Health state detection method, system and equipment and storage medium

Abstract

The embodiment of the invention provides a health state detection method, system and device and a storage medium. The method comprises the steps that excitation current is applied to an excitation electrode; performing bioelectrical impedance measurement on the to-be-measured object through the first measurement electrode array and the second measurement electrode array, and determining a first bioelectrical impedance matrix corresponding to the first measurement electrode array and a second bioelectrical impedance matrix corresponding to the second measurement electrode array; determining a target deviation matrix based on the difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix; and determining a health state detection result of the to-be-detected object based on the target deviation matrix. According to the embodiment of the invention, the accuracy of health state detection results can be improved.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of state detection technologies, and particularly relates to a health state detection method, system, device, and storage medium. Background Art

[0002] Bioelectrical impedance measurement is a detection technology that uses the electrical properties of biological tissues and organs and their variation laws to extract biomedical information related to health status.

[0003] In related technologies, traditional bioelectrical impedance measurement methods usually measure bioelectrical impedance data of specific parts of the human body and perform health status analysis based on these bioelectrical impedance data. However, since local measurement is difficult to comprehensively reflect the overall physiological characteristics of the object to be measured, the accuracy of the health status detection result is relatively low. Summary of the Invention

[0004] The following is an overview of the subject matter described in detail in this article. This overview is not intended to limit the scope of protection of the claims.

[0005] Embodiments of this application provide a health state detection method, system, device, and storage medium, which can improve the accuracy of health state detection results.

[0006] To achieve the above object, a first aspect of the embodiments of the present application provides a health status detection method, which is applied to a health status detection system. The health status detection system includes a detection device, an excitation electrode, a first measurement electrode array, and a second measurement electrode array. The arrangement structures of the first measurement electrode array and the second measurement electrode array are the same. The excitation electrode, the first measurement electrode array, and the second measurement electrode array are respectively electrically connected to the detection device. The excitation electrode contacts the abdominal area of the object to be measured. The first measurement electrode array contacts the left half body area of the object to be measured. The second measurement electrode array contacts the right half body area of the object to be measured. The first measurement electrode array and the second measurement electrode array are symmetric about the median sagittal plane of the object to be measured. The health status detection method includes: applying an excitation current to the excitation electrode; respectively performing bioelectrical impedance measurements on the object to be measured through the first measurement electrode array and the second measurement electrode array to determine a first bioelectrical impedance matrix corresponding to the first measurement electrode array and a second bioelectrical impedance matrix corresponding to the second measurement electrode array, where each element in the first bioelectrical impedance matrix is the bioelectrical impedance measurement result of the measurement electrode corresponding to the first measurement electrode array, and each element in the second bioelectrical impedance matrix is the bioelectrical impedance measurement result of the measurement electrode corresponding to the second measurement electrode array; determining a target deviation matrix based on the difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix; and determining a health status detection result of the object to be measured based on the target deviation matrix.

[0007] In some embodiments, the number of the target deviation matrices is multiple, and each of the target deviation matrices is associated with and records a corresponding excitation frequency. The excitation frequencies corresponding to at least two of the target deviation matrices are different. The excitation frequency is the frequency of the excitation current. The determining the health status detection result of the object to be measured based on the target deviation matrix includes: processing each of the target deviation matrices based on the excitation frequency corresponding to each of the target deviation matrices to obtain an impedance statistical parameter, an impedance distribution parameter, and an impedance characteristic parameter of the object to be measured, where the impedance statistical parameter is determined through statistical analysis processing, the impedance distribution parameter is determined through spatial feature extraction processing, and the impedance characteristic parameter is determined through impedance spectrum analysis processing; and inputting the impedance statistical parameter, the impedance distribution parameter, and the impedance characteristic parameter into a health status detection model for prediction to determine the health status detection result of the object to be measured.

[0008] In some embodiments, the health status detection model includes a first detection sub-model, a second detection sub-model, and a third detection sub-model. The method of inputting the impedance statistical parameter, the impedance distribution parameter, and the impedance characteristic parameter into the health status detection model for prediction to determine the health status detection result of the object to be measured includes: inputting the impedance statistical parameter into the first detection sub-model for prediction to obtain a first detection result; inputting the impedance distribution parameter into the second detection sub-model for prediction to obtain a second detection result; inputting the impedance characteristic parameter into the third detection sub-model for prediction to obtain a third detection result; and fusing the first detection result, the second detection result, and the third detection result to determine the health status detection result of the object to be measured.

[0009] In some embodiments, the method of fusing the first detection result, the second detection result, and the third detection result to determine the health status detection result of the object to be measured includes: obtaining the physiological parameter and the physical activity parameter of the object to be measured; inputting the physiological parameter and the physical activity parameter into a weight prediction model for prediction to determine the target weights corresponding to the first detection result, the second detection result, and the third detection result; and based on the target weights, performing weighted fusion on the first detection result, the second detection result, and the third detection result to determine the health status detection result of the object to be measured.

[0010] In some embodiments, the method of determining the target deviation matrix based on the difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix includes: determining a first deviation matrix based on the difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix; and performing attention processing on the first deviation matrix to obtain the target deviation matrix.

[0011] In some embodiments, the method of performing attention processing on the first deviation matrix to obtain the target deviation matrix includes: extracting features from the first deviation matrix to obtain local spatial features; performing convolution on the local spatial features and a learnable parameter matrix and then performing activation processing to obtain spatial attention weights; based on the spatial attention weights, performing spatial attention processing on the first deviation matrix to obtain a second deviation matrix; and performing self-attention processing on the second deviation matrix to obtain the target deviation matrix.

