Health status analysis device, method and system based on multi-dimensional bioelectric signals

Abstract

The present application relates to the technical field of bioelectric information acquisition and processing, and particularly relates to a health state analysis device, method and system based on multi-dimensional bioelectric signals, which acquires bioelectric signals of each meridian of a measured person and standard bioelectric signals; determines a representation coefficient of the meridian of the measured person according to a difference between the bioelectric signals of each meridian and the corresponding standard bioelectric signals; obtains a difference coefficient of the measured person according to a mean value of the bioelectric signals of all the meridians and a mean value of all the standard bioelectric signals; and classifies health state data of the current measured person based on the representation coefficient and the difference coefficient. That is, the scheme of the present application can quickly realize classification of the health state of the measured person, and provides data auxiliary support for subsequent doctor evaluation.

Description

Technical Field

[0001] This invention generally relates to the field of bioelectrical information acquisition and processing technology. More specifically, this invention relates to a device, method, and system for health status analysis based on multi-dimensional bioelectrical signals. Background Technology

[0002] Meridian measurement is a product of the combination of traditional Chinese medicine theory and modern science and technology. Meridian measurement data acquired through bioconductivity sensors can provide objective indicators for clinical diagnosis in traditional Chinese medicine, serving as an auxiliary basis for physicians' judgment and thus improving their diagnostic capabilities.

[0003] The analysis of health status based on meridian measurement using bioelectric signal sensors has been widely applied. For example, patent application CN 109316187 A, entitled "A Method and System for Identifying Health Status by Electroconduction Meridians in Traditional Chinese Medicine," discloses the identification of measurement values ​​of the twelve bilateral primary acupoints of the human body measured by the electroconduction method in traditional Chinese medicine, and the calculation and analysis of the measurement results. Based on the measurement results of the specific person being measured, the physical condition of the person being measured is specifically identified, as well as the specific balance between the person being measured as a whole and between various meridians, both internally and externally, and between front, back, left, and right.

[0004] It should be noted that although the aforementioned existing technologies can identify meridian measurement data, their analysis methods are too complex and do not take into account the deviations in meridian measurement data due to differences in the physical condition of different individuals, which leads to the problem of inaccurate identification of meridian measurement data. Summary of the Invention

[0005] To address one or more of the aforementioned technical problems, this invention proposes acquiring the bioelectrical signals of the test subject, processing the bioelectrical signals of all meridians into a characterization coefficient, and combining it with a difference coefficient to obtain the current classification of the test subject. This allows doctors to conduct targeted assessments of the test subject based on the classification results. Furthermore, the classification algorithm is simple and can quickly perform a preliminary, rough classification of the test subject. To this end, this invention provides solutions in the following aspects.

[0006] In one aspect of the invention, a health status analysis device based on multidimensional bioelectrical signals includes: The data acquisition module is used to acquire the bioelectrical signals of each meridian of the subject and the standard bioelectrical signals of each meridian of a normal healthy person. The data processing module is used to determine the characterization coefficient of the subject's meridian data based on the difference between the bioelectric signals of each meridian and the corresponding standard bioelectric signals; and to obtain the subject's difference coefficient based on the mean of the bioelectric signals of all meridians and the mean of all standard bioelectric signals. The status analysis module is used to classify the current health status data of the subject based on the characterization coefficient and the difference coefficient.

[0007] Preferably, the method further includes a correction process for the difference: The physical characteristics and age of the test subjects and normal healthy individuals were obtained separately; the physical characteristics included, but were not limited to, height and weight. The difference in body shape characteristics between the test subject and normal healthy individuals will be calculated, and the ratio of this difference to the body shape characteristics of normal healthy individuals will be used as the first adjustment coefficient. Calculate the difference in age between the test subject and a normal healthy person, and use the ratio of this difference to the age of a normal healthy person as the second adjustment coefficient; The product of the first adjustment coefficient and the second adjustment coefficient is used as the compensation adjustment coefficient for the difference. The product of the compensation adjustment coefficient and the difference is calculated as the compensation adjustment value. The sum of the compensation adjustment value and the difference is used as the corrected difference.

