A method for determining the softness of traditional Chinese medicine pulse based on dense flexible sensor array

Abstract

The present invention provides a method for determining the softness of a Chinese medicine pulse based on a dense flexible sensor array, wherein the sensor array is used to obtain the deformation characteristic changes of the pulse caused by the change of finger pressure, so as to obtain the result of the determination of the softness of the pulse; the number of pressure sensors included in the sensor array on the one-dimensional array is ≥5; the method of the present invention comprises: dividing the pulse feeling process into several different stages according to the applied pressure from small to large; using the sensor array to collect the pulse pressure distribution curves of different stages; based on the pulse pressure distribution curve, constructing the deformation characteristics of the pulse, and obtaining the deformation characteristic values ​​in each stage; according to each of the deformation characteristic values, obtaining the total change characteristic value of the pulse in the pulse feeling process; detecting whether the total change characteristic value meets the preset pulse softness determination condition, and obtaining the pulse softness determination result according to the detection result, thereby effectively improving the accuracy of the pulse softness determination.

Description

Technical Field

[0001] The invention belongs to the field of medical sensor application technology, and more specifically relates to a method for determining the softness of traditional Chinese medicine based on a dense flexible sensor array. Background Art

[0002] Pulse softness refers to the degree of softness of the pulse, which can reflect vascular physiological information such as the peripheral resistance of the blood vessels and the degree of vascular wall hardening. Therefore, it is one of the main pulse characteristics that need to be obtained in traditional Chinese medicine pulse diagnosis. At present, pressure or strain sensors are mainly used to collect pulse softness information; for example, a sensor or sensor array is pressed on the radial artery at the Cunkou point, and the pulse wave curve characteristics under a specific loading pressure are collected to obtain the softness information of the radial artery.

[0003] However, since the sensors currently used are large in size, usually with a specification of 10 to 100 mm, when deploying sensors at the pulse cross-section of a single pulse, it is often only possible to deploy a single sensor or an array of no more than three sensors. This results in fewer features of the collected pulse wave curve, and it is impossible to accurately measure the distribution and change characteristics of the pulse signal in the direction of the pulse cross-section, which in turn leads to low accuracy in the pulse softness information identified based on the collected features. Summary of the invention

[0004] In view of the above shortcomings in the prior art, the purpose of the present invention is to provide a method for determining the softness of a Chinese medicine pulse based on a dense flexible sensor array, so as to solve the problem that the accuracy of the pulse softness determination results of the existing methods is low.

[0005] To achieve the above-mentioned purpose and other related purposes, the present invention provides a method for determining the softness of a Chinese medicine pulse based on a dense flexible sensor array, which uses a sensor array to obtain the deformation characteristic changes of the pulse caused by the change of finger pressure during the pulse-taking process to obtain a pulse softness determination result; wherein, the number of pressure sensors included in the sensor array on the one-dimensional array is ≥5; the method for determining the softness of a Chinese medicine pulse based on a dense flexible sensor array comprises: dividing the pulse-taking process into several different stages according to the applied pressure from small to large; during the pulse-taking process, using the sensor array to collect the pulse pressure distribution curve of different stages; based on the pulse pressure distribution curve, constructing the deformation characteristics of the pulse, and obtaining the deformation characteristic values ​​at each stage; wherein, the deformation characteristics are used to characterize the characteristics of the degree of deformation of the pulse; according to each of the deformation characteristic values, obtaining the total change characteristic value of the pulse during the pulse-taking process; detecting whether the total change characteristic value meets the preset pulse softness determination condition, and obtaining the pulse softness determination result according to the detection result.

[0006] In one embodiment of the present invention, the total coverage span of the sensor array in the one-dimensional array is not less than 5 mm, wherein the maximum distance between the centers of two adjacent sensors is not greater than 1.25 mm.

[0007] In one embodiment of the present invention, the sensor array is used to collect the pulse pressure distribution curve of a single stage, including: using the sensor array to collect the pulse pressure value at each collection moment; for the pulse pressure value at each collection moment, according to the arrangement of the corresponding sensor on the pulse cross section, constructing the pressure distribution curve corresponding to each collection moment; among the pressure distribution curves within a single stage, selecting the pressure distribution curve where the maximum pressure value is located as the pulse pressure distribution curve of the single stage.