[0012] To achieve the above object, a second aspect of the embodiments of the present application provides a health status detection system, including a detection device, an excitation electrode, a first measurement electrode array, and a second measurement electrode array. The arrangement structures of the first measurement electrode array and the second measurement electrode array are the same. The excitation electrode, the first measurement electrode array, and the second measurement electrode array are respectively electrically connected to the detection device. The excitation electrode contacts the abdominal area of the object to be measured, the first measurement electrode array contacts the left half body area of the object to be measured, the second measurement electrode array contacts the right half body area of the object to be measured. The first measurement electrode array and the second measurement electrode array are symmetric about the median sagittal plane of the object to be measured. The detection device is configured to execute the health status detection method described in the first aspect above.

[0013] In some embodiments, the first measurement electrode array includes a first flexible thin film layer, a metal conductive layer, and a second flexible thin film layer. The lower side of the metal conductive layer is bonded to the upper side of the first flexible thin film layer, and the lower side of the second flexible thin film layer is bonded to the upper side of the metal conductive layer. The metal conductive layer includes a plurality of measurement electrodes and metal wires corresponding to each of the measurement electrodes. Each of the measurement electrodes is electrically connected to the detection device through the corresponding metal wire. The second flexible thin film layer is provided with through holes corresponding to each of the measurement electrodes. The first measurement electrode array further includes flexible conductive heads corresponding to each of the measurement electrodes. Each of the flexible conductive heads is bonded at the corresponding through hole, and each of the flexible conductive heads is electrically connected to the corresponding measurement electrode.

[0014] To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the health status detection method described in the first aspect above is implemented.

[0015] To achieve the above object, a fourth aspect of the embodiments of the present application provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the health status detection method described in the first aspect above is implemented.

[0016] The embodiments of the present application at least include the following beneficial effects: By applying an excitation current to the excitation electrode through the detection device of the health status detection system, since the excitation electrode is in contact with the abdominal area of the object to be measured, the excitation current flows into the abdominal area of the object to be measured. Also, since the first measurement electrode array is in contact with the left half body area of the object to be measured and the second measurement electrode array is in contact with the right half body area of the object to be measured, the excitation current sequentially passes through the human tissues in the left half body area of the object to be measured and the first measurement electrode array and then enters the detection device. At the same time, the excitation current also sequentially passes through the human tissues in the right half body area of the object to be measured and the second measurement electrode array and then enters the detection device. Then, the detection device can perform bioelectrical impedance measurement on the object to be measured through the first measurement electrode array, obtain the bioelectrical impedance measurement results corresponding to each measurement electrode in the first measurement electrode array, thereby determining the first bioelectrical impedance matrix corresponding to the first measurement electrode array. It can also perform bioelectrical impedance measurement on the object to be measured through the second measurement electrode array, obtain the bioelectrical impedance measurement results corresponding to each measurement electrode in the second measurement electrode array, thereby determining the second bioelectrical impedance matrix corresponding to the second measurement electrode array. Then, based on the difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix, the target deviation matrix is determined. Since the first measurement electrode array and the second measurement electrode array are symmetric about the median sagittal plane of the object to be measured, the target deviation matrix can characterize the overall physiological characteristics of the left and right half bodies of the object to be measured. Therefore, determining the health status detection result based on the target deviation matrix can improve the accuracy of the health status detection result.

[0017] Other features and advantages of the present application will be described in the subsequent description. And, partly, it will become obvious from the description, or can be understood by implementing the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures specifically pointed out in the description, the claims, and the drawings. Brief Description of the Drawings

[0018] The drawings are used to provide a further understanding of the technical solutions of the present application, and constitute a part of the description. Together with the embodiments of the present application, they are used to explain the technical solutions of the present application, and do not constitute a limitation to the technical solutions of the present application.

[0019] Figure 1 It is an optional flowchart of the health status detection method provided by the embodiments of the present application;

[0020] Figure 2 It is an optional flowchart of determining the health status detection result provided by the embodiments of the present application;

[0021] Figure 3 It is an optional specific flowchart of determining the health status detection result provided by the embodiments of the present application;

[0022] Figure 4 An optional process schematic diagram of weighted fusion provided by an embodiment of the present application;

[0023] Figure 5 An optional process schematic diagram of obtaining a target deviation matrix provided by an embodiment of the present application;

[0024] Figure 6 An optional process schematic diagram of attention processing provided by an embodiment of the present application;

[0025] Figure 7 An optional structural schematic diagram of a health status detection system provided by an embodiment of the present application;

[0026] Figure 8 An optional explosion schematic diagram of a first measurement electrode array provided by an embodiment of the present application;

[0027] Figure 9 An optional structural schematic diagram of a first measurement electrode array provided by an embodiment of the present application;

[0028] Figure 10 An optional cross-sectional schematic diagram of a first measurement electrode array provided by an embodiment of the present application;

[0029] Figure 11 An optional structural schematic diagram of a detection device provided by an embodiment of the present application;

[0030] Figure 12 An optional electrical connection schematic diagram of one of the first differential ADCs provided by an embodiment of the present application;

[0031] Figure 13 An optional hardware structural schematic diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0032] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0033] It should be noted that in each specific embodiment of the present application, when it comes to performing relevant processing based on data related to the characteristics of the target object, such as target object attribute information or a set of attribute information, the permission or consent of the target object will be obtained first. Moreover, the collection, use, and processing of such data will comply with relevant laws, regulations, and standards. Among them, the target object can be a user. In addition, when the embodiments of the present application need to obtain the target object attribute information, the separate permission or separate consent of the target object will be obtained by means of a pop-up window or jumping to a confirmation page. After clearly obtaining the separate permission or separate consent of the target object, the necessary data related to the target object for the normal operation of the embodiments of the present application will be obtained.

