Physiological data detection method, device, equipment and medium based on human factor intelligence

Abstract

The present application relates to the field of intelligent device technology, and in particular to a physiological data detection method, device, equipment and medium based on human factors intelligence, wherein the method includes: collecting the initial physiological data of the person under test at the current moment; obtaining the actual physiological data at each moment within the first preset time from the current moment, and matching the change trend according to the actual physiological data at each moment; obtaining the reference physiological data at the current moment according to the change trend, and obtaining the actual physiological data at the current moment according to the reference physiological data and the initial physiological data. The embodiment of the present application can monitor the actual physiological data that conforms to the current physiological state of the person under test in real time, effectively avoid the detection influence caused by motion artifacts, improve the accuracy of physiological detection, reduce the restrictions on the normal physiological activities of the person under test, meet the use needs of the person under test, and improve the use experience of the person under test.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent devices, and in particular to a physiological data detection method, device, equipment and medium based on human factors intelligence. Background Art

[0002] In the related art, in the PPG (Photo Plethysmo Graphic) device, after the detector samples the PPG signal, the analog PPG signal will be transmitted to the ADC (Analog-to-Digital Converter) to be converted into a digital quantity. The ADC outputs the digital PPG signal to other control logics such as heart rate, HRV (Heart rate variability) algorithms, etc. In actual applications, the PPG device can be applied to wearable devices to obtain physiological detection data of the person being measured. For example, when used as a fingertip wearable device, the light emitted by the light-emitting part of the PPG device can be reflected by the fingertips, and the intensity of the reflected light can be detected by the light-receiving part. Since blood absorbs light, the intensity of the reflected light will change with the beating of the heart, and PPG information can be obtained by measuring the intensity of the reflected light.

[0003] However, in the related art, the PPG device requires that the detection parts of the person being measured, such as fingers, wrists, etc., need to keep the PPG device in contact with a specific area to complete the detection. When the person being measured is moving, it is difficult for the detection part of the person being measured and the PPG device to remain relatively still, which will affect the PPG signal sampling and produce motion artifacts. This will result in large errors in the detection results, which is not conducive to the person being measured obtaining his or her own physiological data in real time, and needs to be improved. Summary of the invention

[0004] The present application provides a physiological data detection method, device, equipment and medium based on human factors intelligence to solve the technical problem in the related art that it is difficult to ensure that the detection part of the person being measured and the wearable device remain relatively still, resulting in large errors in the obtained detection data and difficulty in reflecting the real physiological state of the person being measured.

[0005] The first aspect of the present application provides a physiological data detection method based on human factors intelligence, comprising the following steps: collecting the initial physiological data of the person being tested at the current moment; obtaining the actual physiological data at each moment within a first preset time length from the current moment, and matching the change trend according to the actual physiological data at each moment; and obtaining the reference physiological data of the current moment according to the change trend, and obtaining the actual physiological data of the current moment based on the reference physiological data and the initial physiological data.

[0006] According to the above-mentioned technical means, the embodiment of the present application can correct the initial physiological data of the person being measured at the current moment, and determine the reference physiological data at the current moment by matching the changing trend of the physiological data, combining the changing trend of the actual physiological data at each moment and the initial physiological data at the current moment, thereby obtaining the actual physiological data at the current moment that is consistent with the current physiological state of the person being measured, so as to monitor the actual physiological data that is consistent with the current physiological state of the person being measured in real time, effectively avoid the detection impact caused by motion artifacts, improve the accuracy of physiological detection, reduce restrictions on the normal physiological activities of the person being measured, meet the use needs of the person being measured, and improve the use experience of the person being measured.

[0007] Exemplarily, matching the change trend according to the actual physiological data at each moment includes: determining a window to be matched according to the actual physiological data at each moment; determining a target window according to the window to be matched with reference to a signal trend reference curve; and determining the change trend according to the target window.

[0008] According to the above technical means, the embodiment of the present application can use the window matching method to match the corresponding change trend according to the signal trend reference curve and the change trend of the actual physiological data at each moment, so as to correct the initial physiological data.

