Physiological parameter detection method, device, equipment and medium

Abstract

The embodiment of the invention discloses a physiological parameter detection method and device, equipment and a medium. The method comprises the following steps: acquiring original air bag pressure data of a user on the intelligent mattress within a set time length; performing preprocessing operation on the original air bag pressure data to obtain target air bag pressure data; and determining heart rate parameters and breathing parameters of the user according to the target airbag pressure data. According to the technical scheme, the fluctuation change of the body is accurately sensed through the air pressure change of the air bag, and natural and comfortable sleep experience is provided for a user while non-inductive detection of the physiological parameters of the human body is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home appliances, and in particular to a physiological parameter detection method, device, equipment and medium. Background Art

[0002] As people's living standards continue to improve, more and more people are beginning to pay attention to their quality of life. Sleep occupies one-third of our lives, and mattresses are an essential household item in our daily lives. With the advancement of health technology, people's demand for non-invasive health monitoring is growing.

[0003] Traditional monitoring sensors built into mattresses (such as piezoelectric sensors, piezoresistive sensors, etc.) affect the sleeping experience due to the obvious foreign body sensation, and can no longer meet the health monitoring needs of the modern human body. Summary of the Invention

[0004] The present invention provides a physiological parameter detection method, device, equipment and medium, which accurately sense the ups and downs of the body through the changes in air pressure of the airbag, while achieving non-sensing detection of the physiological parameters of the human body and providing users with a natural and comfortable sleeping experience.

[0005] According to one aspect of the present invention, a method for detecting physiological parameters is provided, comprising:

[0006] Obtain the original airbag pressure data within the time period set by the user on the smart mattress;

[0007] performing a preprocessing operation on the original airbag pressure data to obtain target airbag pressure data;

[0008] The user's heart rate parameters and breathing parameters are determined according to the target airbag pressure data.

[0009] According to another aspect of the present invention, there is provided a physiological parameter detection device, comprising:

[0010] A data acquisition module is used to obtain the original airbag pressure data within a set time period set by the user on the smart mattress;

[0011] A data preprocessing module, configured to perform a preprocessing operation on the original airbag pressure data to obtain target airbag pressure data;

[0012] The parameter determination module is used to determine the user's heart rate parameters and breathing parameters based on the target airbag pressure data.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can perform the physiological parameter detection method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the physiological parameter detection method according to any embodiment of the present invention when executed.

[0018] The technical solution of this embodiment of the present invention obtains raw airbag pressure data for a set duration of time spent on a smart mattress; preprocesses this raw airbag pressure data to obtain target airbag pressure data; and determines the user's heart rate and respiratory parameters based on this target airbag pressure data. This technical solution accurately senses the body's fluctuations through airbag pressure changes, achieving seamless detection of physiological parameters while providing a natural and comfortable sleeping experience.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a flow chart of a physiological parameter detection method provided in accordance with the first embodiment of the present invention;

[0022] Figure 2 This is a flow chart of a physiological parameter detection method provided in accordance with the second embodiment of the present invention;

[0023] Figure 3 is a schematic diagram of a heart rate signal provided according to the second embodiment of the present invention;

[0024] Figure 4 This is a schematic structural diagram of a physiological parameter detection device provided according to a third embodiment of the present invention;

[0025] Figure 5It is a structural diagram of an electronic device provided according to the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Example 1

[0029] Figure 1 This is a flow chart of a physiological parameter detection method provided according to the first embodiment of the present invention. This embodiment is applicable to the case of detecting physiological parameters of the human body based on a smart mattress. The method can be performed by a physiological parameter detection device. The physiological parameter detection device can be implemented in the form of hardware and / or software. The physiological parameter detection device can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0030] S110: Obtaining original airbag pressure data within a time period set by the user on the smart mattress.

[0031] The user can refer to a person resting on the smart mattress. The set duration can be a pre-set time. For example, the set duration can be 20 seconds. The raw airbag pressure data can be understood as the airbag pressure change data of the smart mattress. In this embodiment, a high-precision AD converter can be used to collect the airbag pressure change data during the set duration of the user's rest on the smart mattress, i.e., the raw airbag pressure data.

[0032] In this embodiment, a sampling frequency can be set for the high-precision AD converter during data acquisition. Airbag pressure data of the current smart mattress is collected based on the set frequency. A higher sampling frequency improves the accuracy of the collected airbag pressure signal, reduces information loss, and provides more effective information, leading to more accurate subsequent processing. However, this increases computational cost and places higher demands on the AD equipment.

[0033] S120 , performing preprocessing operations on the original airbag pressure data to obtain target airbag pressure data.

[0034] The preprocessing operation may include data noise reduction and filtering operations. Data noise reduction can refer to any data transformation operation for data noise reduction, or other noise reduction operations. The filtering operation can be filtering using any filter. The target airbag pressure data can be understood as data obtained after the data preprocessing operation. In this embodiment, data noise reduction and filtering operations can be performed on the raw airbag pressure data to obtain the target airbag pressure data.