[0034] In the description of the present application, the meaning of "several" is one or more, the meaning of "multiple" is two or more, "greater than", "less than", "exceeding", etc. are understood as not including the present number, and "above", "below", "within", etc. are understood as including the present number.

[0035] It should be noted that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the flowchart. Terms such as "first", "second", etc. in the specification, claims, or the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0036] To facilitate the understanding of the technical solutions provided by the embodiments of the present application, some key terms used in the embodiments of the present application are explained here:

[0037] Artificial Intelligence (AI) is to use a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, a theory, method, technology, and application system that perceives the environment, acquires knowledge, and uses knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a way similar to human intelligence. Artificial intelligence is also to study the design principles and implementation methods of various intelligent machines to enable the machine to have the functions of perception, reasoning, and decision-making.

[0038] Artificial intelligence technology is an interdisciplinary subject, involving a wide range of fields, including both hardware-level technologies and software-level technologies. The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning, autonomous driving, and intelligent transportation.

[0039] Machine learning (ML) is an interdisciplinary subject involving multiple fields, such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, etc. It specifically studies how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve their own performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent, and its applications cover all fields of artificial intelligence. Machine learning and deep learning usually include technologies such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and learning from demonstration.

[0040] In related technologies, traditional bioelectrical impedance measurement methods usually measure bioelectrical impedance data of specific parts of the human body and perform health status analysis based on these bioelectrical impedance data. However, since local measurements are difficult to comprehensively reflect the overall physiological characteristics of the object to be measured, the accuracy of health status detection results is relatively low.

[0041] To address the problem of relatively low accuracy of health status detection results, this application provides a health status detection method, system, device, and storage medium, which can improve the accuracy of health status detection results.

[0042] The health status detection method, system, device, and storage medium provided in the embodiments of this application will be specifically described through the following embodiments. First, the health status detection method in the embodiments of this application will be described.

[0043] The following will further elaborate on the embodiments of this application with reference to the accompanying drawings.

[0044] As Figure 1 shown, Figure 1 is an optional flowchart of the health status detection method provided in the embodiments of this application. This health status detection method can be applied to a health status detection system, which includes a detection device, excitation electrodes, a first measurement electrode array, and a second measurement electrode array. The arrangement structures of the first measurement electrode array and the second measurement electrode array are the same. The excitation electrodes, the first measurement electrode array, and the second measurement electrode array are respectively electrically connected to the detection device. The excitation electrodes are in contact with the abdominal area of the object to be measured, the first measurement electrode array is in contact with the left half body area of the object to be measured, the second measurement electrode array is in contact with the right half body area of the object to be measured, and the first measurement electrode array and the second measurement electrode array are symmetric about the median sagittal plane of the object to be measured. This health status detection method includes but is not limited to the following steps S110 to step S140:

[0045] Step S110, apply an excitation current to the excitation electrodes;

[0046] Step S120: Perform bioelectrical impedance measurements on the object to be measured through the first measurement electrode array and the second measurement electrode array respectively, and determine the first bioelectrical impedance matrix corresponding to the first measurement electrode array and the second bioelectrical impedance matrix corresponding to the second measurement electrode array;

[0047] Step S130: Determine the target deviation matrix based on the difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix;

[0048] Step S140: Determine the health status detection result of the object to be measured based on the target deviation matrix.

[0049] Among them, each element in the first bioelectrical impedance matrix is the bioelectrical impedance measurement result of the measurement electrode corresponding to the first measurement electrode array, and each element in the second bioelectrical impedance matrix is the bioelectrical impedance measurement result of the measurement electrode corresponding to the second measurement electrode array. The element in the i-th row and j-th column of the first bioelectrical impedance matrix is the bioelectrical impedance measurement result of the measurement electrode in the i-th row and j-th column of the first measurement electrode array. Similarly, the element in the i-th row and j-th column of the second bioelectrical impedance matrix is the bioelectrical impedance measurement result of the measurement electrode in the i-th row and j-th column of the second measurement electrode array, where i and j are positive integers.

[0050] It should be noted that since the arrangement structures of the first measurement electrode array and the second measurement electrode array are the same, the sizes of the first bioelectrical impedance matrix and the second bioelectrical impedance matrix are the same. For example, if both the first measurement electrode array and the second measurement electrode array are composed of 4 rows and 4 columns of measurement electrodes, then both the first bioelectrical impedance matrix and the second bioelectrical impedance matrix are 4-row and 4-column matrices. Therefore, after subtracting the first bioelectrical impedance matrix with the same size from the second bioelectrical impedance matrix, a matrix with the same size can be obtained.

[0051] It is worth noting that the current value of the excitation current is usually very small and within the safe range of the human body. For example, the current value of the excitation current is 1 mA.

[0052] Among them, the median sagittal plane refers to the sagittal plane passing through the midline of the human body. The first measurement electrode array and the second measurement electrode array are symmetric about the median sagittal plane of the object to be measured, which can ensure that the target deviation matrix can indicate the symmetry of the bioelectrical impedance in the left and right half-body regions, and further characterize the overall physiological characteristics of the left and right half-bodies of the object to be measured. For example, the first measurement electrode array is pasted on the skin of the upper limb in the left half-body region of the human body, and the second measurement electrode array is pasted on the skin of the upper limb in the right half-body region of the human body, or the first measurement electrode array is pasted on the skin of the lower limb in the left half-body region of the human body, and the second measurement electrode array is pasted on the skin of the lower limb in the right half-body region of the human body.