[0008] In one embodiment of the present invention, the deformation characteristics of the pulse are constructed based on the pulse pressure distribution curve, including: constructing a first identification point in the left curve segment of the pulse pressure distribution curve, and constructing a second identification point in the right curve segment of the pulse pressure distribution curve; based on the distribution positions of the first identification point and the second identification point, the deformation characteristics of the pulse during the pulse diagnosis process are constructed.

[0009] In one embodiment of the present invention, the deformation characteristics of the pulse during the pulse diagnosis process are constructed based on the distribution positions of the first identification point and the second identification point, including: performing difference calculation on the coordinate information of the first identification point and the coordinate information of the second identification point to obtain the horizontal distance between the two points; and using the horizontal distance as the deformation feature.

[0010] In one embodiment of the present invention, constructing a first identification point in the left curve segment of the pulse pressure distribution curve includes: determining the coordinates based on the weight information of each acquisition channel:

[0011]

[0012]

[0013] Among them, vld_chnsi represents the number of each acquisition channel contained in the curve segment; x i is the channel number of the i-th acquisition channel; y i is the pressure value of the i-th acquisition channel; w i is the channel weight of the i-th channel; h is the height coefficient, satisfying 0≤h≤1.

[0014] In one embodiment of the present invention, the total change characteristic value of the pulse during the pulse diagnosis process is obtained based on each of the deformation characteristic values, including: performing linear fitting on each of the deformation characteristic values ​​to obtain a linear fitting slope; and using the slope as the total change characteristic value.

[0015] In one embodiment of the present invention, the total change characteristic value is detected to see whether it meets the preset pulse softness judgment condition, and the pulse softness judgment result is obtained according to the detection result, including: comparing the total change characteristic value with the preset softness threshold, and when the total change characteristic value is greater than the preset softness threshold, the pulse is judged to be a soft pulse.

[0016] As described above, the TCM pulse softness determination method based on a dense flexible sensor array provided by the present invention utilizes a sensor array to collect pressure distribution information on the pulse cross section, and obtains the deformation characteristic values ​​of the pulse at each stage of the pulse taking process based on the pressure distribution information on the pulse cross section, and obtains the pulse softness determination result based on the dynamic changes of the corresponding deformation characteristic values ​​at each stage, which can effectively improve the acquisition accuracy of the pressure distribution information of the pulse cross section, overcome the problems of low acquisition accuracy, inaccurate data, and inability to reflect the deformation characteristics of the pulse during the pulse taking process caused by isolated data collection at a single point or a few points, and by obtaining the pressure distribution information on the pulse cross section, eliminates the limitation that the previous sensor unit can only obtain the longitudinal waveform diagram, and improves the accuracy of pulse softness identification and determination. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of an application scenario of a method for determining pulse softness in traditional Chinese medicine based on a dense flexible sensor array according to the present invention in one embodiment;

[0018] Figure 2 A schematic diagram of the pulse pressure distribution collected at each moment in the present invention;

[0019] Figure 3 A schematic diagram of the structure of the sensor array in the present invention in a one-dimensional array;

[0020] Figure 4 A schematic diagram of the process of the method for determining the softness of a TCM pulse based on a dense flexible sensor array in this embodiment of the present invention;

[0021] Figure 5 A schematic flow chart of an implementation method of using the sensor array to collect the pulse pressure distribution curve corresponding to the current stage in the present invention;

[0022] Figure 6 The present invention constructs a schematic diagram of an envelope of a pulse pressure value in the direction of a pulse cross section;

[0023] Figure 7 A schematic flow chart of the method for acquiring deformation characteristics at each stage of the present invention;

[0024] Figure 8 A schematic diagram of the deformation of the pulse pressure distribution curve as finger pressure increases in the present invention;

[0025] Fig. 9 A schematic diagram of the present invention showing that the horizontal distance between the first marking point and the second marking point increases with the increase of finger pressure;

[0026] Fig.10 The Catmull-Rom spline interpolation algorithm is used to construct a schematic diagram of the smooth curve P(t) between P0 and P1;

[0027] Fig.11 The pulse pressure distribution curve graph collected within 6 pulse wave cycles in the first stage of the present invention;

[0028] Fig.12 The pulse pressure distribution curve graph collected during 6 pulse wave cycles in the second stage of the present invention;

