Standardized pulse diagnosis method and pulse diagnosis platform based on wearable pulse instrument

Abstract

The invention provides a standardized pulse diagnosis method based on a wearable pulse instrument, which comprises the following steps: initializing, pressurizing for multiple times from low to high to obtain wrist pulse pressure data, fitting a curve, determining a reference wave, identifying a trough, calculating a single average wave, evaluating a pulse position, a pulse number, a pulse shape and a pulse potential in multiple dimensions, and the like. A corresponding pulse condition processing method is combined, the principle that traditional Chinese medicine finger pressing pulse taking feels pulse wave changes is simulated, a wrist strap air bag is adopted for pressurization to simulate manual pressing pressure, a sensor monitors and collects pulse change data after pressurization, and after an AI algorithm completes conversion of an electric signal and a pressure value, a human body pulse condition change curve is generated in a fitting mode. Therefore, subjective judgment of manual pulse taking is replaced. The accuracy of manual pulse taking is influenced by the mood of a pulse taking person and external environment factors, and the sensor can continuously and objectively monitor the pulse change of the patient, so that more objective pulse taking data is obtained.

Description

Technical Field

[0001] The invention relates to the technical field of traditional Chinese medicine pulse diagnosis, in particular to a standardized pulse diagnosis method based on a wearable pulse monitor. Background Art

[0002] Pulse diagnosis has the advantages of being non-invasive, simple, fast, and able to comprehensively reflect the overall functional state, and plays an irreplaceable role in the TCM syndrome differentiation and treatment system. However, pulse diagnosis is difficult to learn, master, and popularize, so the modernization of TCM pulse diagnosis research, the realization of data, visualization, and standardization are of great significance to the development of TCM.

[0003] Objectification of pulse diagnosis: Pulse diagnosis is one of the unique diagnostic methods of traditional Chinese medicine. It mainly uses the feeling of fingers to analyze the characteristics of pulse wave such as "position, number, shape, and potential" to judge the functional state of internal organs, thereby achieving the purpose of non-invasive diagnosis, which has positive significance for the diagnosis and treatment of diseases. However, pulse diagnosis of traditional Chinese medicine has a certain degree of subjectivity, and it is quite difficult to master and use it accurately. It is said that "it is easy to understand in the mind, but difficult to understand under the fingers". This project can improve the objectivity of pulse diagnosis of traditional Chinese medicine.

[0004] Visualization of pulse diagnosis: Traditional Chinese medicine pulse diagnosis is a text description, which is relatively abstract. Clinical pulse diagnosis depends entirely on personal experience and understanding. Although modern pulse diagnosis uses images, they are mostly schematic diagrams, pattern diagrams, and model diagrams, not real pulse diagrams, and are also very different in nature from measured pulse diagrams. This project will realize the visualization of Chinese medicine pulse diagnosis, changing the current situation that Chinese medicine pulse diagnosis is not suitable for promotion and difficult to learn.

[0005] Standardization of pulse diagnosis: Traditional pulse diagnosis standards are not unified. With the advancement of science and technology, the research on pulse diagnosis instruments and the emergence of artificial intelligence have made pulse diagnosis standardization possible. Many units are still keen on researching pulse instruments in the laboratory, but rarely consider clinical application issues during research and development, resulting in a disconnect between research results and clinical practice. The pulse instruments developed are either complicated to operate, inconvenient to use, or expensive and difficult for clinical units to adopt. There is a lack of professional guidance on how to analyze pulse examination results. Most Chinese medicine practitioners do not understand the meaning of pulse charts, so it is difficult to popularize and promote them in clinical practice. The most important thing is that most of the collected pulse waves are not analyzed, there are images but no analysis, or the methods are different, each doing its own thing, and there is no reverse verification, making it difficult to form the standardization of machine pulse diagnosis. This project is expected to achieve the standardization of pulse diagnosis.