[0053] To improve the detection effect, the first measurement electrode array can be in contact with the first acupoint in the left half body area of the object to be measured, and the second measurement electrode array can be in contact with the second acupoint in the right half body area of the object to be measured. The first acupoint and the second acupoint are symmetric acupoints to each other. Exemplarily, the first measurement electrode array is in contact with the Neiguan acupoint in the left half body area of the object to be measured, and the second measurement electrode array is in contact with the Neiguan acupoint in the right half body area of the object to be measured, which can improve the accuracy and reliability of the health status detection result.

[0054] It can be understood that since the excitation electrode is in contact with the abdominal area of the object to be measured, the first measurement electrode array is in contact with the left half body area of the object to be measured, and the second measurement electrode array is in contact with the right half body area of the object to be measured, after applying an excitation current to the excitation electrode, the bioelectrical impedance measurement result of the organ or physiological tissue located in the left half body area of the object to be measured can be measured through the first measurement electrode array, and the bioelectrical impedance measurement result of the organ or physiological tissue located in the right half body area of the object to be measured can be measured through the second measurement electrode array. Also, since both the first measurement electrode array and the second measurement electrode array include multiple measurement electrodes, the bioelectrical impedance measurement results at multiple positions in the left half body area of the object to be measured can be measured simultaneously, and the bioelectrical impedance measurement results at multiple positions in the right half body area of the object to be measured can be measured simultaneously, improving the measurement efficiency and data integrity.

[0055] Based on this, an excitation current is applied to the excitation electrode by the detection device of the health status detection system. Since the excitation electrode is in contact with the abdominal area of the object to be measured, the excitation current flows into the abdominal area of the object to be measured. Also, since the first measurement electrode array is in contact with the left half body area of the object to be measured and the second measurement electrode array is in contact with the right half body area of the object to be measured, the excitation current passes through the human tissue in the left half body area of the object to be measured and the first measurement electrode array in sequence and then enters the detection device. At the same time, the excitation current also passes through the human tissue in the right half body area of the object to be measured and the second measurement electrode array in sequence and then enters the detection device. Then, the detection device can perform bioelectrical impedance measurement on the object to be measured through the first measurement electrode array, obtain the bioelectrical impedance measurement results corresponding to each measurement electrode in the first measurement electrode array, and thus determine the first bioelectrical impedance matrix corresponding to the first measurement electrode array. The detection device can also perform bioelectrical impedance measurement on the object to be measured through the second measurement electrode array, obtain the bioelectrical impedance measurement results corresponding to each measurement electrode in the second measurement electrode array, and thus determine the second bioelectrical impedance matrix corresponding to the second measurement electrode array. Then, based on the difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix, a target deviation matrix is determined. Since the first measurement electrode array and the second measurement electrode array are symmetric about the median sagittal plane of the object to be measured, the target deviation matrix can characterize the overall physiological characteristics of the left and right half bodies of the object to be measured. Therefore, determining the health status detection result of the object to be measured based on the target deviation matrix can improve the accuracy of the health status detection result.

[0056] It should be noted that the form of the health status detection result can be diverse. For example, the form of the health status detection result can be a health score. The higher the health score, the better the health status of the object to be measured, and the lower the health score, the more likely there are health problems with the object to be measured. Another example is that the form of the health status detection result can be a health level. The health status detection result can include health levels such as excellent, medium, and poor, and the health status of the object to be measured is indicated by the health level.

[0057] Specifically, the health status detection result can be determined through a variety of implementation methods. One of the determination methods of the health status detection result is described below.

[0058] In a possible implementation method, referring to Figure 2 , the number of target deviation matrices is multiple, and each target deviation matrix is associated with and records a corresponding excitation frequency. The excitation frequencies corresponding to at least two target deviation matrices are different. The excitation frequency is the frequency of the excitation current. Determining the health status detection result of the object to be measured based on the target deviation matrix includes, but is not limited to, the following steps:

[0059] Step S210: Process each target deviation matrix based on the excitation frequency corresponding to each target deviation matrix to obtain the impedance statistical parameters, impedance distribution parameters, and impedance characteristic parameters of the object under test.

[0060] Step S220: Input the impedance statistical parameters, impedance distribution parameters, and impedance characteristic parameters into the health status detection model for prediction to determine the health status detection result of the object under test.

[0061] Among them, the impedance statistical parameters are determined through statistical analysis processing, the impedance distribution parameters are determined through spatial feature extraction processing, and the impedance characteristic parameters are determined through impedance spectrum analysis processing.

[0062] Among them, the impedance statistical parameters include mean, variance, standard deviation, maximum value, minimum value, range, covariance, correlation analysis, etc. The magnitude of the mean can be used to characterize the degree of bioelectrical impedance deviation between the left and right sides of the human body. Through data model processing and analysis of the degree of bioelectrical impedance deviation, physiological sign information closely related to human health can be obtained, such as cell tissue, body fluid distribution, etc. The maximum value, minimum value, and range can be used to characterize the extreme changes in bioelectrical impedance. Covariance and correlation analysis can be used to characterize the relationship between different data in the target deviation matrix, and the relationship between different data in the target deviation matrix can be used as corroborative data for the physiological sign state of the human body.

[0063] Among them, the impedance distribution parameters can be used to characterize specific physiological states, such as cardiovascular health, fat distribution, etc.; the impedance characteristic parameters can be used to characterize different physiological characteristics, such as water, fat, and muscle content, etc.