[0029] Fig.13 The pulse pressure distribution curve graph collected within 6 pulse wave cycles in the third stage of the present invention;

[0030] Fig.14 The pulse pressure distribution curve graph collected within 6 pulse wave cycles in the fourth stage of the present invention;

[0031] Fig.15 A fitted straight line graph of the average value of the spacing between the marking points obtained at each stage of the present invention;

[0032] Reference numerals

[0033] 1 Sensor Array

[0034] 11 Sensors

[0035] 2 Pulse body

[0036] 3 Pulse pressure distribution curve DETAILED DESCRIPTION

[0037] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0038] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0039] Example 1

[0040] In order to solve the technical problems existing in the prior art, an embodiment of the present invention provides, in a first aspect, a method for determining the softness of a TCM pulse based on a dense flexible sensor array. During the TCM pulse taking process, a dense flexible sensor array (hereinafter referred to as the "sensor array") is used to obtain the deformation characteristic changes of the pulse as the applied pressure changes, thereby obtaining the softness determination result of the pulse.

[0041] Wherein, the pulse feeling process is a process in which finger pressure gradually increases.

[0042] The number of pressure sensors included in the one-dimensional array of the sensor array is ≥5, and the maximum center distance m between two adjacent sensors along the cross-sectional direction of the pulse body is no more than 1.25 mm.

[0043] The softness determination result includes a soft pulse grading result of the pulse body; in one specific embodiment, the soft pulse grading result includes two levels: soft pulse and hard pulse; in another specific embodiment, the soft pulse grading result includes four levels: soft pulse, relatively soft pulse, relatively hard pulse and hard pulse.

[0044] See also Figures 1 to 3 , which is a schematic diagram of the application of the method for determining the softness of a TCM pulse based on a dense flexible sensor array as described in an embodiment of the present invention.

[0045] in, Figure 1 FIG. 1 is a schematic diagram of an application scenario of the TCM pulse softness determination method based on a dense flexible sensor array in one embodiment; Figure 1 As shown, the sensor array 1 includes a plurality of micro pressure sensors 11 (hereinafter referred to as "sensors"); a pulse pressure distribution curve 3 is obtained during the pulse diagnosis process based on each sensor.

[0046] The sensors 11 are arranged in a one-dimensional array or a two-dimensional array, which is not specifically limited here.

[0047] When used, the sensor array 1 is pressed on the pulse 2 so that the arrangement direction of the sensor array is perpendicular or nearly perpendicular to the extension direction of the pulse; the sensors in the sensor array collect pulse pressure data at each moment to obtain Figure 2 The pulse pressure distribution curve at each moment is shown.

[0048] Preferably, the middle area of ​​the sensor array is pressed over the pulse body, so that the sensor array can completely collect the overall morphological characteristics of the pulse body without leakage, thereby making the pressure data collected by each sensor in the array more accurate.

[0049] On a one-dimensional array, Figure 3As shown, the number of pressure sensors (hereinafter referred to as "sensors") included is ≥ 5; the sensors are arranged in sequence, and the total coverage span M is not less than 5 mm; the maximum center spacing m between two adjacent sensors along the pulse cross-section direction is not greater than 1.25 mm.

[0050] In a specific embodiment, the sensor array is a one-dimensional array composed of 18 sensors, each sensor is regarded as an acquisition channel, that is, the sensor array provides 18 signal acquisition channels; the size of a single sensor is 0.4×0.4mm; the center distance between two adjacent sensors is 0.65mm, the total coverage span is 11.05mm, and it is packaged on a flexible substrate.

[0051] See also Figure 4 , which is a flow chart of the method for determining the softness of a TCM pulse based on a dense flexible sensor array in this embodiment. Figure 4 As shown, the method comprises the following steps:

[0052] S100, dividing the pulse diagnosis process into a plurality of different stages according to the pressure applied from small to large;

[0053] The pulse-feeding process is the same as that of traditional Chinese medicine, which is a process of gradually increasing finger pressure.

[0054] In this embodiment, the pulse-feeding process is divided into four stages according to the finger pressure from light to heavy, namely, the lightest finger pressure, slightly heavy finger pressure, relatively heavy finger pressure and the heaviest finger pressure.

[0055] It should be noted that the size of the finger pressure used to divide each stage can be determined according to the specific pulse body and pulse feeling method, and is not specifically limited here.

