force applied when the needle treatment head contacts the skin in real time using a six-axis force sensor. In this embodiment, the treatment force measurement module consists of a six-axis force sensor, a signal conditioning circuit, and an A / D converter.

[0151] Specifically, in the acupuncture treatment device, the core function of the six-axis force sensor is to monitor the magnitude and direction of the force applied when the treatment head contacts the skin in real time, providing mechanical feedback data for closed-loop control. It can be used to record the treatment process, store three-dimensional mechanical data (Fxyz), and generate treatment reports (such as pressure-time curves and force uniformity analysis).

[0152] The six-axis force sensor is configured as follows:

[0153] 1) Elastomer structure:

[0154] The elastomer is the core mechanical component of the sensor, converting external forces / torques into local strain. This device employs a crossbeam structure, consisting of four orthogonal beams (high sensitivity, low cross-interference). Stress concentration zones are designed using finite element analysis (FEA) to improve sensitivity.

[0155] 2) Strain gauge layout:

[0156] Full-bridge configuration: Each axis uses 4 strain gauges to form a Wheatstone bridge. A typical layout is shown in Table 4 below:

[0157] Table 4 Typical Layout Schemes

[0158] Measurement axis Strain gauge position Fx Horizontal beam upper and lower surface longitudinal patches Fy Vertical beam left and right surface longitudinal patches Fz Four beam root transverse patches (shear strain)

[0159] 3) The signal chain hardware is shown in Table 5 below:

[0160] Table 5 Signal Chain Hardware

[0161] Module Function Typical device Wheatstone bridge Strain to voltage conversion 350 Ω strain gauge (sensitivity factor 2.0) Instrumentation amplifier Amplify weak differential signal AD8421 (gain 1000, bandwidth 1 MHz) Low pass filter Suppress high frequency noise Second order active filter (cutoff frequency 1 kHz) ADC Analog to digital conversion ADS1256 (24 bit, 30 kSPS) Temperature sensor Real time temperature drift compensation PT1000 (accuracy ±0.1 °C)

[0162] 4) Working principle (learning - electrical signal conversion):

[0163] When forces Fx, Fy, and Fz are applied, the elastic body deforms, and the resistance wire of the strain gauge deforms under the force, thus changing its resistance. The Wheatstone bridge converts the resistance change of the strain gauge into an output voltage, which is then amplified by an instrumentation amplifier. The analog voltage is converted into a digital value by an ADC, and the MCU performs measurement calculations and temperature compensation. Since the temperature coefficient of resistance of the strain gauge and the thermal expansion of the elastic body both change with temperature, temperature compensation is necessary. The output signal can be corrected based on the temperature data of the strain gauge material. The torque (Mx, My) data is used to determine whether the needle is tilted or deviated. When the torque exceeds the threshold (e.g., Mx > 0.1 Nm), the system issues an abnormal attitude alarm.

[0164] The needle posture measurement module is used to monitor the posture data of the needle treatment head in real time when it comes into contact with the skin using a posture sensor;

[0165] This embodiment employs the BNO055 integrated attitude sensor with a built-in MCU, enabling high-precision attitude measurement in complex environments. It features a built-in attitude fusion algorithm that directly outputs Euler angles / quaternions. Combined with an accelerometer, gyroscope, and magnetometer, the sensor fusion algorithm (such as Kalman filtering) calculates the three-dimensional attitude angles (pitch, roll, yaw).

[0166] Accelerometer: Measures the direction of gravity (static) and acceleration of motion (dynamic), used to calculate pitch and roll angles;

[0167] Gyroscope: measures angular velocity and calculates angle change through integration, but drifts over time;

[0168] Magnetometer: measures the direction of the Earth's magnetic field and provides an absolute heading angle, but is susceptible to ferromagnetic interference;

[0169] Specifically, the use of posture sensors to monitor the posture data of the acupuncture head in real time when it comes into contact with the skin includes:

[0170] Based on accelerometers, gyroscopes, and magnetometers, the acceleration, angular velocity, and magnetic field strength data of the acupuncture head when it comes into contact with the skin are acquired in real time.

[0171] The three-dimensional attitude angle of the needle treatment head when it comes into contact with the skin is obtained by fusing triaxial acceleration, angular velocity and magnetic field strength data using the Kalman filter algorithm and then solving the data using the quaternion transformation algorithm.