[0008] Preferably, the characterization coefficient is: in, For the first k The difference corresponding to each meridian, i.e. S k For the first k The bioelectrical signal corresponding to each meridian, S k 0 For the first k The standard bioelectrical signals corresponding to each meridian n δ represents the number of meridians, and δ is the compensation and adjustment coefficient. The difference coefficient is: Where S is the mean of the bioelectrical signals of all meridians, and S0 is the mean of the standard bioelectrical signals of all meridians.

[0009] Preferably, the process of classifying the current subject's health status data is as follows: By comparing the characterization coefficient with the first and second thresholds, and by comparing the difference coefficient with the third and fourth thresholds, the current classification of the subject's health status data is determined. Specifically: If the characterization coefficient is less than the second threshold and the difference coefficient is less than the fourth threshold, then the current subject is considered normal. When the characterization coefficient is greater than the second threshold and less than the first threshold, and the difference coefficient is less than the fourth threshold, the current subject belongs to the category of Yin Shi; where the first threshold is greater than the second threshold. When the characterization coefficient is greater than the first threshold and the difference coefficient is less than the fourth threshold, the current subject is suspected of having a disease. When the characterization coefficient is less than the second threshold, and the difference coefficient is greater than the fourth threshold but less than the third threshold, the current subject is considered to have Yang deficiency; where the fourth threshold is less than the third threshold. When the characterization coefficient is greater than the second threshold and less than the first threshold, and the difference coefficient is greater than the fourth threshold and less than the third threshold, the current test subject belongs to the category of Yin-Yang deficiency; When the characterization coefficient is greater than the first threshold and the difference coefficient is greater than the fourth threshold but less than the third threshold, the current subject is suspected of having a disease. When the characterization coefficient is less than the second threshold and the difference coefficient is greater than the third threshold, the current subject is suspected of having a disease. When the characterization coefficient is greater than the second threshold and less than the first threshold, and the difference coefficient is greater than the third threshold, then the current subject belongs to Yin deficiency. When the characterization coefficient is greater than the first threshold and the difference coefficient is greater than the third threshold, the current subject is suspected of having a disease.

[0010] Preferably, the bioelectric signal is one of biocurrent value, biopressure value, or resistance value.

[0011] In another aspect of the invention, a health status analysis method based on multidimensional bioelectrical signals includes the following steps: Obtain the bioelectrical signals of each meridian of the subject and the standard bioelectrical signals of each meridian of a normal healthy person; The characteristic coefficients of the meridians of the test subject are determined based on the difference between the bioelectric signals of each meridian and the corresponding standard bioelectric signals. The difference coefficient of the subject is obtained by taking the mean of the bioelectrical signals of all meridians and the mean of all standard bioelectrical signals; the health status data of the current subject is classified based on the characterization coefficient and the difference coefficient.

[0012] Preferably, the method further includes a correction process for the difference: The physical characteristics and age of the test subjects and normal healthy individuals were obtained separately; the physical characteristics included, but were not limited to, height and weight. The difference in body shape characteristics between the test subject and normal healthy individuals will be calculated, and the ratio of this difference to the body shape characteristics of normal healthy individuals will be used as the first adjustment coefficient. Calculate the difference in age between the test subject and a normal healthy person, and use the ratio of this difference to the age of a normal healthy person as the second adjustment coefficient; The product of the first adjustment coefficient and the second adjustment coefficient is used as the compensation adjustment coefficient for the difference. The product of the compensation adjustment coefficient and the difference is calculated as the compensation adjustment value. The sum of the compensation adjustment value and the difference is used as the corrected difference.

[0013] Preferably, the characterization coefficient is: in, For the first k The difference corresponding to each meridian, i.e. S k For the first k The bioelectrical signal corresponding to each meridian, S k 0 For the first k The standard bioelectrical signals corresponding to each meridian n δ represents the number of meridians, and δ is the compensation and adjustment coefficient. The difference coefficient is: Where S is the mean of the bioelectrical signals of all meridians, and S0 is the mean of the standard bioelectrical signals of all meridians.