[0064] Specifically, alveolar fluid, which is extracellular fluid, can be measured at low frequencies, or the internal situation of cells in the target area can be measured by penetrating the cell membrane at high frequencies. Usually, the excitation frequency needs to be adjusted according to the actual measurement requirements. Exemplarily, the excitation frequency required for body composition analysis can be around 50 kHz, the excitation frequency required for blood impedance detection can be around 100 kHz, and the excitation frequency required for cell activity detection can be around 0.1 kHz to 10 kHz.

[0065] Based on this, the number of target deviation matrices is multiple, and each target deviation matrix is associated with a corresponding excitation frequency recorded. The excitation frequencies corresponding to at least two target deviation matrices are different, which means that bioelectrical impedance measurement needs to be carried out under excitation currents with different excitation frequencies. When carrying out bioelectrical impedance measurement under excitation currents with different excitation frequencies, a comprehensive bioelectrical impedance measurement result of the object to be measured can be obtained. The target deviation matrix determined based on the first measurement electrode array and the corresponding second measurement electrode array can characterize the comprehensive physiological characteristics of the object to be measured; then, by statistically analyzing and processing each target deviation matrix, comprehensive and accurate impedance statistical parameters are obtained; by extracting spatial characteristics of each target deviation matrix, comprehensive and accurate impedance distribution parameters are obtained; by performing impedance spectrum analysis on each target deviation matrix, comprehensive and accurate impedance characteristic parameters are obtained. Inputting the impedance statistical parameters, impedance distribution parameters, and impedance characteristic parameters into the health status detection model for prediction to determine the health status detection result of the object to be measured can further improve the accuracy of the health status detection result.

[0066] In a possible implementation, referring to Figure 3 , the health status detection model includes a first detection sub-model, a second detection sub-model, and a third detection sub-model. Inputting the impedance statistical parameters, impedance distribution parameters, and impedance characteristic parameters into the health status detection model for prediction to determine the health status detection result of the object to be measured includes, but is not limited to, the following steps:

[0067] Step S310, input the impedance statistical parameters into the first detection sub-model for prediction to obtain a first detection result;

[0068] Step S320, input the impedance distribution parameters into the second detection sub-model for prediction to obtain a second detection result;

[0069] Step S330, input the impedance characteristic parameters into the third detection sub-model for prediction to obtain a third detection result;

[0070] Step S340, fuse the first detection result, the second detection result, and the third detection result to determine the health status detection result of the object to be measured.

[0071] Among them, the first detection result, the second detection result, and the third detection result respectively characterize the health status of the object to be measured in different dimensions. The forms of the first detection result, the second detection result, and the third detection result can be diverse. For example, the forms of the first detection result, the second detection result, and the third detection result can all be detection scores. The higher the detection score, the better the health status of the object to be measured, and the lower the detection score, the more likely there are health problems with the object to be measured.

[0072] It should be noted that when the forms of the first detection result, the second detection result, and the third detection result can all be detection scores, the first detection sub-model, the second detection sub-model, and the third detection sub-model can all be trained regression models, which can predict reliable detection scores.

[0073] Based on this, the impedance statistical parameters are predicted by the first detection sub-model to obtain the first detection result; the impedance distribution parameters are predicted by the second detection sub-model to obtain the second detection result; the impedance characteristic parameters are predicted by the third detection sub-model to obtain the third detection result; then the first detection result, the second detection result, and the third detection result are fused, which is equivalent to fusing the detection results of multiple dimensions to comprehensively reflect the overall health status of the object to be measured, so as to obtain the health status detection result of the object to be measured, which can further improve the accuracy of the health status detection result.

[0074] In a possible implementation, referring to Figure 4 , fusing the first detection result, the second detection result, and the third detection result to determine the health status detection result of the object to be measured includes, but is not limited to, the following steps:

[0075] Step S410, obtaining the physiological parameters and physical activity parameters of the object to be measured;

[0076] Step S420, inputting the physiological parameters and physical activity parameters into a weight prediction model for prediction to determine the target weights corresponding to the first detection result, the second detection result, and the third detection result;

[0077] Step S430, based on the target weights, performing weighted fusion on the first detection result, the second detection result, and the third detection result to determine the health status detection result of the object to be measured.

[0078] Based on this, by obtaining the physiological parameters and physical activity parameters of the object to be measured, then predicting the physiological parameters and physical activity parameters through a weight prediction model to determine the target weights corresponding to the first detection result, the second detection result, and the third detection result, and then based on the target weights, performing weighted fusion on the first detection result, the second detection result, and the third detection result to determine the health status detection result of the object to be measured. Since the target weights of each detection result are dynamically adjusted by the physiological parameters and physical activity parameters of the object to be measured, the health status detection result determined by weighted fusion is more in line with the actual physiological state of the object to be measured, can fully reflect the individual differences of the object to be measured, and further improve the accuracy and reliability of the health status detection result.

[0079] Next, another determination method of the health status detection result will be described.

[0080] In a possible implementation manner, determining the health status detection result of the object to be measured based on the target deviation matrix includes: inputting the target deviation matrix into a health index prediction model for prediction to obtain the health index prediction result of the object to be measured, and determining the health status detection result of the object to be measured based on the health index prediction result.

[0081] It can be understood that the health index prediction result may include the body fat percentage, blood glucose level, etc. of the object to be measured. The health index prediction model can be a trained regression model. By predicting to obtain the health index prediction result, the health status detection result of the object to be measured can be effectively determined.

[0082] In a possible implementation manner, referring to Figure 5 , determining the target deviation matrix based on the difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix includes, but is not limited to, the following steps:

[0083] Step S510: Determine the first deviation matrix based on the difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix;

[0084] Step S520: Perform attention processing on the first deviation matrix to obtain the target deviation matrix.