[0056] Specifically, the division of different stages in the pulse diagnosis process includes:

[0057] The finger pressure extreme value during the pulse diagnosis process is obtained; based on the finger pressure extreme value, the finger pressure size is segmented, and each segment corresponds to a different stage; wherein the extreme value includes the minimum value and the maximum value of the finger pressure.

[0058] In other embodiments, the division of different stages in the pulse diagnosis process may also be:

[0059] The total duration of the pulse-feeding process is obtained in advance; the total duration is segmented according to the number of stages, and each time period corresponds to a different pulse-feeding stage.

[0060] S200, during the pulse diagnosis process, using the sensor array to collect pulse pressure distribution curves at different stages;

[0061] The pulse pressure distribution curve is a distribution curve of the pulse pressure values ​​collected by the sensor array in the direction of the pulse cross section.

[0062] It should be noted that the pulse pressure distribution curve is a distribution curve of the pulse pressure values ​​collected by each effective channel in the sensor array; wherein, the effective channel is a channel that can effectively sense the pulse pressure condition, usually provided by several sensors close to the top of the blood vessel.

[0063] For a single stage, the sensor array is used to collect the pulse pressure distribution curve corresponding to the current stage, such as Figure 5 As shown, including:

[0064] S201, using a sensor array to collect pulse pressure values ​​at each collection moment;

[0065] Specifically, each sensor in the pulse array is controlled to collect pressure values ​​of the corresponding coverage area of ​​the sensor based on the same collection frequency; illustratively, each sensor is controlled to collect each pressure value within 6 consecutive pulse wave cycles at a collection frequency of 800 Hz.

[0066] The pressure value collected by each sensor at the same collection time is used as the pulse pressure value collected by the sensor array at that moment.

[0067] Among them, the pressure values ​​collected at the same collection time include: the pressure values ​​collected by each sensor at the same time, and the pressure values ​​collected by each sensor within a preset time difference range, that is, the time difference between the corresponding collection times of any two sensors among each sensor is less than the preset time length; optionally, the preset time length is any time length not greater than 0.1 seconds.

[0068] S202, for the pulse pressure value at each acquisition time, construct a pulse pressure distribution curve corresponding to each acquisition time according to the arrangement of the corresponding sensor on the pulse cross section;

[0069] Specifically, Figure 6 As shown, the arrangement direction of each sensor on the pulse cross section is taken as the X-axis, and the pulse pressure value collected at each collection moment is taken as the Y-axis, and the envelope line of the pulse pressure value in the direction of the pulse cross section is constructed as the pressure distribution curve at each collection moment.

[0070] This step is performed for the pulse pressure values ​​collected at each collection moment to obtain the pressure distribution curve corresponding to each collection moment.

[0071] S203, among the pressure distribution curves in a single stage, selecting the pressure distribution curve with the maximum pressure value as the pulse pressure distribution curve of the single stage;

[0072] Specifically, for each of the pressure distribution curves in the same stage, the maximum pressure value in each of the pressure distribution curves, that is, the maximum value of the Y axis in the pressure distribution curve, is obtained respectively.

[0073] Among the above-mentioned maximum pressure values, the maximum value is determined, and the pressure distribution curve corresponding to the maximum value is set as the pulse pressure distribution curve of the current stage to improve the accuracy of the deformation characteristics obtained in the subsequent steps.

[0074] S300, constructing a deformation feature of a pulse body based on the pulse body pressure distribution curve, and obtaining a deformation feature value at each stage;

[0075] The deformation feature is used to characterize the degree of deformation of the pulse body during the pulse diagnosis process; optionally, the degree of deformation includes the degree of horizontal deformation caused by the pulse body being squeezed to both sides.

[0076] Specifically, when this step is executed, Figure 7 As shown, it includes the following sub-steps:

[0077] In this embodiment, the morphological characteristics include characteristic deformation of the vein body, and the method for obtaining the deformation degree characteristics is as follows: Figure 7 As shown, including:

[0078] S301, constructing a first identification point in the left curve segment of the pulse pressure distribution curve, and constructing a second identification point in the right curve segment of the pulse pressure distribution curve;

[0079] During the pulse diagnosis process, Figure 7 As shown, the collected pulse pressure distribution curve will present a curve with a concave middle portion, that is, the pulse pressure in the middle portion is smaller than the pulse pressures on both sides; and as shown Figure 8 As shown in the figure, as the finger pressure increases, the range of the middle depression gradually increases; this is because when the pulse is pressed, as the finger pressure increases, the blood in the pulse is squeezed from the center to both sides. According to the position of the lowest point of the depression in the pulse pressure distribution curve, the pulse pressure distribution curve is divided into a left curve segment and a right curve segment.