[0014] Preferably, the process of classifying the current subject's health status data is as follows: By comparing the characterization coefficient with the first and second thresholds, and by comparing the difference coefficient with the third and fourth thresholds, the current classification of the subject's health status data is determined. Specifically: If the characterization coefficient is less than the second threshold and the difference coefficient is less than the fourth threshold, then the current subject is considered normal. When the characterization coefficient is greater than the second threshold and less than the first threshold, and the difference coefficient is less than the fourth threshold, the current subject belongs to the category of Yin Shi; where the first threshold is greater than the second threshold. When the characterization coefficient is greater than the first threshold and the difference coefficient is less than the fourth threshold, the current subject is suspected of having a disease. When the characterization coefficient is less than the second threshold, and the difference coefficient is greater than the fourth threshold but less than the third threshold, the current subject belongs to Yang deficiency; where the fourth threshold is less than the third threshold. When the characterization coefficient is greater than the second threshold and less than the first threshold, and the difference coefficient is greater than the fourth threshold and less than the third threshold, the current test subject belongs to the category of Yin-Yang deficiency; When the characterization coefficient is greater than the first threshold and the difference coefficient is greater than the fourth threshold but less than the third threshold, the current subject is suspected of having a disease. When the characterization coefficient is less than the second threshold and the difference coefficient is greater than the third threshold, the current subject is suspected of having a disease. When the characterization coefficient is greater than the second threshold and less than the first threshold, and the difference coefficient is greater than the third threshold, then the current subject belongs to Yin deficiency. When the characterization coefficient is greater than the first threshold and the difference coefficient is greater than the third threshold, the current subject is suspected of having a disease.

[0015] In another aspect of the invention, a health status analysis system based on multidimensional bioelectric signals includes: a processor; and a memory storing computer instructions for health status analysis based on multidimensional bioelectric signals, wherein when the computer instructions are executed by the processor, the device performs the technical solution according to the above-described health status analysis method based on multidimensional bioelectric signals.

[0016] The beneficial effects of this invention are as follows: This invention acquires bioelectrical signals from various meridians, and obtains the characterization coefficient and difference coefficient of the test subject based on the bioelectrical signals. The difference coefficient and characterization coefficient are used to classify the physical health status of the test subject, which can quickly and roughly estimate the current category of the test subject. This provides data support for doctors to conduct further health assessments of the test subject, enabling more targeted diagnosis of the test subject.

[0017] Meanwhile, when using the meridian bioelectric signals of normal healthy people as the standard bioelectric signals, this invention also takes into account the body type and age factors of the test subject and normal healthy people. That is, it introduces the index of this factor as a compensation adjustment coefficient to correct each difference and optimize the characterization coefficient, which can make the subsequent classification more accurate. Attached Figure Description

[0018] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein: Figure 1 This is a structural block diagram of the health status analysis device based on multi-dimensional bioelectric signals in this embodiment; Figure 2 This is a schematic diagram showing the classification region to which the subject belongs after analysis using the health status analysis device based on multi-dimensional bioelectric signals in this embodiment; Figure 3 This is a flowchart illustrating the health status analysis method based on multidimensional bioelectrical signals in this embodiment. Figure 4 This is a structural block diagram of the health status analysis system based on multi-dimensional bioelectrical signals in this embodiment. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0021] While existing technologies can analyze meridian measurement data and assist physicians in assessing the health status of test subjects based on the analysis results, their analysis methods are too complex and do not take into account the differences in acupoint locations during meridian measurement due to individual differences in the body. This results in deviations in the measured bioelectrical signal data, leading to biases in the analysis of meridian measurement data.

[0022] Therefore, based on the above-mentioned technical problems, the present invention provides a health status analysis device, method and system based on multi-dimensional bioelectric signals. By acquiring bioelectric signal data from multiple meridians, and performing data processing and analysis based on all bioelectric signals and corresponding preset standard bioelectric signals, the current health status of the subject can be analyzed. The method is simple and can quickly provide doctors with corresponding data support for subsequent assessment of the subject's health status.