[0035] In this embodiment, optionally, the original airbag pressure data is preprocessed to obtain target airbag pressure data, including: performing data transformation processing on the original airbag pressure data to obtain transformed airbag pressure data; and performing filtering processing on the transformed airbag pressure data to obtain target airbag pressure data.

[0036] The noise reduction process may include a wavelet transform and a Hilbert-Huang transform. Filtering may be an operation that processes data using a filter. In this embodiment, the collected raw airbag pressure data may first be subjected to a wavelet transform to eliminate noise from the raw airbag pressure data, obtaining eliminated airbag pressure data. The eliminated airbag pressure data may then be subjected to a Hilbert-Huang transform for secondary filtering to obtain transformed airbag pressure data. The transformed airbag pressure data may then be filtered to obtain target airbag pressure data. Specifically, in this embodiment, the transformed airbag pressure data may be smoothed using a Portworth filter to facilitate subsequent search for peak point information. In this embodiment, different filtering ranges may be used when filtering the transformed airbag pressure data using the Portworth filter. For determining heart rate, the transformed airbag pressure data may be filtered within a filtering range of 3-10 Hz. For determining respiratory parameters, the transformed airbag pressure data may be filtered within a filtering range of 0.1-0.7 Hz.

[0037] Furthermore, in this embodiment, by evaluating and detecting the signal of the target airbag pressure data, if all that exists is background noise, it means that there is no heart rate and breathing signal in the data.

[0038] In this embodiment, through such a setting, the collected original airbag pressure data can be subjected to signal noise reduction processing and filtering and smoothing processing operations, thereby obtaining a clearer and more accurate data signal to facilitate subsequent data calculation and processing operations.

[0039] In this embodiment, optionally, data transformation processing is performed on the original airbag pressure data to obtain transformed airbag pressure data, including: performing a first transformation processing operation on the original airbag pressure data to obtain first airbag pressure data; performing a second transformation processing operation on the first airbag pressure data to obtain transformed airbag pressure data.

[0040] Among them, the first transformation processing may refer to wavelet transformation processing. Wavelet transformation may be a process of decomposing a signal into sub-signals of different frequency bands through multi-scale analysis, and performing denoising processing by utilizing the difference in the distribution of wavelet coefficients of noise and signal in different frequency bands. The second transformation processing may refer to Hilbert-Huang transformation processing HHT. Hilbert-Huang transformation processing may be a process of decomposing a signal into a series of intrinsic mode functions (IMFs) through empirical mode decomposition (EMD), and then performing Hilbert transformation (HT) on each IMF to obtain the instantaneous frequency and amplitude, thereby performing time-frequency analysis on the signal. In this embodiment, by

[0041] In this embodiment, the wavelet transform processing operation on the original airbag pressure data can be to perform a hierarchical decomposition on the collected original airbag pressure data to obtain the corresponding first airbag pressure data; then perform Hilbert-Huang transform processing on the first airbag pressure data to obtain the time-frequency data of the air pressure data signal, that is, the transformed airbag pressure data.

[0042] In this embodiment, through such an arrangement, the collected original airbag pressure data is transformed and processed so that subsequent data analysis and processing can be performed based on the obtained airbag pressure data.

[0043] S130: Determine the user's heart rate parameters and breathing parameters according to the target airbag pressure data.

[0044] The heart rate parameter may refer to a user's heart rate parameter. The breathing parameter may refer to a user's breathing parameter. In this embodiment, data signals of different frequencies may be extracted from the target airbag pressure data, and the user's heart rate parameter and breathing parameter may be determined based on different preset spacing information.

[0045] The technical solution of this embodiment of the present invention obtains raw airbag pressure data for a set duration of time spent on a smart mattress; preprocesses this raw airbag pressure data to obtain target airbag pressure data; and determines the user's heart rate and respiratory parameters based on this target airbag pressure data. This technical solution accurately senses the body's fluctuations through airbag pressure changes, achieving seamless detection of physiological parameters while providing a natural and comfortable sleeping experience.

[0046] Example 2

[0047] Figure 2 This is a flow chart of a physiological parameter detection method provided in accordance with the second embodiment of the present invention. This embodiment is optimized based on the above embodiment. The specific optimization is as follows: determining the user's heart rate parameter and breathing parameter based on the target airbag pressure data, including: determining multiple peak data included in the target airbag pressure data; determining the user's heart rate parameter and breathing parameter based on the multiple peak data and the set interval. Figure 2 As shown, the method includes:

[0048] S210: Obtaining original airbag pressure data within a time period set by the user on the smart mattress.

[0049] S220: Preprocess the original airbag pressure data to obtain target airbag pressure data.

[0050] S230: Determine multiple peak data included in the target airbag pressure data.