[0080] It should be noted that in the initial stage of pulse diagnosis, when the finger pressure is relatively small, the pulse pressure distribution curve collected does not show a depression in the middle.

[0081] For a single pulse pressure distribution curve, a first identification point is determined in its left curve segment, and a second identification point is determined in its right curve segment.

[0082] In a specific embodiment, the method for determining the first identification point includes:

[0083] For the left curve segment, based on the weight information of each acquisition channel, the coordinates of the first landmark point P are determined as:

[0084]

[0085]

[0086] Wherein, vld_chnsi represents the serial number of each acquisition channel included in the left curve segment; the acquisition channel is a sensor, that is, a single sensor represents one acquisition channel;

[0087] x i is the number of the i-th acquisition channel, that is, the horizontal coordinate of the i-th acquisition channel here; y i is the pressure value of the i-th acquisition channel, that is, the ordinate of the pressure distribution curve of the pulse cross section at this location;

[0088] w i is the channel weight of the i-th channel, which is a preset parameter used to represent the channel weight of the i-th acquisition channel in the left curve segment;

[0089] h i is the height coefficient, which is a preset parameter and satisfies 0≤h≤1. When the value of h is closer to 0, it means that the identification point is closer to the bottom; when the value of h is closer to 1, it means that the identification point is closer to the top.

[0090] By adjusting the height coefficient and the weight of each channel, the distribution position of the identification point in the left curve segment is determined.

[0091] Exemplarily, when the weight of each effective channel of the left half section is 1, the weights of the other channels are all 0, and h=0.5, the identification point P is the centroid of the left half section.

[0092] Exemplarily, when the weights of the two effective channels at both ends are 1 and the weights of the other channels are 0, h=0, the identification point P is the midpoint of the base of the effective channel.

[0093] Exemplarily, when the weight of the channel with the highest amplitude is 1 and the weights of the other channels are all 0, h=1, the identification point P is the peak point of the envelope curve.

[0094] It should be noted that the specific methods for determining the marking point P include but are not limited to the above methods, as long as the coordinates satisfy the form of Formula 1.

[0095] Similarly, based on the above method, the coordinate information of the second identification point Q in the right curve segment is obtained.

[0096] S302, constructing deformation characteristics of the pulse body during the pulse diagnosis process based on the distribution positions of the first identification points and the second identification points;

[0097] Specifically, the coordinate information of the first identification point and the coordinate information of the second identification point are difference calculated to obtain the horizontal distance between the two points, that is, the spacing distance between the two points in the X-axis direction; and the horizontal distance is used as the deformation feature.

[0098] For example, Fig. 9 As shown, during the pulse diagnosis process, the horizontal distance between the first marking point and the second marking point increases as the finger pressure increases.

[0099] It should be noted that, in the initial stage of pulse diagnosis, when the pulse pressure distribution curve does not show a depression in the middle, the deformation feature is set to 0.

[0100] S400, obtaining a total change characteristic value of the pulse body during the pulse diagnosis process according to each of the deformation characteristic values;

[0101] Specifically, a linear fit is performed on each of the deformation characteristic values ​​obtained in step S300 to obtain a slope of the linear fit, and the slope is used as the total change characteristic value.

[0102] In other embodiments, the total change characteristic value may further include a quadratic fitting coefficient, etc.; that is, nonlinear fitting is performed on each of the deformation characteristic values ​​obtained in step S300 to obtain a quadratic fitting coefficient, and the quadratic fitting coefficient is used as the total change characteristic value.

[0103] S500, detecting whether the total change characteristic value meets the preset pulse softness determination condition, and obtaining the pulse softness determination result according to the detection result.

[0104] Wherein, the preset pulse softness determination condition includes being greater than a preset softness threshold.

[0105] Specifically, the total change characteristic value is compared with a preset softness threshold, and when the total change characteristic value is greater than the preset softness threshold, the pulse is determined to be a soft pulse.