[0023] Specifically, the meridians of this invention are aimed at the twelve meridians, which are clinically referred to as the twelve primary meridians. The twelve primary meridians are connected to the internal organs in the body. The twelve primary meridians are distributed symmetrically on the left and right sides of the head, face, limbs, trunk and other parts of the body with the midline as the axis. They have fixed routes and directions of blood and qi flow and the rules of meridian intersection. It includes the three Yin meridians of the hand (Lung Meridian of Hand-Taiyin, Pericardium Meridian of Hand-Jueyin, Heart Meridian of Hand-Shaoyin), the three Yang meridians of the hand (Large Intestine Meridian of Hand-Yangming, Triple Energizer Meridian of Hand-Shaoyang, Small Intestine Meridian of Hand-Taiyang), the three Yin meridians of the foot (Spleen Meridian of Foot-Taiyin, Liver Meridian of Foot-Jueyin, Kidney Meridian of Foot-Shaoyin), and the three Yang meridians of the foot (Stomach Meridian of Foot-Yangming, Gallbladder Meridian of Foot-Shaoyang, Bladder Meridian of Foot-Taiyang). The flow of Qi and blood begins at the Lung Meridian of Hand-Taiyin in the middle Jiao, flows through each meridian in sequence, and finally reaches the Liver Meridian of Foot-Jueyin, then returns to the Lung Meridian of Hand-Taiyin. The flow is continuous, that is, the order of the flow of Qi and blood in the twelve meridians is: Lung Meridian of Hand-Taiyin - Large Intestine Meridian of Hand-Yangming - Stomach Meridian of Foot-Yangming - Spleen Meridian of Foot-Taiyin - Heart Meridian of Hand-Shaoyin - Small Intestine Meridian of Hand-Taiyang - Bladder Meridian of Foot-Shaoyin - Kidney Meridian of Foot-Jueyin - Heart Meridian of Hand-Jueyin - Triple Energizer Meridian of Hand-Shaoyang - Gallbladder Meridian of Foot-Jueyin - Liver Meridian of Foot-Lung. According to the above-mentioned meridian distribution route and direction, the meridians circulate one after another, running through the whole body in a continuous cycle.

[0024] Meridians are important channels connecting the internal organs. When meridians are blocked, the internal organs lose their normal communication, and their functions cannot be properly performed. This leads to imbalances in Qi, blood, Yin, and Yang, which in turn affects health and harms the body. Therefore, measuring meridian data can help analyze the health status of the person being tested, assisting doctors in making accurate assessments of their health.

[0025] Figure 1 This is a structural block diagram of the health status analysis device based on multi-dimensional bioelectric signals in this embodiment. Figure 2 This is a schematic diagram showing the classification region to which the subject belongs after analysis using the health status analysis device based on multi-dimensional bioelectric signals in this embodiment.

[0026] Specifically, taking the twelve meridians of the left or right foot of a test subject as an example, such as... Figure 1 As shown, the health status analysis device based on multi-dimensional bioelectric signals in this embodiment includes: a data acquisition device, a data processing device, and a status analysis device.

[0027] The data acquisition device is used to acquire the bioelectrical signals of each meridian of the subject and the standard bioelectrical signals of each meridian of a normal healthy person.

[0028] In this embodiment, a meridian acupoint resistance measuring instrument, a traditional Chinese medicine meridian instrument, or a dedicated acupoint electrograph for collecting human acupoint bioelectrical signals is used to monitor each acupoint (e.g., 24 acupoints) corresponding to each of the twelve meridians one by one, obtaining the bioelectrical signals of the meridians. Specifically, the obtained bioelectrical signals of the meridians are recorded as S1, S2, ... S 12 Wherein, S1~S3 represent the bioelectrical signals of the Lung Meridian of Hand-Taiyin, Pericardium Meridian of Hand-Jueyin, and Heart Meridian of Hand-Shaoyin, respectively; S4~S6 represent the bioelectrical signals of the Large Intestine Meridian of Hand-Yangming, Triple Energizer Meridian of Hand-Shaoyang, and Small Intestine Meridian of Hand-Taiyang, respectively; and S7~S9 represent the bioelectrical signals of the Spleen Meridian of Foot-Taiyin, Liver Meridian of Foot-Jueyin, and Kidney Meridian of Foot-Shaoyin, respectively. 10 ~S 12 These represent the bioelectrical signals of the Stomach Meridian of Foot Yangming, the Gallbladder Meridian of Foot Shaoyang, and the Bladder Meridian of Foot Taiyang, respectively.