[0006] In order to achieve the standardization and objectivity of Chinese medicine pulse diagnosis, the applicant invented a standardized pulse diagnosis method based on a wearable pulse meter. The wearable pulse meter collects human pulses, and in combination with the corresponding pulse processing method, the wristband airbag is pressurized to simulate the artificial pressing pressure by simulating the principle of feeling the pulse wave changes by pressing the pulse with fingers in Chinese medicine. The sensor monitors and collects the pulse change data after pressurization. After the AI ​​algorithm completes the conversion of electrical signals and pressure values, it fits and generates the human pulse change curve, thereby replacing the subjective judgment of manual pulse diagnosis. The accuracy of manual pulse diagnosis is affected by the mood of the person taking the pulse and external environmental factors, while the sensor can continuously and objectively monitor the patient's pulse changes, thereby obtaining more objective pulse diagnosis data. Summary of the invention

[0007] The invention proposes a standardized pulse diagnosis method based on a wearable pulse monitor, which solves the objectivity and standardization problems of traditional Chinese medicine pulse diagnosis existing in the prior art.

[0008] The technical solution of the present invention is achieved in this way: a standardized pulse diagnosis method based on a wearable pulse monitor, using the wearable pulse monitor to collect pulse, comprising the following steps:

[0009] Step S1: Initialize the patient's height h and weight g, and calculate the standard pressure P0

[0010] Step S2: applying pressure in sequence according to the following pulse diagnosis techniques under standard pressure: floating pressure, middle pressure, sinking pressure, and pressure pressing on the bone, and each step is T seconds, and m pressure data are read per second. After removing the data of T0 seconds before each step, fitting is performed, and the fitting curves are marked as ②: floating fitting curve; ③: middle pressure fitting curve; ④: sinking fitting curve; ⑤: pressure pressing on the bone fitting curve;

[0011] Step S3: Determine the reference wave and calculate the average wavelength; calculate the minimum convex hull area Ca of each fitting curve, and take the fitting curve with the largest minimum convex hull area as the reference wave; filter, set the wavelength or wave height that is too small as a pseudo wave, and ignore the trough of the pseudo wave and the adjacent smaller peak mark (or merge it into an adjacent wave with a smaller wavelength). Recalculate the average wavelength as aL.

[0012] Step S4: trough identification;

[0013] The point where the slope changes before and after in the fitting curve is defined as the pole, the point with the largest difference in the longitudinal axis between adjacent concave and convex poles is the rising segment, and the starting point of the rising segment is the calibration trough point. Taking aL as the scale, after the calibration trough point, find the rising segment with the starting point in the range of [aL×constant F] to [aL×(1+constant F^2)] on the X-axis. If the rising segment cannot be found within this range, modify the range to [aL×(1+constant F)] to [aL×(2+constant F^2)]. If the rising segment is still not found, feedback is given as an invalid spectrum, and the starting point of this rising segment is used as the next calibration point. Similarly, the starting points of all rising segments after the calibration trough point are determined. Similarly, before calibrating the trough point, find a rising segment with a starting point in the range of [-aL×constant F] to [-aL×(1+constant F^2)] on the X axis. If the rising segment cannot be found within this range, modify the range to [-aL×(1+constant F)] to [-aL×(2+constant F^2)]. If the rising segment is still not found, feedback is given as an invalid spectrum, and the starting point of this rising segment is used as the next calibration point. Similarly, determine the starting points of all rising segments before the calibration trough point. The lowest concave pole within the range of [-aL×constant F^3~0] before and after the starting point of each rising segment is the front trough. The highest point between the front and rear troughs is the peak.

[0014] Step S5: Calculate the single mean wave;

[0015] Calculate the single mean wave: divide each single wave x-axis into N equal parts to obtain x0~x N There are N+1 equally divided points corresponding to y0~y N , y0~y N Take the average values ​​respectively and synthesize these N+1 average values ​​into a single wave; the wave area is the area of ​​the figure enclosed by the lines connecting the pre-peak valley and the post-peak valley and the fitting curve; the wave circumference is the sum of the arc length and chord length of the figure enclosed by the lines connecting the pre-peak valley and the post-peak valley and the fitting curve / arc.

[0016] Step S6: According to the waveform diagram obtained in the above steps, the pulse position, pulse rate, pulse shape and pulse strength are calculated according to individual dimensions.

[0017] As a preferred technical solution, the standard pressure P0 calculation formula is as follows:

[0018] P0=90×(BMI / 23)^0.5

[0019] Wherein, BMI is body mass index, and BMI=g / h2, where height h is in meters and weight g is in kilograms. P0×(constant G)^0 (floating pressure); P0×(constant G)^1 (medium pressure); P0×(constant G)^2 (sunk pressure); P0×(constant G)^3 (bone pressure).