[0106] Furthermore, in order to improve the accuracy of extracting the identification points in the pulse pressure distribution curve, before executing step S300, the TCM pulse softness determination method based on the dense flexible sensor array further includes:

[0107] The pulse pressure distribution curve is preprocessed by a smoothing method to improve the accuracy of the marking points.

[0108] Specifically, Fig.10 As shown, the Catmull-Rom spline interpolation algorithm is used to construct a smooth curve P(t) between P0 and P1. P(t) is the following cubic curve to be solved:

[0109] P(t)=at 3 +bt 2 +ct+d

[0110]

[0111] Among them, P -1 P0 and P1 are the two points to be smoothed.

[0112] It should be noted that, in actual implementation, other processing methods for smoothly connecting the pulse pressure distribution curve may also be used, and are not limited thereto.

[0113] The following will illustrate the implementation process of the TCM pulse softness determination method based on dense flexible sensor array provided by the present invention through a specific embodiment. Among them, the first identification point is taken from the center of gravity of the left curve segment, and the second identification point is taken from the center of gravity of the right curve segment; after the pulse diagnosis begins, the sensor array is controlled to collect data in 4 groups of finger pressure stages from floating to sinking to obtain the pulse pressure distribution curve corresponding to each stage. Figure 11 to Figure 14 Schematic diagrams of the pulse pressure distribution curves collected in 6 pulse wave cycles at each stage are shown respectively; the vertical dotted lines in each figure are, from left to right, the left boundary of the left curve segment, the dividing line between the left curve segment and the right curve segment, and the right boundary of the right curve segment; point G1 is the first identification point, and point G2 is the second identification point.

[0114] In each stage, the distances between the first marking point and the second marking point measured in the six pulse wave cycles are averaged; the average distance values ​​in each stage are shown in the following table, which are 1.99, 2.06, 2.52 and 2.6 respectively.

[0115] Table 1 Average distance between the first mark point and the second mark point collected in each pulse wave cycle

[0116]

[0117] A linear fit is made to the average spacing of the marking points obtained in the above stages, and the fitting line is as follows: Fig.15 As shown, its slope is 0.23 and the goodness of fit R 2 =0.9. Since the pulse softness threshold is 0.04 and 0.23≥0.04, the pulse is judged to be soft.

[0118] Example 2

[0119] In order to solve the problems existing in the prior art, the present invention provides an electronic device, which includes: a processor, a memory, a transceiver, a communication interface and a system bus; the memory and the communication interface are connected to the processor and the transceiver through the system bus and complete communication with each other, the memory is used to store computer programs, the communication interface is used to communicate with other devices, and the processor and the transceiver are used to run computer programs, so that the processing device executes each step in the method for determining the softness of traditional Chinese medicine pulse based on a dense flexible sensor array as described above.

[0120] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0121] Example 3

[0122] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when called by a processor, implements the various steps in the method for determining the softness of a TCM pulse based on a dense flexible sensor array as described above.

[0123] Among them, the computer-readable storage medium can be a tangible device that can hold and store instructions used by the instruction execution device. The computer-readable storage medium can be, for example, (but not limited to) an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, and a mechanical encoding device.

[0124] The computer-readable program described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. A network adapter card or a network interface in each computing / processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0125] In summary, the method for determining the softness of a Chinese medicine pulse based on a dense flexible sensor array provided by the present invention utilizes a sensor array to collect pressure distribution information on a pulse cross section, extracts deformation characteristics of the pulse at each stage of the pulse diagnosis process based on the pressure distribution information on the pulse cross section, and obtains the result of the determination of the softness of the pulse based on the dynamic changes of the deformation characteristic values ​​corresponding to each stage. This can effectively provide the acquisition accuracy of the pressure distribution information on the pulse cross section, overcome the problems of low acquisition accuracy, inaccurate data, and inability to reflect the deformation characteristics of the pulse during the pulse diagnosis process caused by isolated data acquisition at a single point or a few points, and by obtaining the pressure distribution information on the pulse cross section, eliminates the limitation that the previous sensor unit can only obtain the longitudinal waveform diagram, thereby improving the accuracy of pulse softness identification and determination. In addition, by adopting a smoothing method to pre-process the collected pulse pressure distribution curve, the characteristic points on the curve can be made easier to identify, further improving the accuracy of deformation feature acquisition, and also improving the accuracy of pulse softness determination.