[0029] In this embodiment, the bioelectric signal is one of the following: biocurrent value, biopressure value, or resistance value.

[0030] The data processing device is used to determine the characterization coefficient of the subject's meridian data based on the difference between the bioelectric signals of each meridian and the corresponding standard bioelectric signals; and to obtain the subject's difference coefficient based on the mean of the bioelectric signals of all meridians and the mean of all standard bioelectric signals.

[0031] Specifically, taking 12 types of meridians as an example, the process of obtaining the characterization coefficients in this embodiment is as follows: The difference between the bioelectrical signals of each meridian and the corresponding standard bioelectrical signals is obtained, specifically: , , ..., ,… ; Based on each difference, the characterization coefficients are calculated; Among them, the characterization coefficient α The expression is: in, For the first k The difference corresponding to each meridian, i.e. S k For the first k The bioelectrical signal corresponding to each meridian, S k 0 For the first k The standard bioelectrical signals corresponding to each meridian n The number of meridians. n =12.

[0032] In this embodiment, the difference coefficient is the ratio of the difference between the mean of all meridian bioelectrical signals and the mean of all standard bioelectrical signals to the mean of all standard bioelectrical signals; specifically, the difference coefficient... β The expression is: Where S is the mean of the bioelectrical signals of all meridians, and S0 is the mean of the standard bioelectrical signals of all meridians.

[0033] It should be noted that the aforementioned standard bioelectric signal is obtained by randomly acquiring the bioelectric signals of each meridian from several normal, healthy individuals, and then using the average value of the bioelectric signals of each meridian as the standard bioelectric signal for that meridian. Alternatively, as another implementation method, the bioelectric signal of the meridian from only one normal, healthy individual can be used as the standard bioelectric signal for that meridian.

[0034] The standard bioelectric signals mentioned above can be obtained by selecting bioelectric signal data of each meridian from historical data of a normal healthy person, or by measuring the meridian bioelectric signals of a randomly selected normal healthy person under the same environmental conditions as the subject. The normal healthy person's body type, age, and gender should be as consistent as possible to avoid the influence of other external factors on the results. This is because factors such as season, gender, and age all affect the meridians; therefore, when determining the standard bioelectric signals, the age, gender, body type, and monitoring time of the normal healthy person and the subject should be kept as consistent as possible to reduce the influence of external factors and improve the accuracy of the judgment.

[0035] Furthermore, even if we try to keep the age, gender, body type, and monitoring time of normal healthy individuals and test subjects consistent as much as possible, differences between different test subjects and normal healthy individuals are inevitable. Therefore, in order to eliminate errors, in this embodiment, the obtained differences are corrected. The specific process is as follows: The physical characteristics and age of the test subjects and normal healthy individuals were obtained separately; the physical characteristics included, but were not limited to, height and weight. The difference in body shape characteristics between the test subject and normal healthy individuals will be calculated, and the ratio of this difference to the body shape characteristics of normal healthy individuals will be used as the first adjustment coefficient. Calculate the difference in age between the test subject and a normal healthy person, and use the ratio of this difference to the age of a normal healthy person as the second adjustment coefficient; The product of the first adjustment coefficient and the second adjustment coefficient is used as the compensation adjustment coefficient for the difference. The product of the compensation adjustment coefficient and the difference is calculated as the compensation adjustment value. The sum of the compensation adjustment value and the difference is used as the corrected difference.

[0036] Based on the corrected difference, the characterization coefficient in this embodiment is: in, For the first k The difference corresponding to each meridian, i.e. S k For the first k The bioelectrical signal corresponding to each meridian, S k 0 For the first k The standard bioelectrical signals corresponding to each meridian n δ represents the number of meridians, and δ is the compensation and adjustment coefficient.

[0037] The body shape features mentioned above can be weight or height; or they can be both. Specifically, taking weight as an example, only the subject's weight and the standard weight of a normal healthy person are calculated to obtain the difference value and the first adjustment coefficient. When the body shape features are weight and height, weight and height can be allocated according to weights, and after feature fusion, a body shape feature can be obtained, and then the corresponding difference value can be obtained.