[0009] Exemplarily, before determining the target window based on the window to be matched, it also includes: using a preset signal trend reference curve as the signal trend reference curve; or, generating the signal trend reference curve based on actual physiological data of the person being measured at multiple moments in a static state and maintaining a second preset time length.

[0010] According to the above technical means, the embodiment of the present application can obtain a signal trend reference curve by presetting or by collecting the actual physiological data of the person being measured at rest, so that the signal trend reference curve can be more in line with the physiological data trend of the person being measured, so as to match the change trend.

[0011] Exemplarily, obtaining the actual physiological data at the current moment based on the reference physiological data and the initial physiological data includes: acquiring difference information between the reference physiological data and the initial physiological data; determining a time-varying coefficient based on the difference information, and calculating the actual physiological data based on the time-varying coefficient, the reference physiological data and the initial physiological data.

[0012] According to the above-mentioned technical means, the embodiment of the present application can determine the time-varying coefficient based on the difference information between the reference physiological data and the initial physiological data, so as to obtain the actual physiological data based on the time-varying coefficient, the reference physiological data and the initial physiological data, thereby further improving the accuracy of the actual physiological data, so that the actual physiological data can accurately reflect the actual physiological state of the person being measured.

[0013] Exemplarily, determining the time-varying coefficient according to the difference information includes: using the difference information as an index, querying a preset relationship table to obtain the time-varying coefficient; or calculating the time-varying coefficient according to the difference information and a preset activation function.

[0014] According to the above technical means, the embodiment of the present application can obtain the time-varying coefficient by table lookup or calculation, so as to obtain the actual physiological data using the time-varying coefficient.

[0015] The second aspect of the present application provides a physiological data detection device based on human factors intelligence, including: an acquisition module, used to collect the initial physiological data of the person being tested at the current moment; a matching module, used to obtain the actual physiological data of each moment within a first preset time length from the current moment, and match the change trend according to the actual physiological data at each moment; and a detection module, used to obtain the reference physiological data of the current moment according to the change trend, and obtain the actual physiological data of the current moment based on the reference physiological data and the initial physiological data.

[0016] According to the above-mentioned technical means, the embodiment of the present application can correct the initial physiological data of the person being measured at the current moment, and determine the reference physiological data at the current moment by matching the changing trend of the physiological data, combining the changing trend of the actual physiological data at each moment and the initial physiological data at the current moment, thereby obtaining the actual physiological data at the current moment that is consistent with the current physiological state of the person being measured, so as to monitor the actual physiological data that is consistent with the current physiological state of the person being measured in real time, effectively avoid the detection impact caused by motion artifacts, improve the accuracy of physiological detection, reduce restrictions on the normal physiological activities of the person being measured, meet the use needs of the person being measured, and improve the use experience of the person being measured.

[0017] Exemplarily, the matching module includes: a determination unit, used to determine the window to be matched based on the actual physiological data at each moment; a matching unit, used to refer to the signal trend reference curve and determine the target window based on the window to be matched; and an identification unit, used to determine the change trend based on the target window.

[0018] According to the above technical means, the embodiment of the present application can use the window matching method to match the corresponding change trend according to the signal trend reference curve and the change trend of the actual physiological data at each moment, so as to correct the initial physiological data.

[0019] Exemplarily, it also includes: a first generating module, used to use a preset signal trend reference curve as the signal trend reference curve; or, a second generating module, used to generate the signal trend reference curve based on actual physiological data of the person being measured at multiple moments in a static state and maintaining a second preset time length.

[0020] According to the above technical means, the embodiment of the present application can obtain a signal trend reference curve by presetting or by collecting the actual physiological data of the person being measured at rest, so that the signal trend reference curve can be more in line with the physiological data trend of the person being measured, so as to match the change trend.

[0021] Exemplarily, the detection module includes: an acquisition unit, used to acquire difference information between the reference physiological data and the initial physiological data; a calculation unit, used to determine a time-varying coefficient based on the difference information, and calculate the actual physiological data based on the time-varying coefficient, the reference physiological data and the initial physiological data.

[0022] According to the above-mentioned technical means, the embodiment of the present application can determine the time-varying coefficient based on the difference information between the reference physiological data and the initial physiological data, so as to obtain the actual physiological data based on the time-varying coefficient, the reference physiological data and the initial physiological data, thereby further improving the accuracy of the actual physiological data, so that the actual physiological data can accurately reflect the actual physiological state of the person being measured.