[0020] As a preferred technical solution, in step S6, the pulse position, pulse rate, pulse shape and pulse strength are calculated according to 10 dimensions: depth, length, frequency, rhythm, width, tension, fullness, fluency, strength and slope.

[0021] As a preferred technical solution, the pulse position is calculated from two dimensions: depth and length. The single average wave height is compared between the second fitting curve, the third fitting curve, the fourth fitting curve and the fifth fitting curve of this single average wave to divide it into shallow pulse position, deep pulse position and very deep pulse position; the ratio of the wave area above the wave waist to the wave area below the wave waist of the reference wave of this single average wave is compared with the system average value to divide it into short pulse position and long pulse position.

[0022] As a preferred technical solution, the pulse number is calculated from two dimensions: frequency and rhythm. The reference wave of this single average wave is compared with the system average value, and the 15-second peak count divides the pulse frequency into slow, slightly slow, fast and extremely fast; the wave height or wavelength is compared within the reference wave curve to divide the rhythm into irregular and paused.

[0023] As a preferred technical solution, the pulse shape is calculated from three dimensions: width, tension and fullness. The width is divided into wide and thin according to the ratio of the single average wave area to the 15-second peak count when the single average wave reference wave is compared with the system average value; the tension is divided into tight and loose according to the y-axis value of the peak when the single average wave reference wave is compared with the system average value; the fullness is divided into open and empty according to the y-axis value of the trough when multiple single average waves are compared.

[0024] As a preferred technical solution, the pulse is calculated from three dimensions: fluency, strength and slope. The single average wave reference wave is compared with the system average value to divide the fluency into smooth and difficult; the single average wave reference wave is compared with the system average value, and the ratio of the single average wave area to the single average wave circumference is used to divide the strength into weak and strong; the ratio of the peak x-axis value to the wavelength is compared within the single average wave reference wave to divide the slope into gentle and rapid.

[0025] A standardized pulse diagnosis platform based on a wearable pulse monitor is used to execute the above-mentioned standardized pulse diagnosis method based on a wearable pulse monitor.

[0026] Compared with the prior art, this solution has the following beneficial effects:

[0027] A standardized pulse diagnosis method based on a wearable pulse monitor is provided. The human pulse is collected through the wearable pulse monitor. In combination with the corresponding pulse processing method, the pulse wave change principle of feeling the pulse wave by pressing the pulse with fingers in traditional Chinese medicine is simulated. The wristband airbag is pressurized to simulate the pressure of manual pressing. The sensor monitors and collects the pulse change data after pressurization. After the AI ​​algorithm completes the conversion of electrical signals and pressure values, the human pulse change curve is fitted to replace the subjective judgment of manual pulse diagnosis. The accuracy of manual pulse diagnosis is affected by the mood of the person taking the pulse and external environmental factors, while the sensor can continuously and objectively monitor the patient's pulse changes, thereby obtaining more objective pulse diagnosis data. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 A method flow chart of a standardized pulse diagnosis method based on a wearable pulse monitor of the present invention;

[0030] Figure 2 The invention discloses a method for calculating pulse position, pulse rate, pulse shape and pulse strength of a standardized pulse diagnosis method based on a wearable pulse imager;

[0031] Figure 3 This is a first interface screenshot of the standardized pulse diagnosis platform based on the wearable pulse monitor of the present invention;

[0032] Figure 4 This is a second interface screenshot of the standardized pulse diagnosis platform based on the wearable pulse monitor of the present invention;

[0033] Figure 5 This is a screenshot of the third interface of the standardized pulse diagnosis platform based on the wearable pulse monitor of the present invention;

[0034] Figure 6 This is a screenshot of the fourth interface of the standardized pulse diagnosis platform based on the wearable pulse monitor of the present invention;

[0035] Figure 7 This is a screenshot of the fifth interface of the standardized pulse diagnosis platform based on the wearable pulse monitor of the present invention. DETAILED DESCRIPTION

[0036] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0037] Referring to Figure 1 , this embodiment provides a standardized pulse diagnosis method based on a wearable pulse meter, which specifically includes the following steps:

[0038] Step 1: Initialize height (m) and weight (Kg), and calculate the standard pressure = 90×(BMI / 23)^0.5, where BMI (body mass index) = weight / height^2;