[0038] The status analysis device is used to classify the current health status data of the subject based on the characterization coefficient and the difference coefficient.

[0039] The specific process of analyzing the current health status data of the subject in this embodiment is as follows: By comparing the characterization coefficient with the first and second thresholds, and by comparing the difference coefficient with the third and fourth thresholds, the current category of the test subject is determined. Specifically: If the characterization coefficient is less than the second threshold and the difference coefficient is less than the fourth threshold, then the current subject is considered normal. When the characterization coefficient is greater than the second threshold and less than the first threshold, and the difference coefficient is less than the fourth threshold, the current subject belongs to the category of Yin Shi; where the first threshold is greater than the second threshold. When the characterization coefficient is greater than the first threshold and the difference coefficient is less than the fourth threshold, the current subject is suspected of having a disease. When the characterization coefficient is less than the second threshold, and the difference coefficient is greater than the fourth threshold but less than the third threshold, the current subject belongs to Yang deficiency; where the fourth threshold is less than the third threshold. When the characterization coefficient is greater than the second threshold and less than the first threshold, and the difference coefficient is greater than the fourth threshold and less than the third threshold, the current test subject belongs to the category of Yin-Yang deficiency; When the characterization coefficient is greater than the first threshold and the difference coefficient is greater than the fourth threshold and less than the third threshold, the current subject is suspected of having a disease; when the characterization coefficient is less than the second threshold and the difference coefficient is greater than the third threshold, the current subject is suspected of having a disease. When the characterization coefficient is greater than the second threshold and less than the first threshold, and the difference coefficient is greater than the third threshold, the current subject is considered to have Yin deficiency; when the characterization coefficient is greater than the first threshold and the difference coefficient is greater than the third threshold, the current subject is suspected of having a disease.

[0040] In this embodiment, the first threshold is 0.6, the second threshold is 0.3, the third threshold is 0.6, and the fourth threshold is 0.2. Table 1 shows the classification results for the current subject. α< 0.3 and β< A value of 0.2 indicates the subject is considered normal; a value of 0.3 indicates the subject is considered normal. <α< 0.6 and β< When the value is 0.2, the current subject is considered to be in a state of "yin excess"; when... α> 0.6 and β< When the value is 0.2, the person being tested is suspected of having a disease; when... α< 0.3 and 0.2 <β< At 0.6, the subject is considered to have Yang deficiency; at 0.3... <α< 0.6 and 0.2 <β< At 0.6, the current subject is deficient in both Yin and Yang; when α> 0.6 and 0.2 <β< At 0.6, the current subject is suspected of having an illness; when α< 0.3 and β> At a value of 0.6, the subject is suspected of having a disease; at 0.3...<α< 0. and β> If the value is 0.6, then the person being tested has Yin deficiency; when... α> 0.6 and β> If the value is 0.6, the person being tested is suspected of having a disease.

[0041] Table 1 For example, taking the current test subject A and test subject B as an example, such as Figure 2 As shown, the characterization coefficients α The vertical axis represents the coefficient of difference. β A coordinate system is established with the horizontal axis as the coordinate system, where the subject A's coordinates are (0.65, 0.65). α> 0.6 and β> Within the interval of 0.6, subject A falls into the category of suspected illness, while subject B's coordinates (0.25, 0.4) are located at 0.3. <α< 0.6 and 0.2 <β< Within the range of 0.6, subject B belongs to the category of Yin-Yang deficiency; thus, the doctor can diagnose the disease or treat the symptoms according to the category.

[0042] In this embodiment, the subjects are classified according to the difference coefficient and the characterization coefficient, which can quickly achieve the preliminary classification of the subjects and provide data support for doctors to conduct health assessments of the subjects.

[0043] Figure 3 This is a flowchart of the steps of the health status analysis method based on multidimensional bioelectric signals in this embodiment.