[0085] Based on this, first determine the first deviation matrix based on the difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix, and then perform attention processing on the first deviation matrix to obtain the target deviation matrix. By introducing attention processing, the key information in the first deviation matrix can be learned and focused on, and the interference of irrelevant information can be reduced, thereby optimizing the data quality of the target deviation matrix and further improving the accuracy of the health status detection result.

[0086] In a possible implementation manner, referring to Figure 6 , performing attention processing on the first deviation matrix to obtain the target deviation matrix includes, but is not limited to, the following steps:

[0087] Step S610: Extract features from the first deviation matrix to obtain local spatial features;

[0088] Step S620: Convolve the local spatial features with a learnable parameter matrix and then perform activation processing to obtain spatial attention weights;

[0089] Step S630: Perform spatial attention processing on the first deviation matrix based on the spatial attention weights to obtain a second deviation matrix;

[0090] Step S640: Perform self-attention processing on the second deviation matrix to obtain the target deviation matrix.

[0091] Based on this, local spatial features are obtained by extracting the features of the first deviation matrix, and then the local spatial features are convolved with the learnable parameter matrix and then activated to obtain the spatial attention weights. Based on the spatial attention weights, spatial attention processing is performed on the first deviation matrix to obtain the second deviation matrix. By introducing spatial attention processing, the most representative bioelectrical impedance measurement results in the first deviation matrix can be enhanced, and redundant signals can be suppressed. Then, self-attention processing is performed on the second deviation matrix to obtain the target deviation matrix. By introducing self-attention processing, the correlation between various bioelectrical impedance measurement results in the second deviation matrix can be captured, the key information in the second deviation matrix can be learned and focused on, and the interference of irrelevant information can be reduced, so as to optimize the data quality of the target deviation matrix and further improve the accuracy of the health status detection result.

[0092] In addition, referring to Figure 7 , the present application also provides a health status detection system, including a detection device 701, an excitation electrode 702, a first measurement electrode array 703, and a second measurement electrode array 704. The arrangement structures of the first measurement electrode array 703 and the second measurement electrode array 704 are the same. The excitation electrode 702, the first measurement electrode array 703, and the second measurement electrode array 704 are respectively electrically connected to the detection device 701. The excitation electrode 702 contacts the abdominal area of the object to be measured, the first measurement electrode array 703 contacts the left half body area of the object to be measured, the second measurement electrode array 704 contacts the right half body area of the object to be measured. The first measurement electrode array 703 and the second measurement electrode array 704 are symmetric about the median sagittal plane of the object to be measured. The detection device 701 is used to execute the above-mentioned health status detection method.

[0093] It can be understood that the specific implementation manner of this health status detection system is basically the same as the specific embodiment of the above-mentioned health status detection method, and will not be elaborated here.

[0094] In a possible implementation manner, referring to Figures 8 to 10, the first measurement electrode array 703 includes a first flexible thin film layer 801, a metal conductive layer 802, and a second flexible thin film layer 803. The lower side of the metal conductive layer 802 is adhered to the upper side of the first flexible thin film layer 801, and the lower side of the second flexible thin film layer 803 is adhered to the upper side of the metal conductive layer 802. The metal conductive layer 802 includes a plurality of measurement electrodes 804 and metal wires 805 corresponding to each measurement electrode 804. Each measurement electrode 804 is electrically connected to the detection device through the corresponding metal wire 805. The second flexible thin film layer 803 is provided with through holes 806 corresponding to each measurement electrode 804. The first measurement electrode array 703 further includes flexible conductive heads 807 corresponding to each measurement electrode 804. Each flexible conductive head 807 is adhered to the corresponding through hole 806, and each flexible conductive head 807 is electrically connected to the corresponding measurement electrode 804.

[0095] Based on this, by adhering the first flexible thin film layer 801 to the lower side of the metal conductive layer 802 and adhering the second flexible thin film layer 803 to the upper side of the metal conductive layer 802, the metal conductive layer 802 can be effectively protected. The second flexible thin film layer 803 is provided with through holes 806 corresponding to each measurement electrode 804. Each flexible conductive head 807 is adhered to the corresponding through hole 806, and each flexible conductive head 807 is electrically connected to the corresponding measurement electrode 804. When the first measurement electrode array 703 is in contact with the left half body area of the object to be measured, the flexible conductive heads 807 can be in contact with the contact parts of the object to be measured. Since both the first flexible thin film layer 801 and the second flexible thin film layer 803 have high flexibility and bendability, the first flexible thin film layer 801 and the second flexible thin film layer 803 can be bent to ensure that each flexible conductive head 807 is in full contact with the contact parts of the object to be measured, improving the accuracy and reliability of the bioelectrical impedance measurement of the first measurement electrode array 703.

[0096] It should be noted that the structure of the second measurement electrode 804 array can be the same as that of the first measurement electrode array 703, which can improve the accuracy and reliability of the bioelectrical impedance measurement of the second measurement electrode 804 array.

[0097] In a possible implementation, refer to Figure 11, the detection device 701 includes a main control unit 1101, a first analog-to-digital converter 1102, a second analog-to-digital converter 1103, and a power supply unit 1104. The output end of the first analog-to-digital converter 1102, the output end of the second analog-to-digital converter 1103, and the input end of the power supply unit 1104 are electrically connected to the main control unit 1101 respectively. The output end of the power supply unit 1104 is electrically connected to the excitation electrode 702. The input end of the first analog-to-digital converter 1102 is electrically connected to the first measurement electrode array 703, and the input end of the second analog-to-digital converter 1103 is electrically connected to the second measurement electrode array 704.