[0172] The treatment technique evaluation module is used to compare the magnitude, direction, and posture data of the force applied when the needle treatment head contacts the skin in real time with the standard data in the needle treatment plan, and evaluate the treatment technique on a 100-point scale. The treatment technique is compared with the database standard to generate a 100-point evaluation report to assist doctors in optimizing their operations.

[0173] The human-computer interaction module is used to display impedance cloud diagrams, acupoint coordinates, acupuncture treatment plans, acupuncture treatment data, and treatment technique scores.

[0174] In this embodiment, the human-computer interaction module uses a 7-inch TFT touchscreen (1024×600 resolution); and the user interface supports switching between Chinese and English. The high-definition touchscreen displays data such as impedance cloud diagrams, acupoint coordinates, treatment pressure-time curves, and force application posture in real time, and can provide abnormal alarms.

[0175] In addition, this embodiment also includes a communication interface (with a built-in wireless Bluetooth communication module, which can realize instruction transmission and data exchange with smart devices such as computers and mobile phones) and a memory (using electrically erasable memory to store database data).

[0176] Furthermore, the multi-frequency signal generator (AD9834 chip) based on DDS (Direct Digital Synthesis) technology currently used in this embodiment can be replaced with other chips based on PLL (Phase-Locked Loop) or direct frequency synthesis (such as AD9850), or a programmable logic device (FPGA) can be used to implement multi-frequency signal generation. Advantages: Reduced cost or increased frequency range (e.g., extended to 200kHz).

[0177] The AD5941 bioimpedance measurement chip can be replaced with other bioimpedance chips that support four-wire measurements (such as TI's AFE4300 series), or impedance measurement circuits can be built using discrete components (such as precision operational amplifiers and ADC modules). Advantages: Flexible adaptation to different accuracy requirements or cost-constrained scenarios.

[0178] The six-axis force sensor can be replaced with a piezoelectric or capacitive sensor, and the three-dimensional force and torque are indirectly derived by combining it with multi-axis mechanical analysis algorithms. Advantages: Suitable for applications sensitive to size or cost.

[0179] In addition to electrical impedance, this embodiment can also use optical sensing (such as near-infrared spectroscopy) to detect changes in blood flow at acupoints, or thermal imaging technology to capture local temperature anomalies, as auxiliary criteria for determining sensitized acupoints. Advantage: Multi-dimensional data fusion improves the reliability of the determination.

[0180] Inertial measurement units (IMUs) can be simplified to using only accelerometers and gyroscopes (removing magnetometers), or combined with visual sensors (such as miniature cameras) to track needle posture through image recognition technology. Advantages: Reduced hardware complexity or enhanced adaptability to dynamic environments.

[0181] In addition to NTC thermistors, digital temperature sensors (such as the DS18B20) or software compensation algorithms based on ambient temperature prediction can be used. Advantages: Improved compensation accuracy or simplified hardware design.

[0182] After sensitizing acupoints are located, they can be replaced with laser acupuncture (low-intensity laser stimulation), electroacupuncture (TENS device), or ultrasound therapy to achieve acupoint stimulation in a non-invasive manner. Advantages: Expands the applicability of treatment methods and meets the needs of different patients.

[0183] The sine wave can be replaced with a square wave, triangle wave, or modulated waveform (such as pulse width modulation PWM), and the frequency generation method can use techniques other than DDS (such as RC oscillation circuits). Advantages: Exploring the optimization of the effects of different waveforms on acupoint stimulation.

[0184] Embedded touchscreens can be replaced with external mobile terminals (such as tablets and smartphones) to achieve data visualization and operation control via an app. Advantages: Reduced hardware costs and increased user interaction flexibility.

[0185] In summary, by utilizing the above-mentioned technical solutions of this invention, this invention can not only accurately locate sensitized acupoints through the dual-frequency domain threshold determination method and support vector machine algorithm, but also recommend acupuncture treatment plans based on the patient's disease symptoms and evaluate the acupuncture treatment effect, thereby realizing the full automation of the sensitized acupoint detection and auxiliary treatment process, and solving the core problems of traditional methods relying on experience and lacking operational transparency.