[0044] In another aspect of the invention, a method for analyzing health status based on multidimensional bioelectrical signals is also provided, such as... Figure 3 As shown, it includes the following steps: Obtain the bioelectrical signals of each meridian of the subject and the standard bioelectrical signals of each meridian of a normal healthy person; The characteristic coefficients of the meridians of the test subject are determined based on the difference between the bioelectric signals of each meridian and the corresponding standard bioelectric signals. The difference coefficient of the subject is obtained by taking the mean of the bioelectrical signals of all meridians and the mean of all standard bioelectrical signals; the health status data of the current subject is classified based on the characterization coefficient and the difference coefficient.

[0045] It should be noted that since the methods in each step of this embodiment use the same technical solution as the device embodiments in the above embodiments, the relevant content will not be described in detail here.

[0046] Figure 4This is a structural block diagram of the health status analysis system based on multi-dimensional bioelectrical signals according to this embodiment. In another aspect of the invention, a health status analysis system based on multi-dimensional bioelectrical signals is also provided, comprising: a processor; and a memory storing computer instructions for health status analysis based on multi-dimensional bioelectrical signals, wherein when the computer instructions are executed by the processor, the device performs the health status analysis method based on multi-dimensional bioelectrical signals according to one or more of the preceding embodiments.

[0047] like Figure 4 As shown, device 501 in the system may include CPU 5011, which may be a general-purpose CPU, a dedicated CPU, or other information processing and program execution unit. Furthermore, device 501 may also include a mass storage device 5012 and a read-only memory (ROM) 5013. The mass storage device 5012 may be configured to store various types of data and programs required for multimedia networks, while the ROM 5013 may be configured to store data required for power-on self-test of device 501, initialization of various functional modules in the system, drivers for basic input / output of the system, and data required to boot the operating system.

[0048] Furthermore, device 501 also includes other hardware platforms or components, such as the TPU (Tensor Processing Unit) 5014, GPU (Graphics Processing Unit) 5015, FPGA (Field Programmable Gate Array) 5016, and MLU (Memory Logic Unit) 5017 shown. It is understood that although various hardware platforms or components are shown in device 501, they are merely exemplary and not limiting, and those skilled in the art can add or remove corresponding hardware as needed. For example, device 501 may include only a CPU as a known hardware platform and another hardware platform as the test hardware platform of this invention.

[0049] The device 501 of the present invention also includes a communication interface 5018, through which it can connect to a local area network / wireless local area network (LAN / WLAN) 505, and further connect to a local server 506 or the Internet 507 via the LAN / WLAN. Alternatively or additionally, the device 501 of the present invention can also directly connect to the Internet or a cellular network via the communication interface 5018 based on wireless communication technology, such as third-generation ("3G"), fourth-generation ("4G"), or fifth-generation ("5G") wireless communication technology. In some application scenarios, the device 501 of the present invention can also access a server 508 on an external network and, possibly, a database 509, as needed.

[0050] Peripherals of device 501 may include a display device 502, an input device 503, and a data transfer interface 504. In one embodiment, the display device 502 may include, for example, one or more speakers and / or one or more visual displays. The input device 503 may include, for example, a keyboard, mouse, microphone, gesture capture camera, or other input buttons or controls configured to receive data input or user commands. The data transfer interface 504 may include, for example, a serial interface, parallel interface, or Universal Serial Bus interface (“USB”), Small Computer System Interface (“SCSI”), Serial ATA, FireWire (“FireWire”), PCI Express, and High Definition Multimedia Interface (“HDMI”), configured for data transfer and interaction with other devices or systems.

[0051] The CPU 5011, mass storage 5012, read-only memory ROM 5013, TPU 5014, GPU 5015, FPGA 5016, MLU 5017, and communication interface 5018 of the device 501 of the present invention can be interconnected via bus 5019, and can interact with peripheral devices through this bus. In one embodiment, the CPU 5011 can control other hardware components in the device 501 and its peripheral devices through this bus 5019.

[0052] In operation, the processor CPU 5011 of the device 501 of the present invention can acquire media data packets through the input device 503 or the data transmission interface 504, and retrieve computer program instructions or code stored in the memory 5012 to process the health status analysis based on multi-dimensional bioelectric signals.