[0098] Among them, both the first analog-to-digital converter 1102 and the second analog-to-digital converter 1103 are analog-to-digital converters (ADC).

[0099] Based on this, when applying an excitation current, the main control unit 1101 can control the power supply unit 1104 to apply an excitation current with a specific excitation frequency to the excitation electrode 702. When performing bioelectrical impedance measurement, the first analog-to-digital converter 1102 can collect the voltage signal of the first measurement electrode array 703 and convert it into first voltage data. The main control unit 1101 can receive the first voltage data converted by the first analog-to-digital converter 1102, and then determine the first bioelectrical impedance matrix through the excitation current and the first voltage data. Similarly, the second analog-to-digital converter 1103 can collect the voltage signal of the second measurement electrode array 704 and convert it into second voltage data. The main control unit 1101 can receive the second voltage data converted by the second analog-to-digital converter 1103, and then determine the second bioelectrical impedance matrix through the excitation current and the second voltage data, ensuring the reliability and accuracy of the bioelectrical impedance measurement.

[0100] In a possible implementation manner, the detection device further includes a first return resistor and a second return resistor. The number of the first return resistors can be the same as the number of the measurement electrodes in the first measurement electrode array, and the number of the second return resistors can be the same as the number of the measurement electrodes in the second measurement electrode array. One end of the first return resistor is electrically connected to the corresponding measurement electrode in the first measurement electrode array, and the other end of the first return resistor is electrically connected to the main control unit. One end of the first return resistor is electrically connected to the corresponding measurement electrode in the first measurement electrode array, and the other end of the first return resistor is electrically connected to the main control unit. One end of the second return resistor is electrically connected to the corresponding measurement electrode in the first measurement electrode array, and the other end of the second return resistor is electrically connected to the main control unit;

[0101] The first analog-to-digital converter is the first differential ADC, and the second analog-to-digital converter is the second differential ADC. The number of the first differential ADCs is the same as the number of measurement electrodes in the first measurement electrode array, and the number of the first differential ADCs is the same as the number of measurement electrodes in the second measurement electrode array. The first input terminal of the first differential ADC is electrically connected to the corresponding measurement electrode in the first measurement electrode array, the second input terminal of the first differential ADC is electrically connected to the output terminal of the power supply unit, the first input terminal of the second differential ADC is electrically connected to the corresponding measurement electrode in the second measurement electrode array, and the second input terminal of the second differential ADC is electrically connected to the output terminal of the power supply unit.

[0102] Based on this, by setting the first return resistor and the second return resistor, it can be ensured that the excitation current can flow back to the main control unit after passing through the object to be measured. By using the first differential ADC and the second differential ADC and defining the connection modes of the first differential ADC and the second differential ADC, the potential differences between each measurement electrode and the excitation electrode can be separately collected, thereby improving the reliability of the voltage data.

[0103] Exemplarily, refer to Figure 12 , Figure 12 which is an optional electrical connection schematic diagram of one of the first differential ADCs provided in the embodiments of the present application.

[0104] Among them, the first input terminal of the first differential ADC1201 is electrically connected to the corresponding measurement electrode 804 in the first measurement electrode array. The excitation electrode 702 and the second input terminal of the first differential ADC1201 are respectively electrically connected to the output terminal of the power supply unit 1104. One end of the first return resistor 1202 is electrically connected to the corresponding measurement electrode 804 in the first measurement electrode array, and the other end of the first return resistor 1202 is electrically connected to the main control unit 1101. The main control unit 1101 is also connected to the power supply terminal and the ground terminal of the first differential ADC1201.

[0105] In addition, referring to Figure 13 , Figure 13 shows the hardware structure of an electronic device in another embodiment. The electronic device includes:

[0106] A processor 1301, which can be implemented in a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0107] The memory 1302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 1302 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1302 and are called by the processor 1301 to execute the health status detection method of the embodiments of this application;

[0108] The input / output interface 1303 is used to implement information input and output;

[0109] The communication interface 1304 is used to implement communication interaction between this device and other devices. It can implement communication through a wired method (such as USB, network cable, etc.) or through a wireless method (such as mobile network, WIFI, Bluetooth, etc.);

[0110] The bus 1305 transmits information between various components of the device (such as the processor 1301, the memory 1302, the input / output interface 1303, and the communication interface 1304);

[0111] Among them, the processor 1301, the memory 1302, the input / output interface 1303, and the communication interface 1304 achieve communication connections with each other inside the device through the bus 1305.

[0112] The embodiments of this application also provide a storage medium. The storage medium is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned health status detection method.

[0113] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0114] The embodiments described in the embodiments of the present application are for more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0115] Those skilled in the art can understand that Figures 1 to 6 the technical solutions shown in do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0116] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and their appropriate combinations.

[0118] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0119] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Here, A and B can be singular or plural. The character " / " generally indicates an "or" relationship between the associated objects before and after. "At least one (item) of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0120] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above-mentioned unit division is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms.

[0121] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] In addition, each functional unit in various embodiments of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0123] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media that can store programs, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0124] The preferred embodiments of the embodiments of this application have been described above with reference to the accompanying drawings, which does not limit the scope of the rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall be within the scope of the rights of the embodiments of this application.