[0023] Exemplarily, the calculation unit includes: a query subunit, used to query a preset relationship table with the difference information as an index to obtain the time-varying coefficient; or a calculation subunit, used to calculate the time-varying coefficient according to the difference information and a preset activation function.

[0024] According to the above technical means, the embodiment of the present application can obtain the time-varying coefficient by table lookup or calculation, so as to obtain the actual physiological data using the time-varying coefficient.

[0025] The third aspect of the present application provides a wearable device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the physiological data detection method based on human factors intelligence as described in the above embodiment.

[0026] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned physiological data detection method based on human factors intelligence.

[0027] The embodiment of the present application can correct the initial physiological data of the person being measured at the current moment, and determine the reference physiological data at the current moment by matching the change trend of the physiological data, combining the change trend of the actual physiological data at each moment and the initial physiological data at the current moment, so as to obtain the actual physiological data at the current moment that meets the current physiological state of the person being measured, improve the accuracy of the actual physiological data, reduce the restrictions on the normal physiological activities of the person being measured, meet the use needs of the person being measured, and improve the use experience of the person being measured. Thus, the technical problem in the related technology that it is difficult to ensure that the detection part of the person being measured and the wearable device remain relatively still, so that the obtained detection data has a large error and it is difficult to reflect the true physiological state of the person being measured is solved.

[0028] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0030] Figure 1 A flowchart of a physiological data detection method based on human factor intelligence provided according to an embodiment of the present application;

[0031] Figure 2 It is a schematic diagram of the principle of a physiological data detection method based on human factor intelligence according to one embodiment of the present application;

[0032] Figure 3 A schematic diagram of the design principle of an activation function according to an embodiment of the present application;

[0033] Figure 4 It is a structural schematic diagram of a physiological data detection device based on human factor intelligence provided according to an embodiment of the present application;

[0034] Figure 5 The present invention is a schematic diagram of the structure of a wearable device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0036] The following describes the wearable device method, device, equipment and medium for detecting physiological data based on human factors intelligence in the embodiment of the present application with reference to the accompanying drawings. In view of the technical problem that it is difficult to ensure that the detection part of the person being measured and the wearable device remain relatively still in the related technology mentioned in the background technology center, so that the obtained detection data has a large error and it is difficult to reflect the true physiological state of the person being measured, the present application provides a physiological data detection method based on human factors intelligence, in which the initial physiological data of the person being measured at the current moment can be corrected, and the reference physiological data at the current moment can be determined by matching the change trend of the physiological data, combining the change trend of the actual physiological data matching at each moment and the initial physiological data at the current moment, so as to obtain the actual physiological data at the current moment that conforms to the current physiological state of the person being measured, so as to monitor the actual physiological data that conforms to the current physiological state of the person being measured in real time, effectively avoid the detection influence caused by motion artifacts, improve the accuracy of physiological detection, reduce the restriction on the normal physiological activity behavior of the person being measured, meet the use needs of the person being measured, and improve the use experience of the person being measured. Thereby, a technical problem in the related technology that it is difficult to ensure that the detection part of the person being measured and the wearable device remain relatively still, resulting in large errors in the obtained detection data and difficulty in reflecting the real physiological state of the person being measured is solved.

[0037] Specifically, Figure 1 A flowchart of a physiological data detection method based on human factors intelligence provided in an embodiment of the present application.

[0038] like Figure 1 As shown, the physiological data detection method based on human intelligence includes the following steps:

[0039] In step S101, the initial physiological data of the person being measured at the current moment is collected.

[0040] It is understandable that human intelligence is based on human factors engineering technology combined with artificial intelligence (AI) technology. Human factors engineering technology is the application of human psychological and physiological principles to the engineering and design of products, processes and systems. In other words, it is the technology of designing and improving the human-machine-environment system according to human characteristics. Combining this technology with artificial intelligence technology can enable machines to interact with people more intelligently. For example, in some scenarios, the physiological state of a person can be identified based on his physiological signals.