[0126] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for determining the softness of a Chinese medicine pulse based on a dense flexible sensor array, characterized in that: The sensor array is used to obtain the deformation characteristic changes of the pulse body caused by the change of the finger pressure during the pulse diagnosis process, so as to obtain the pulse body softness determination result; wherein the number of pressure sensors included in the sensor array on the one-dimensional array is ≥5; The method for determining the softness of a TCM pulse based on a dense flexible sensor array comprises: The pulse diagnosis process is divided into several different stages according to the pressure applied from small to large; During the pulse diagnosis process, the sensor array is used to collect pulse pressure distribution curves at different stages; Based on the pulse pressure distribution curve, the deformation characteristics of the pulse body are constructed, and the deformation characteristic values ​​at each stage are obtained; wherein the deformation characteristics are used to characterize the characteristics of the degree of deformation of the pulse body; According to each of the deformation characteristic values, obtaining a total change characteristic value of the pulse body during the pulse diagnosis process; It is detected whether the total change characteristic value meets the preset pulse softness determination condition, and the pulse softness determination result is obtained according to the detection result.

2. The method for determining the softness of a Chinese medicine pulse based on a dense flexible sensor array according to claim 1, characterized in that: The total coverage span of the sensor array in the one-dimensional array is not less than 5 mm, wherein the maximum center distance between two adjacent sensors is not greater than 1.25 mm.

3. The method for determining the softness of a TCM pulse based on a dense flexible sensor array according to claim 1, characterized in that: The sensor array is used to collect a single-stage pulse pressure distribution curve, including: Using a sensor array to collect pulse pressure values ​​at each collection moment; For the pulse pressure value at each acquisition moment, according to the arrangement of the corresponding sensor on the pulse cross section, construct the pressure distribution curve corresponding to each acquisition moment; Among the pressure distribution curves in a single stage, the pressure distribution curve with the maximum pressure value is selected as the pulse pressure distribution curve of the single stage.

4. The method for determining the softness of a TCM pulse based on a dense flexible sensor array according to claim 3, characterized in that: The step of constructing the deformation characteristics of the pulse body based on the pulse body pressure distribution curve includes: Constructing a first marking point in the left curve segment of the pulse pressure distribution curve, and constructing a second marking point in the right curve segment of the pulse pressure distribution curve; Based on the distribution positions of the first identification points and the second identification points, the deformation characteristics of the pulse during the pulse diagnosis process are constructed.

5. The method for determining the softness of a TCM pulse based on a dense flexible sensor array according to claim 4, characterized in that: The step of constructing the deformation characteristics of the pulse body during the pulse diagnosis process based on the distribution positions of the first identification point and the second identification point includes: The coordinate information of the first identification point and the coordinate information of the second identification point are difference calculated to obtain the horizontal distance between the two points; and the horizontal distance is used as the deformation feature.

6. The method for determining the softness of a TCM pulse based on a dense flexible sensor array according to any one of claims 3 to 5, characterized in that: The step of constructing a first identification point in the left curve segment of the pulse pressure distribution curve comprises: Based on the weight information of each acquisition channel, the coordinates are determined as: Among them, vld_chnsi represents the number of each acquisition channel contained in the curve segment; x i is the channel number of the i-th acquisition channel; y i is the pressure value of the i-th acquisition channel; w i is the channel weight of the i-th channel; h is the height coefficient, satisfying 0≤h≤1.

7. The method for determining the softness of a TCM pulse based on a dense flexible sensor array according to claim 1, characterized in that: The method of obtaining the total change characteristic value of the pulse body during the pulse diagnosis process according to each of the deformation characteristic values ​​includes: performing linear fitting on each of the deformation characteristic values ​​to obtain a linear fitting slope; and using the slope as the total change characteristic value.

8. The method for determining the softness of a TCM pulse based on a dense flexible sensor array according to claim 1, characterized in that: The detecting whether the total change characteristic value satisfies a preset pulse body softness determination condition, and obtaining a pulse body softness determination result according to the detection result, comprises: The total change characteristic value is compared with a preset softness threshold, and when the total change characteristic value is greater than the preset softness threshold, the pulse is determined to be a soft pulse.

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