[0186] In addition, this invention can not only assist medical staff in locating acupoints more accurately, thereby effectively improving the accuracy of acupoint location and ensuring treatment effects, and assisting medical staff in learning and improving their acupuncture skills, but also allows medical staff to intuitively view the treatment process, making it easier to adjust the treatment plan in a timely manner. At the same time, it also allows patients to clearly understand the treatment process, enhancing their confidence and compliance with treatment.

[0187] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A sensitized acupoint exploration auxiliary system based on acupuncture needles, characterized in that, It includes a sensitized acupoint detection module, an auxiliary evaluation module, and a human-computer interaction module; The sensitized acupoint detection module includes: The baseline calibration module is used to calibrate the resistance measurement values ​​of acupoints to eliminate the interference of non-acupoint-related factors on the measurement results; The frequency domain impedance measurement module is used to generate multi-frequency measurement signals of human body impedance and acquire the real and imaginary components of impedance at each frequency point to form frequency domain impedance data. The formula for calculating impedance is: ； In the formula, Z(f) represents the impedance value, and V rms (f) represents the voltage amplitude, I rms (f) represents the current amplitude, θ(f) represents the phase difference between voltage and current, j represents the imaginary unit, and e represents the Euler number; The sensitized acupoint identification module is used to analyze the patient's acupoint impedance data using the dual-frequency domain threshold determination method and the support vector machine algorithm respectively, to obtain the first identification result and the second identification result, and to accurately locate the sensitized acupoints based on the first identification result and the second identification result. The auxiliary assessment module includes: The acupuncture treatment plan determination module is used to recommend acupuncture treatment plans for the disease based on the patient's disease symptoms from the treatment plan database. The acupuncture treatment plan includes the treatment acupoints, the direction of acupoint treatment, posture, force and time. The needle data recording module is used to monitor the magnitude, time, direction, and posture data of the force applied when the needle treatment head comes into contact with the skin in real time. The treatment technique assessment module is used to compare the magnitude, direction, and posture data of the force applied when the needle treatment head comes into contact with the skin in real time with the standard data in the needle treatment plan, and to evaluate the treatment technique on a 100-point scale. The sensitized acupoint identification module includes a sensitized region determination module, a dual-frequency domain threshold determination module, a support vector machine determination module, and a comprehensive determination module. The sensitization area determination module is used to determine the initial area of ​​the sensitization acupoints based on the patient's symptoms and in conjunction with the acupoint map. The dual-frequency domain threshold determination module is used to evaluate the frequency domain impedance data of each acupoint in the sensitized acupoint area obtained by measurement using a preset frequency domain impedance threshold evaluation standard, and obtain the first identification result. The support vector machine determination module is used to identify the impedance data of each acupoint in the initial region of the sensitized acupoint using the support vector machine algorithm, and obtain a second identification result; The comprehensive judgment module is used to accurately locate the sensitized acupoints based on the first identification result and the second identification result; The comprehensive judgment module, when accurately locating the sensitized acupoints based on the first and second identification results, includes: When both the first and second identification results determine that the acupoint is a sensitized acupoint, then the acupoint is confirmed as a sensitized acupoint. When both the first and second identification results determine that the acupoint is a non-sensitized acupoint, then the acupoint is determined to be a non-sensitized acupoint. When the first identification result determines that the acupoint is a sensitized acupoint, the second identification result determines that it is a non-sensitized acupoint, and the probability score is less than or equal to the first threshold, then the acupoint is determined to be a non-sensitized acupoint. When the first identification result determines that the acupoint is a non-sensitized acupoint, the second identification result determines that it is a sensitized acupoint, and the probability score is greater than or equal to the second threshold, then the acupoint is determined to be a sensitized acupoint.

2. The sensitized acupoint exploration auxiliary system based on acupuncture needle according to claim 1, characterized in that, The baseline calibration module, when calibrating the resistance measurement values ​​of acupoints to eliminate interference from non-acupoint-related factors on the measurement results, includes: Obtain the baseline impedance value measured in the non-acupoint area of ​​the patient, and randomly obtain the resistance value measured in any acupoint area according to the acupoint map; Based on the baseline impedance value of non-acupoint areas and the resistance value of acupoint areas, the calibrated resistance value is determined to eliminate interference from the individual skin baseline impedance.