[0041] The physiological data detection method based on human intelligence in the embodiment of the present application can realize the detection of the physiological data of the person being measured by the wearable device on the basis of human intelligence, wherein the wearable device involved can realize the collection of the physiological data of the person being measured by utilizing the PPG technology, wherein the PPG technology is a technology that uses a controllable light source (e.g., LED (Light Emitting Diode)) to illuminate the human skin, uses AFE (Analog Front End) to connect PD (Photo-Diode) as a detector, and measures the attenuated light after the light source is reflected and absorbed by the human blood vessels and tissues, thereby recording the pulsation state of the blood vessels and measuring the pulse wave. Since the PPG technology has the characteristics of simple measurement signal acquisition and easy-to-wear measurement device, it has gradually become the main method for measuring blood oxygen, pulse and heart rate under non-hospital conditions.

[0042] For example, when the wearable device is a wrist device, such as a bracelet, wristwatch, smart watch, etc., the person being measured can wear the wearable device on a designated part so that a controllable light source is irradiated on the detection part of the person's body surface, thereby realizing the collection of the initial physiological data of the person being measured at the current moment.

[0043] In step S102, actual physiological data at each moment within a first preset time period from the current moment is acquired, and a change trend is matched according to the actual physiological data at each moment.

[0044] It can be understood that in the related technology, the wearable device can collect and output the physiological data of the person being measured at a certain interval (which can be set by yourself or use the default setting), so that the person being measured can obtain the physiological data of the most recent collection moment through the output physiological data; or determine the changes in the physiological state of the person being measured through the physiological data at multiple collection moments, and output information such as the health analysis and health advice of the person being measured.

[0045] However, due to the motion artifacts caused by the movement of the person being measured, the physiological data output by the wearable device may not necessarily reflect the actual physiological state of the person being measured. Therefore, it is necessary to correct the initial physiological data collected at each moment so that the actual physiological data can be output at each moment.

[0046] Specifically, the embodiment of the present application can obtain the actual physiological data at each moment within a certain time period from the current moment, and match the change trend of the actual physiological data within this time period, such as slope matching, so as to follow up the change trend and determine the actual physiological data at the current moment.

[0047] It should be noted that the first preset duration can be set by those skilled in the art according to actual conditions, and no specific limitation is made here. The actual physiological data at each moment are obtained from multiple actual physiological data within a certain time period before each moment, that is, the actual physiological data at each moment are the output results after the initial physiological data at each moment are corrected, and can accurately reflect the real physiological state of the person being measured at each moment.

[0048] For example, if there is no actual physiological data within a certain period of time at the current moment, or the amount of actual physiological data is small and cannot match the change trend, the embodiment of the present application can remind the person being measured to wait for a certain period of time through the wearable device until sufficient actual physiological data is obtained. In order to ensure the accuracy of the actual physiological data, the embodiment of the present application can also remind the person being measured to remain relatively still with the wearable device during the waiting time through the wearable device.

[0049] Exemplarily, matching the change trend according to the actual physiological data at each moment includes: determining a window to be matched according to the actual physiological data at each moment; determining a target window according to the window to be matched with reference to a signal trend reference curve; and determining the change trend according to the target window.

[0050] like Figure 2 As shown, the embodiment of the present application can generate a signal trend reference curve based on signal data within a period. In this period, K can be set to [k1, k2, k3, k4, k5, k6, k7, k8, k9, k 10 ,k 11 ,k 12 ,k 13 ,k 14 ,k 15 ,k 16 ], where K represents the changing trend within the period, i.e. the slope, k n represents each signal acquisition moment within the cycle, [k1,k2,k3,k4,k5,k6,k7,k8,k9,k 10 ,k 11 ,k 12 ,k 13 ,k 14 ,k 15 ,k 16 ] can form a completion window.

[0051] Furthermore, the embodiment of the present application can determine the change trend of the actual physiological data within a certain period of time according to the actual physiological data at each moment within a certain period of time. m =[k t-6 ,k t-5 ,k t-4 ,kt-3 ,k t-2 ,k t-1 ], where K m is the changing trend of actual physiological data within a certain period of time, k t-n is the output time of the actual physiological data within a certain period of time, [k t-6 ,k t-5 ,k t-4 ,k t-3 ,k t-2 ,k t-1 ] can form a window to be matched.