[0039] Step 2: Apply pressure in sequence according to floating extraction at the pressure of [standard pressure], middle extraction at the pressure of [standard pressure]×(constant G)^1, deep extraction at the pressure of [standard pressure×(constant G)^2], and heavy extraction pressing on the bone at the pressure of [standard pressure×(constant G)^3]. Read 25 pressure data per second for 20 seconds for each step, and剔除 the data of the first 5 seconds of each step. Fit the curves and mark them as curve ②, curve ③, curve ④, and curve ⑤ in sequence. If any one of ②③④⑤ is missing, end the calculation and feedback as an invalid spectrum.

[0040] Step 3, reference wave: Calculate the minimum convex hull area Ca of the fitted curve, If (highest point - lowest point) / (Ca / 15 seconds) > (constant G)^6 for any one of ②③④⑤, end the calculation and feedback as an invalid spectrum. Filtering: If the initially calculated average wavelength is pL and the average wave height is pH, set the wavelengths < pL×constant F ∪ wave heights < pH×constant F as pseudo-waves, and mark and ignore the wave valleys of the pseudo-waves and an adjacent smaller wave peak (or merge them into an adjacent wave with a smaller wavelength). Recalculate the average wavelength as aL. The X-axis projection of the previous wave valley is B, the X-axis projection of the subsequent wave valley is C, and the X-axis projection of the previous wave valley of the previous wave is A. If the pseudo-wave is the first wave, remove the previous wave valley; if it is the last wave, remove the subsequent wave valley. If |(B - A) - pL| < |(C - A) - pL|, remove the subsequent wave valley; if |(B - A) - pL| > |(C - A) - pL|, remove the previous wave valley; if |(B - A) - pL| = |(C - A) - pL|, remove the wave valley with a larger y value.

[0041] Step 4, Trough Identification: There are some extreme points on the curve (i.e., points where the slope changes by ± before and after). For the rising segment where the Δy between adjacent concave and convex extreme points is Δy = max ∩ Δy > pH × constant F^3, compare the starting point of the rising segment with the second extreme point before the starting point (the first extreme point is convex), and the lower one is the calibrated trough point. Using the recalculated average wavelength aL as the scale, find the rising segment whose starting point is within the range of [aL × constant F] to [aL × (1 + constant F^2)] on the X-axis after the calibrated trough point. If no rising segment is found within this range, modify the range to [aL × (1 + constant F)] to [aL × (2 + constant F^2)]. If still no rising segment is found, feedback as an invalid spectrum, and then use the starting point of this rising segment as the next calibration point, and so on to determine the starting points of all rising segments after the calibrated trough point. Similarly, find the rising segment whose starting point is within the range of [-aL × constant F] to [-aL × (1 + constant F^2)] on the X-axis before the calibrated trough point. If no rising segment is found within this range, modify the range to [-aL × (1 + constant F)] to [-aL × (2 + constant F^2)]. If still no rising segment is found, feedback as an invalid spectrum, and then use the starting point of this rising segment as the next calibration point, and so on to determine the starting points of all rising segments before the calibrated trough point. If the Δy of the first rising segment < pH × constant F, the corresponding trough point needs to be excluded. The lowest concave extreme point within the range of [-aL × constant F^3 to 0] before and after the starting point of each rising segment is the front trough. The highest point between the front and rear troughs is the peak.

[0042] Step 5, Calculate the single average wave: Divide the x-axis of each single wave into 24 equal parts, obtain y0 to y24 corresponding to 25 equally divided points from x0 to x24, take the average values of y0 to y24 respectively, and combine these 25 average values into a single wave ②③④⑤; Wave area: The area of the figure enclosed by the connection lines of the valley before the peak and the valley after the peak and the fitting curve; Wave perimeter: The perimeter of the figure enclosed by the connection line (chord) of the valley before the peak and the valley after the peak and the fitting curve (arc) (arc length + chord length).

[0043] Step 6, Calculate the pulse position, pulse rate, pulse shape, and pulse potential according to 10 dimensions including depth, length, frequency, rhythm, width, tension, fullness, fluency, strength, and slope.