[0053] As can be seen from the above description of the modular design of this invention, the system of this invention can be flexibly arranged according to application scenarios or needs, and is not limited to the architecture shown in the accompanying drawings. Furthermore, it should be understood that any module, unit, component, server, computer, or device performing operations in the examples of this invention may include or otherwise access computer-readable media, such as storage media, computer storage media, or data storage devices (removable) and / or non-removable) such as disks, optical discs, or magnetic tapes. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data.

[0054] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A health status analysis device based on multi-dimensional bioelectrical signals, characterized in that, include: The data acquisition module is used to acquire the bioelectrical signals of each meridian of the subject and the standard bioelectrical signals of each meridian of a normal healthy person. The data processing module is used to determine the characterization coefficient of the subject's meridian data based on the difference between the bioelectric signals of each meridian and the standard bioelectric signals of the corresponding meridian; and to obtain the subject's difference coefficient based on the mean of the bioelectric signals of all meridians and the mean of all standard bioelectric signals. The status analysis module is used to classify the current health status data of the test subject based on the characterization coefficient and the difference coefficient. The characterization coefficient is: in, For the first k The difference corresponding to each meridian, i.e. S k For the first k The bioelectrical signal corresponding to each meridian, S k 0 For the first k The standard bioelectrical signals corresponding to each meridian n δ represents the number of meridians, and δ is the compensation and adjustment coefficient. The difference coefficient is: Where S is the mean of the bioelectrical signals of all meridians, and S0 is the mean of the standard bioelectrical signals of all meridians.

2. The health status analysis device based on multi-dimensional bioelectrical signals according to claim 1, characterized in that, It also includes a correction process for the difference: The physical characteristics and age of the test subjects and normal healthy individuals were obtained separately; the physical characteristics included, but were not limited to, height and weight. The difference in body shape characteristics between the test subject and normal healthy individuals will be calculated, and the ratio of this difference to the body shape characteristics of normal healthy individuals will be used as the first adjustment coefficient. Calculate the difference in age between the test subject and a normal healthy person, and use the ratio of this difference to the age of a normal healthy person as the second adjustment coefficient; The product of the first adjustment coefficient and the second adjustment coefficient is used as the compensation adjustment coefficient for the difference. The product of the compensation adjustment coefficient and the difference is calculated as the compensation adjustment value. The sum of the compensation adjustment value and the difference is used as the corrected difference.

3. The health status analysis device based on multi-dimensional bioelectrical signals according to claim 1, characterized in that, The process of classifying the current subject's health status data is as follows: By comparing the characterization coefficient with the first and second thresholds, and by comparing the difference coefficient with the third and fourth thresholds, the current classification of the subject's health status data is determined. Specifically: If the characterization coefficient is less than the second threshold and the difference coefficient is less than the fourth threshold, then the current subject is considered normal. When the characterization coefficient is greater than the second threshold and less than the first threshold, and the difference coefficient is less than the fourth threshold, the current subject belongs to the category of Yin Shi; where the first threshold is greater than the second threshold. When the characterization coefficient is greater than the first threshold and the difference coefficient is less than the fourth threshold, the current subject is suspected of having a disease. When the characterization coefficient is less than the second threshold, and the difference coefficient is greater than the fourth threshold but less than the third threshold, the current subject belongs to Yang deficiency; where the fourth threshold is less than the third threshold. When the characterization coefficient is greater than the second threshold and less than the first threshold, and the difference coefficient is greater than the fourth threshold and less than the third threshold, the current test subject belongs to the category of Yin-Yang deficiency; When the characterization coefficient is greater than the first threshold and the difference coefficient is greater than the fourth threshold but less than the third threshold, the current subject is suspected of having a disease. When the characterization coefficient is less than the second threshold and the difference coefficient is greater than the third threshold, the current subject is suspected of having a disease. When the characterization coefficient is greater than the second threshold and less than the first threshold, and the difference coefficient is greater than the third threshold, then the current subject belongs to Yin deficiency. When the characterization coefficient is greater than the first threshold and the difference coefficient is greater than the third threshold, the current subject is suspected of having a disease.

4. The health status analysis device based on multi-dimensional bioelectrical signals according to claim 1, characterized in that, The bioelectric signal is one of the following: biocurrent value, biopressure value, or resistance value.

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