[0052] The embodiment of the present application can refer to the signal trend reference curve and determine the target window according to the window to be matched, wherein the formula for calculating the match can be as follows:

[0053]

[0054] Based on the above formula, the embodiment of the present application can determine the target window, and then determine the change trend according to the target window, such as finding the extrapolated slope k t_ , and determine the trend of change, such as calculating the measurement slope k mt .

[0055] Exemplarily, before determining the target window based on the window to be matched, it also includes: using a preset signal trend reference curve as a signal trend reference curve; or, generating a signal trend reference curve based on actual physiological data of the person being measured at multiple moments in a static state and maintaining a second preset time length.

[0056] In the actual execution process, the signal trend reference curve can be obtained in two ways:

[0057] First, the embodiment of the present application can use a preset signal trend reference curve as the signal trend reference curve, wherein the preset signal trend reference curve can be obtained by analyzing a large number of samples through big data, and in order to improve the reliability of the signal trend reference curve, different signal trend reference curves can be obtained by distinguishing samples of different genders, ages, body shapes, etc., thereby selecting the best signal trend reference curve from a variety of preset signal trend reference curves based on factors such as the gender, age, body shape, etc. of the actual person being measured.

[0058] Second, the embodiment of the present application can generate a signal trend reference curve based on the actual physiological data of the person being measured at multiple moments in a static state and maintaining a second preset time. That is to say, after the person being measured wears the wearable device, the embodiment of the present application can remind the person being measured to remain static for a certain period of time, and obtain a signal trend reference curve by collecting the actual physiological data at multiple moments in a certain period of time, wherein the second preset time and the change trend within the second preset time are a complete window.

[0059] It should be noted that the second preset duration can be set by those skilled in the art according to actual conditions and is not specifically limited here.

[0060] In step S103, the reference physiological data at the current moment is acquired according to the change trend, and the actual physiological data at the current moment is obtained according to the reference physiological data and the initial physiological data.

[0061] It is understandable that, according to the change trend of the actual physiological data at each moment within the first preset time, the theoretical value of the physiological data at the current moment, i.e., the reference physiological data, can be derived. However, the change trend of the physiological data does not completely conform to the signal trend reference curve, and the actual physiological data is not completely equal to the reference physiological data.

[0062] Therefore, the embodiment of the present application needs to determine the actual physiological data at the current moment based on the reference physiological data and the initial physiological data, so that the actual physiological data can be more consistent with the actual physiological state of the person being measured.

[0063] Exemplarily, actual physiological data at the current moment is obtained based on reference physiological data and initial physiological data, including: obtaining difference information between the reference physiological data and the initial physiological data; determining a time-varying coefficient based on the difference information, and calculating actual physiological data based on the time-varying coefficient, reference physiological data and initial physiological data.

[0064] Specifically, the embodiment of the present application can obtain the difference information between the reference physiological data and the initial physiological data, such as the difference between the extrapolated slope and the measured slope: mt -k t_ , and based on the difference information, the corresponding time-varying coefficient a (between 0 and 1) is obtained, so as to calculate the actual physiological data by combining the time-varying coefficient, the reference physiological data and the initial physiological data.

[0065] For example, the present application can calculate the corrected slope k at the current moment by the following formula: t+ :

[0066] k t+ =k t_ +a(k mt -kt ).

[0067] Based on the corrected slope k at the current moment t+ Calculate the pulse value at the time, where the calculation formula can be:

[0068] p=p0+k t +Δt,

[0069] Among them, p0 is the pulse value at the previous moment, Δt is the time interval between adjacent signal acquisition moments, and k t+ is the slope value after correction at the current moment, and p is the pulse value after correction at the current moment.

[0070] Furthermore, the embodiment of the present application can obtain actual physiological data at the current moment based on the pulse value corrected at the current moment.

[0071] Exemplarily, determining the time-varying coefficient according to the difference information includes: using the difference information as an index, querying a preset relationship table to obtain the time-varying coefficient; or calculating the time-varying coefficient according to the difference information and a preset activation function.