[0044] Among them, the specific calculation method in Step 6 refers to Figure 2 : Specifically, the pulse position is calculated from two dimensions of depth and length. By comparing the single average wave heights among the second fitting curve, the third fitting curve, the fourth fitting curve, and the fifth fitting curve of this single average wave, it is divided into shallow pulse position, deep pulse position, and extremely deep pulse position; By comparing the ratio of the part above the wave waist to the part below the wave waist of the wave area between the reference wave of this single average wave and the system average value, it is divided into short pulse position and long pulse position.

[0045] The pulse rate is calculated from two dimensions: frequency and rhythm. The reference wave of this single average wave is compared with the system average value, and the 15-second peak count divides the pulse frequency into slow, slightly slow, fast and extremely fast. The wave height or wavelength within the reference wave curve is compared to divide the rhythm into irregular and paused.

[0046] The pulse shape is calculated from three dimensions: width, tension and fullness. The width is divided into wide and thin according to the ratio of the single average wave area to the 15-second peak count when comparing the single average wave benchmark wave with the system average. The tension is divided into tight and loose according to the y-axis value of the peak when comparing the single average wave benchmark wave with the system average. The fullness is divided into stretch and empty according to the y-axis value of the trough when comparing multiple single average waves.

[0047] The pulse is calculated from three dimensions: fluency, strength and slope. The comparison between the single average wave benchmark wave and the system average value divides the fluency into smooth and difficult; the comparison between the single average wave benchmark wave and the system average value divides the strength into weak and strong by the ratio of the single average wave area to the single average wave circumference; the comparison between the single average wave benchmark wave and the wavelength divides the slope into gentle and rapid.

[0048] At the same time, the applicant has also developed a pulse diagnosis instrument management system that is used in conjunction with the pulse diagnosis instrument. Figures 3 to 7 Shown

[0049] Figure 3 : Boot page;

[0050] Figure 4 : Page of height, weight and physical fitness data not received: If no data is received after booting up, the user will be prompted to enter the account and fill in the height and weight information on the client;

[0051] Figure 5 : Received height, weight and physical fitness data page: After booting up, if data is received, the received data will be displayed, and the user will be prompted to confirm whether the device is tied properly. Click [start] to start collecting pulse;

[0052] Figure 6 : Pulse collection page: Perform four pressures in sequence: floating, middle, sinking, and pressing hard on the bone. When the corresponding step is reached, the yellow arrow at the bottom of the text flashes;

[0053] Figure 7 : Completed: Displays that the collection is complete and prompts the user to view the results on the client.

[0054] 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 principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A standardized pulse diagnosis method based on a wearable pulse monitor, using the wearable pulse monitor to collect pulse, characterized in that: The following steps are involved: Step S1: Initialize the patient's height h and weight g, and calculate the standard pressure P0 Step S2: applying pressure in sequence according to the following pulse diagnosis techniques under standard pressure: floating pressure, middle pressure, sinking pressure, and heavy pressure on the bone, and each step is T seconds, and m pressure data are read per second. After removing the data of T0 seconds before each step, fitting is performed, and the fitting curves are marked as ②: floating fitting curve; ③: middle fitting curve; ④: sinking fitting curve; ⑤: Press the bone fitting curve again; Step S3: determine the reference wave and calculate the average wavelength; By calculating the minimum convex hull area Ca of each fitting curve, the fitting curve with the largest minimum convex hull area is used as the reference wave; filtering, setting the wavelength or wave height that is too small as a pseudo wave, ignoring the trough of the pseudo wave and the adjacent smaller peak mark (or merging it into an adjacent wave with a smaller wavelength). Recalculate the average wavelength as aL. Step S4: trough identification; The point where the slope changes before and after in the fitting curve is defined as the pole, the point with the largest difference in the longitudinal axis between adjacent concave and convex poles is the rising segment, and the starting point of the rising segment is the calibration trough point. Taking aL as the scale, after the calibration trough point, find the rising segment with the starting point in the range of [aL×constant F] to [aL×(1+constant F^2)] on the X-axis. If the rising segment cannot be found within this range, modify the range to [aL×(1+constant F)] to [aL×(2+constant F^2)]. If the rising segment is still not found, feedback is given as an invalid spectrum, and the starting point of this rising segment is used as the next calibration point. Similarly, the starting points of all rising segments after the calibration trough point are determined. Similarly, before calibrating the trough point, find a rising segment with a starting point in the range of [-aL×constant F] to [-aL×(1+constant F^2)] on the X axis. If the rising segment cannot be found within this range, modify the range to [-aL×(1+constant F)] to [-aL×(2+constant F^2)]. If the rising segment is still not found, feedback is given as an invalid spectrum, and the starting point of this rising segment is used as the next calibration point. Similarly, determine the starting points of all rising segments before the calibration trough point. The lowest concave pole within the range of [-aL×constant F^3~0] before and after the starting point of each rising segment is the front trough. The highest point between the front and rear troughs is the peak. Step S5: Calculate the single mean wave; Calculate the single mean wave: divide each single wave x-axis into N equal parts to obtain x0~x N There are N+1 equally divided points corresponding to y0~y N , y0~y N Take the average values ​​respectively and synthesize these N+1 average values ​​into a single wave; the wave area is the area of ​​the figure enclosed by the lines connecting the pre-peak valley and the post-peak valley and the fitting curve; the wave circumference is the sum of the arc length and chord length of the figure enclosed by the lines connecting the pre-peak valley and the post-peak valley and the fitting curve / arc. Step S6: According to the waveform diagram obtained in the above steps, the pulse position, pulse rate, pulse shape and pulse strength are calculated according to individual dimensions.