Claims

1. A health status detection method, characterized in that: The invention is applied to a health status detection system, the health status detection system comprises a detection device, an excitation electrode, a first measuring electrode array and a second measuring electrode array, the arrangement structure of the first measuring electrode array and the second measuring electrode array are consistent, the excitation electrode, the first measuring electrode array and the second measuring electrode array are electrically connected to the detection device respectively, the excitation electrode contacts the abdominal area of ​​the object to be detected, the first measuring electrode array contacts the left half of the body area of ​​the object to be detected, the second measuring electrode array contacts the right half of the body area of ​​the object to be detected, the first measuring electrode array and the second measuring electrode array are symmetrical about the median sagittal plane of the object to be detected, and the health status detection method comprises: applying an excitation current to the excitation electrode; Performing bioimpedance measurement on the object to be measured by respectively using the first measuring electrode array and the second measuring electrode array, and determining a first bioimpedance matrix corresponding to the first measuring electrode array and a second bioimpedance matrix corresponding to the second measuring electrode array, wherein each element in the first bioimpedance matrix is ​​a bioimpedance measurement result of the measuring electrodes corresponding to the first measuring electrode array, and each element in the second bioimpedance matrix is ​​a bioimpedance measurement result of the measuring electrodes corresponding to the second measuring electrode array; determining a target deviation matrix based on a difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix; A health status detection result of the object to be detected is determined based on the target deviation matrix.

2. The health status detection method according to claim 1, characterized in that: There are multiple target deviation matrices, each of which is associated with a corresponding excitation frequency, and at least two target deviation matrices correspond to different excitation frequencies, the excitation frequency being the frequency of the excitation current. The health status detection result of the object to be tested is determined based on the target deviation matrix, including: Based on the excitation frequency corresponding to each of the target deviation matrices, each of the target deviation matrices is processed to obtain impedance statistical parameters, impedance distribution parameters and impedance characteristic parameters of the object to be measured, wherein the impedance statistical parameters are determined by statistical analysis processing, the impedance distribution parameters are determined by spatial feature extraction processing, and the impedance characteristic parameters are determined by impedance spectrum analysis processing; The impedance statistical parameter, the impedance distribution parameter and the impedance characteristic parameter are input into a health status detection model for prediction to determine a health status detection result of the object to be detected.

3. The health status detection method according to claim 2, characterized in that: The health status detection model includes a first detection sub-model, a second detection sub-model and a third detection sub-model, and the impedance statistical parameter, the impedance distribution parameter and the impedance characteristic parameter are input into the health status detection model for prediction to determine the health status detection result of the object to be detected, including: Inputting the impedance statistical parameter into the first detection sub-model for prediction to obtain a first detection result; Inputting the impedance distribution parameter into the second detection sub-model for prediction to obtain a second detection result; Inputting the impedance characteristic parameter into the third detection sub-model for prediction to obtain a third detection result; The first detection result, the second detection result and the third detection result are integrated to determine the health status detection result of the object to be detected.

4. The health status detection method according to claim 3, characterized in that: The fusing the first detection result, the second detection result and the third detection result to determine the health status detection result of the object to be detected includes: Acquiring physiological parameters and physical activity parameters of the subject to be measured; Inputting the physiological parameter and the physical activity parameter into a weight prediction model for prediction, and determining target weights corresponding to the first test result, the second test result, and the third test result; Based on the target weight, the first detection result, the second detection result and the third detection result are weightedly fused to determine the health status detection result of the object to be detected.

5. The health status detection method according to claim 1, characterized in that: The step of determining a target deviation matrix based on a difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix comprises: determining a first deviation matrix based on a difference between the first bioelectrical impedance matrix and the second bioelectrical impedance matrix; Attention processing is performed on the first deviation matrix to obtain a target deviation matrix.

6. The health status detection method according to claim 5, characterized in that: The performing attention processing on the first deviation matrix to obtain a target deviation matrix includes: Performing feature extraction on the first deviation matrix to obtain local spatial features; Convolving the local spatial features with the learnable parameter matrix and performing activation processing to obtain a spatial attention weight; Based on the spatial attention weights, performing spatial attention processing on the first deviation matrix to obtain a second deviation matrix; The second deviation matrix is ​​subjected to self-attention processing to obtain a target deviation matrix.

7. A health status detection system, characterized in that: It includes a detection device, an excitation electrode, a first measuring electrode array and a second measuring electrode array, the arrangement structure of the first measuring electrode array and the second measuring electrode array are consistent, the excitation electrode, the first measuring electrode array and the second measuring electrode array are electrically connected to the detection device respectively, the excitation electrode contacts the abdominal area of ​​the object to be detected, the first measuring electrode array contacts the left half of the body area of ​​the object to be detected, and the second measuring electrode array contacts the right half of the body area of ​​the object to be detected, the first measuring electrode array and the second measuring electrode array are symmetrical about the median sagittal plane of the object to be detected, and the detection device is used to perform the health status detection method described in any one of claims 1 to 6.

8. The health status detection system according to claim 7, characterized in that: The first measuring electrode array includes a first flexible film layer, a metal conductive layer and a second flexible film layer, the lower side of the metal conductive layer is bonded to the upper side of the first flexible film layer, the lower side of the second flexible film layer is bonded to the upper side of the metal conductive layer, the metal conductive layer includes a plurality of measuring electrodes and metal wires corresponding to each of the measuring electrodes, each of the measuring electrodes is electrically connected to the detection device through the corresponding metal wire, the second flexible film layer is provided with a through hole corresponding to each of the measuring electrodes, the first measuring electrode array also includes a flexible conductive head corresponding to each of the measuring electrodes, each of the flexible conductive heads is bonded to the corresponding through hole, and each of the flexible conductive heads is electrically connected to the corresponding measuring electrode.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the health status detection method described in any one of claims 1 to 6 when executing the computer program.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the health status detection method described in any one of claims 1 to 6 is implemented.

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