[0072] It can be understood that the time-varying coefficient a and the difference information (k mt -k t ) has a certain relationship, when the difference information (k mt -k t ) is larger, the time-varying coefficient a is smaller.

[0073] In some embodiments, the embodiments of the present application can obtain the time-varying coefficient by table lookup, and use the difference information as an index to query the preset relationship table to obtain the time-varying coefficient. The preset relationship table can be obtained by big data analysis.

[0074] In other embodiments, the present application can calculate the time-varying coefficient based on the difference information and the preset activation function. The preset activation function can be as follows: Figure 3 Design as shown.

[0075] According to the physiological data detection method based on human factors intelligence proposed in the embodiment of the present application, the initial physiological data of the person being tested at the current moment can be corrected, and the reference physiological data at the current moment can be determined by matching the change trend of the physiological data, combining the change trend of the actual physiological data at each moment and the initial physiological data at the current moment, so as to obtain the actual physiological data at the current moment that conforms to the current physiological state of the person being tested, so as to monitor the actual physiological data that conforms to the current physiological state of the person being tested in real time, effectively avoid the detection influence caused by motion artifacts, improve the accuracy of physiological detection, reduce the restrictions on the normal physiological activities of the person being tested, meet the use needs of the person being tested, and improve the use experience of the person being tested. Thus, the technical problem in the related technology that it is difficult to ensure that the detection part of the person being tested and the wearable device remain relatively still, so that the obtained detection data has a large error and it is difficult to reflect the true physiological state of the person being tested is solved.

[0076] Next, a physiological data detection device based on human factors intelligence proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0077] Figure 4 It is a block diagram of a physiological data detection device based on human factors intelligence according to an embodiment of the present application.

[0078] like Figure 4 As shown, the physiological data detection device 10 based on human factors intelligence includes: a collection module 100, a matching module 200 and a detection module 300.

[0079] The acquisition module 100 is used to acquire the initial physiological data of the person being measured at the current moment.

[0080] The matching module 200 is used to obtain the actual physiological data at each moment within a first preset time period from the current moment, and match the change trend according to the actual physiological data at each moment.

[0081] The detection module 300 is used to obtain the reference physiological data at the current moment according to the change trend, and obtain the actual physiological data at the current moment according to the reference physiological data and the initial physiological data.

[0082] Exemplarily, the matching module 200 includes: a determination unit, a matching unit and a recognition unit.

[0083] The determination unit is used to determine the window to be matched according to the actual physiological data at each moment.

[0084] The matching unit is used to determine the target window according to the window to be matched with reference to the signal trend reference curve.

[0085] The identification unit is used to determine the change trend according to the target window.

[0086] Exemplarily, the physiological data detection device 10 based on human factors intelligence further includes: a first generating module or a second generating module.

[0087] Wherein, the first generating module is used to use a preset signal trend reference curve as the signal trend reference curve.

[0088] The second generating module is used to generate a signal trend reference curve according to the actual physiological data of the person under test at multiple moments in a static state and maintained for a second preset time period.

[0089] Exemplarily, the detection module 200 includes: an acquisition unit and a calculation unit.

[0090] The acquisition unit is used to acquire difference information between the reference physiological data and the initial physiological data.

[0091] The calculation unit is used to determine the time-varying coefficient according to the difference information, and calculate the actual physiological data according to the time-varying coefficient, the reference physiological data and the initial physiological data.

[0092] Exemplarily, the computing unit includes: a query subunit or a computing subunit.

[0093] The query subunit is used to query the preset relationship table with the difference information as an index to obtain the time-varying coefficient.

[0094] The computing subunit is used to calculate the time-varying coefficient according to the difference information and the preset activation function.

[0095] It should be noted that the aforementioned explanation of the embodiment of the physiological data detection method based on human intelligence is also applicable to the physiological data detection device based on human intelligence in this embodiment, and will not be repeated here.