2. A standardized pulse diagnosis method based on a wearable pulse monitor as claimed in claim 1, characterized in that: The calculation formula of standard pressure P0 is as follows: P0=90×(BMI / 23)^0.5 Wherein, BMI is body mass index, and BMI=g / h2, where height h is in meters and weight g is in kilograms. P0×(constant G)^0 (floating pressure); P0×(constant G)^1 (medium pressure); P0×(constant G)^2 (sunk pressure); P0×(constant G)^3 (bone pressure).

3. A standardized pulse diagnosis method based on a wearable pulse monitor as claimed in claim 1, characterized in that: In step S6, the pulse position, pulse rate, pulse shape and pulse strength are calculated according to 10 dimensions: depth, length, frequency, rhythm, width, tension, fullness, fluency, strength and slope.

4. A standardized pulse diagnosis method based on a wearable pulse monitor as claimed in claim 3, characterized in that: in, The pulse position is calculated from two dimensions: depth and length. The single average wave height is compared between the second fitting curve, the third fitting curve, the fourth fitting curve and the fifth fitting curve of this single average wave, and is divided into shallow pulse position, deep pulse position and very deep pulse position. The pulse position is divided into short pulse position and long pulse position by comparing the reference wave of this single average wave with the system average value and the ratio of the wave area above the wave waist to the wave area below the wave waist.

5. A standardized pulse diagnosis method based on a wearable pulse monitor as claimed in claim 3, characterized in that: in, The pulse rate is calculated from two dimensions: frequency and rhythm. The reference wave of this single average wave is compared with the system average value, and the 15-second peak count divides the pulse frequency into slow, slightly slow, fast and extremely fast. The wave height or wavelength within the reference wave curve is compared to divide the rhythm into irregular and paused.

6. A standardized pulse diagnosis method based on a wearable pulse monitor as claimed in claim 3, characterized in that: in, The pulse shape is calculated from three dimensions: width, tension and fullness. The width is divided into wide and thin according to the ratio of the single average wave area to the 15-second peak count when comparing the single average wave benchmark wave with the system average. The tension is divided into tight and loose according to the y-axis value of the peak when comparing the single average wave benchmark wave with the system average. The fullness is divided into stretch and empty according to the y-axis value of the trough when comparing multiple single average waves.

7. A standardized pulse diagnosis method based on a wearable pulse monitor as claimed in claim 3, characterized in that: The pulse is calculated from three dimensions: fluency, strength and slope. The comparison between the single average wave benchmark wave and the system average value divides the fluency into smooth and difficult; the comparison between the single average wave benchmark wave and the system average value divides the strength into weak and strong by the ratio of the single average wave area to the single average wave circumference; the comparison between the single average wave benchmark wave and the wavelength divides the slope into gentle and rapid.

8. A standardized pulse diagnosis platform based on a wearable pulse monitor, characterized in that: Used to execute a standardized pulse diagnosis method based on a wearable pulse monitor as described in any one of claims 1 to 7.

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