3. The sensitized acupoint exploration auxiliary system based on acupuncture needle according to claim 2, characterized in that, The calibrated resistance value is used to eliminate the influence of the human body surface on impedance measurement, improve the time-varying effect of human body impedance caused by long contact time between the human body impedance measurement probe and the human body, and put the calibrated resistance value into the frequency domain impedance measurement value. The formula for calculating the calibrated resistance value is as follows: ； In the formula, Z norm Z represents the calibrated resistance value. meas Z represents the resistance value of the acupoint area. base This represents the baseline impedance value for non-acupuncture areas.

4. The sensitized acupoint exploration auxiliary system based on acupuncture needle according to claim 1, characterized in that, The impedance change rate is used to determine the sensitization point, and the impedance change rate at each frequency point includes the measured impedance and the calibrated resistance value. The rates of change of impedance modulus, resistance, and reactance relative to reference values ​​were calculated using the measured impedance values ​​as follows: ； ； ； In the formula, Represents the rate of change of frequency domain impedance, ΔR f ΔX represents the rate of change of resistance in the frequency domain. f Z represents the rate of change of frequency domain reactance. ref R represents the reference impedance value. ref X represents the reference resistance value. ref Z represents the reference reactance value. f R represents the frequency domain impedance value. f X represents the frequency domain resistance value. f This represents the frequency domain reactance value.

5. The sensitized acupoint exploration auxiliary system based on acupuncture needle according to claim 1, characterized in that, The dual-frequency domain threshold determination module, when evaluating the impedance data of each acupoint in the initial region of the sensitized acupoint using a preset dual-frequency domain threshold evaluation standard to obtain the first identification result, includes: Determine whether the impedance values ​​of each acupoint in the initial area of ​​the sensitization acupoint simultaneously meet the same-body comparison standard and the population statistical standard. If so, the acupoint is determined to be a sensitization acupoint; otherwise, the acupoint is determined to be a non-sensitization acupoint. Among them, the self-comparison standard is that the deviation of the impedance value of a certain acupoint from the average impedance value of other acupoints of the patient is greater than or equal to a preset percentage threshold. The population statistical standard is defined as the deviation of the impedance value of a certain acupoint from the normal fluctuation range of that acupoint in healthy people.

6. The sensitized acupoint exploration auxiliary system based on acupuncture needle according to claim 1, characterized in that, The support vector machine (SVM) determination module, when using the SVM algorithm to identify the impedance data of each acupoint in the initial region of the sensitized acupoints and obtain the second identification result, includes: Obtain the impedance values ​​of each acupoint at different frequencies and the baseline impedance values ​​of non-acupoint areas from the database, and use the baseline impedance values ​​of non-acupoint areas to calibrate the impedance values ​​of each acupoint at different frequencies. A sample matrix is ​​constructed based on the calibrated impedance values, and the sample matrix is ​​standardized. The standardized sample matrix is ​​then used to train the support vector machine model. Using a trained support vector machine model, the category of each acupoint in the initial region of the sensitized acupoints is predicted, and a probability score is output.

7. The sensitized acupoint exploration auxiliary system based on acupuncture needle according to claim 1, characterized in that, The needle data recording module includes a treatment time recording module, a treatment force measurement module, and a needle posture measurement module; The treatment time recording module is used to record the treatment time of the acupuncture needle. The treatment force measurement module is used to monitor the magnitude and direction of the force when the needle treatment head comes into contact with the skin in real time using a six-axis force sensor. The needle posture measurement module is used to monitor the posture data of the needle treatment head in real time when it comes into contact with the skin using a posture sensor.

8. The sensitized acupoint exploration auxiliary system based on acupuncture needle according to claim 7, characterized in that, The needle posture measurement module, when using a posture sensor to monitor the posture data of the needle treatment head in contact with the skin in real time, includes: Based on accelerometers, gyroscopes, and magnetometers, the acceleration, angular velocity, and magnetic field strength data of the acupuncture head when it comes into contact with the skin are acquired in real time. The three-dimensional attitude angle of the needle treatment head when it comes into contact with the skin is obtained by fusing triaxial acceleration, angular velocity and magnetic field strength data using the Kalman filter algorithm and then solving the data using the quaternion transformation algorithm.

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