[0096] According to the physiological data detection device based on human factors intelligence proposed in the embodiment of the present application, the initial physiological data of the person being tested at the current moment can be corrected, and the reference physiological data at the current moment can be determined by matching the change trend of the physiological data, combining the change trend of the actual physiological data at each moment and the initial physiological data at the current moment, so as to obtain the actual physiological data at the current moment that conforms to the current physiological state of the person being tested, so as to monitor the actual physiological data that conforms to the current physiological state of the person being tested in real time, effectively avoid the detection influence caused by motion artifacts, improve the accuracy of physiological detection, reduce the restrictions on the normal physiological activities of the person being tested, meet the use needs of the person being tested, and improve the use experience of the person being tested. Thus, the technical problem in the related technology that it is difficult to ensure that the detection part of the person being tested and the wearable device remain relatively still, so that the obtained detection data has a large error and it is difficult to reflect the true physiological state of the person being tested is solved.

[0097] Figure 5A schematic diagram of the structure of a wearable device provided in an embodiment of the present application. The wearable device may include:

[0098] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0099] When the processor 502 executes the program, the physiological data detection method based on human factor intelligence provided in the above embodiment is implemented.

[0100] Furthermore, the wearable device also includes:

[0101] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0102] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0103] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0104] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0105] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0106] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0107] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned physiological data detection method based on human factors intelligence is implemented.

[0108] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0109] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0110] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0111] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0112] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0113] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0114] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0115] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A physiological data detection method based on human factor intelligence, characterized in that: The following steps are involved: Collect the initial physiological data of the person being tested at the current moment; Acquire actual physiological data at each moment within a first preset time period from the current moment, and match a change trend according to the actual physiological data at each moment; as well as Acquire the reference physiological data at the current moment according to the change trend, and obtain the actual physiological data at the current moment according to the reference physiological data and the initial physiological data; Among them, obtaining the actual physiological data at the current moment based on the reference physiological data and the initial physiological data includes: obtaining difference information between the reference physiological data and the initial physiological data; determining a time-varying coefficient based on the difference information, and calculating the actual physiological data based on the time-varying coefficient, the reference physiological data and the initial physiological data.

2. The method according to claim 1, characterized in that The matching of the change trend according to the actual physiological data at each moment includes: Determine a window to be matched according to the actual physiological data at each moment; Referring to the signal trend reference curve, determining the target window according to the window to be matched; The change trend is determined according to the target window.

3. The method according to claim 2, characterized in that Before determining the target window according to the window to be matched, the method further includes: Using a preset signal trend reference curve as the signal trend reference curve; Alternatively, the signal trend reference curve is generated based on actual physiological data of the person under test at multiple moments who is in a stationary state and maintains a second preset time period.

4. The method according to claim 1, characterized in that: The determining of the time-varying coefficient according to the difference information includes: Using the difference information as an index, querying a preset relationship table to obtain the time-varying coefficient; Alternatively, the time-varying coefficient is calculated according to the difference information and a preset activation function.

5. A physiological data detection device based on human factor intelligence, characterized in that: include: The acquisition module is used to acquire the initial physiological data of the person being tested at the current moment; A matching module, used to obtain actual physiological data at each moment within a first preset time from the current moment, and match the change trend according to the actual physiological data at each moment; as well as a detection module, configured to obtain reference physiological data at the current moment according to the change trend, and obtain actual physiological data at the current moment according to the reference physiological data and the initial physiological data; Among them, the detection module includes: an acquisition unit, used to obtain the difference information between the reference physiological data and the initial physiological data; a calculation unit, used to determine the time-varying coefficient according to the difference information, and calculate the actual physiological data according to the time-varying coefficient, the reference physiological data and the initial physiological data.

6. The device according to claim 5, characterized in that The matching module comprises: A determination unit, configured to determine a window to be matched according to the actual physiological data at each moment; A matching unit, used for determining a target window according to the window to be matched, with reference to a signal trend reference curve; An identification unit is used to determine the change trend according to the target window.

7. The device according to claim 5, characterized in that Also includes: A first generating module, configured to use a preset signal trend reference curve as the signal trend reference curve; Alternatively, the second generating module is used to generate the signal trend reference curve according to actual physiological data of the person under test at multiple moments who is in a stationary state and maintains a second preset time period.

8. A wearable device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the physiological data detection method based on human factors intelligence as described in any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the physiological data detection method based on human factors intelligence as described in any one of claims 1 to 4.

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