[0051] Among them, the peak data may refer to the individual maximum value data in the target airbag pressure data within a set time length. In this embodiment, the target airbag pressure data may include multiple maximum value data. Multiple peak data may be understood as the number of maximum values ​​determined in the current target airbag pressure data. The specific numerical value of the first number may be determined based on the current target airbag pressure data. The target airbag pressure data in this embodiment may be represented in the form of a triangular waveform, and the multiple peak data may refer to the number of peak data contained in the upper half of the X-axis. In this embodiment, the multiple maximum value data contained therein may be determined from the target airbag pressure data within a set time length.

[0052] S240: Determine the user's heart rate parameter and breathing parameter respectively according to the plurality of peak data and the set interval.

[0053] The set interval may be a pre-set interval between two peak values. The set interval in this embodiment may include a set range of intervals, such as a range between a set minimum interval and a set maximum interval. In this embodiment, different interval information may be set during the determination of heart rate parameters and respiratory parameters.

[0054] In this embodiment, point information corresponding to the heart rate and point information corresponding to the breathing can be analyzed separately based on multiple peak data and pre-set spacing information, so that the user's heart rate parameters and breathing parameters can be obtained based on the point information corresponding to the heart rate and the point information corresponding to the breathing.

[0055] In this embodiment, optionally, the set interval includes a first set interval and a second set interval; the user's heart rate parameters and breathing parameters are respectively determined according to multiple peak data and the set intervals, including: respectively obtaining the first set interval corresponding to the heart rate parameter and the second set interval corresponding to the breathing parameter; determining the peak mean of multiple peak data; based on the peak mean, determining the user's heart rate parameters and breathing parameters in combination with the first set interval and the second set interval.

[0056] Among them, the first set spacing can be the spacing information set for the heart rate parameter analysis. Exemplarily, the first set spacing in this embodiment can set the maximum (t1) and minimum (t2) spacing information. Exemplarily, the maximum (t1) can be 150 and the minimum (t2) can be 40, and can also be set according to actual needs. The second set spacing can be the spacing information set for the breathing parameter analysis. Exemplarily, the second set spacing in this embodiment can set the maximum (t1) and minimum (t2) spacing information. Exemplarily, the maximum (t1) can be 500 and the minimum (t2) can be 100, and can also be set according to actual needs. The peak mean can be the mean data obtained by averaging the determined multiple peak data.

[0057] In this embodiment, the first set interval corresponding to the heart rate parameter and the second set interval corresponding to the breathing parameter can be obtained respectively, and then the heights of multiple peak data are added and averaged to obtain the peak mean, and then data analysis is performed based on the peak mean and the first set interval to obtain each heart rate point data to determine the user's heart rate parameter; and data analysis is performed based on the peak mean and the second set interval to obtain each breathing point data to determine the user's breathing parameter.

[0058] Through such a setting in this embodiment, data analysis can be performed based on the set spacing information and peak mean corresponding to the heart rate and breathing, and the signals of each point can be determined respectively to determine the user's heart rate parameters and breathing parameters, providing reliability and accuracy determined by physiological parameters.

[0059] In this embodiment, optionally, the user's heart rate parameters and breathing parameters are determined based on the peak mean and in combination with the first set interval and the second set interval, including: determining each heart rate point information in sequence based on the peak mean and the first set interval, and determining the user's heart rate parameters based on each heart rate point information; determining each breathing point information in sequence based on the peak mean and the second set interval, and determining the user's breathing parameters based on the breathing point information.

[0060] Among them, the heart rate point information can be the peak point information that meets the heart rate determined according to the peak mean and the first set interval. In this embodiment, the peak point information that meets the breathing can be determined according to the peak mean and the second set interval. In this embodiment, the first heart rate point information and the second heart rate point information can be determined in sequence according to the first set interval, and the subsequent heart rate point information can be periodically determined according to the second heart rate point information and the first set interval, so as to obtain each heart rate point information. In this embodiment, the point with the closest distance and the highest adjacent peak value can be selected as the breathing point information according to the second set interval and the peak mean, and then the relevant point information can be processed to obtain the user's effective breathing parameters. Exemplarily, the specific process of determining the breathing parameters based on the airbag signal in this embodiment can be to filter the airbag pressure data signal by 0.1-0.7HZ, and then select the peak point local_max therein. Calculate the distance between the first and second coordinates of local_max. The judgment process based on the distance between the two coordinates can be: ① If the distance is < 24, select the one with the higher peak as the breathing point breath_pos; otherwise, add both to the breathing point, i = 2; ② Repeat step ① with breath_pos[:-1] and local_max[++i] to obtain the information of each breathing point, and the corresponding breathing parameters can be determined based on the breathing point information.

[0061] For example, in this embodiment, determining a user's heart rate parameters based on various heart rate point information can be as follows: If the sampling frequency is set to 100, the first heart rate point information is 100, the second heart rate point information is 150, and the spacing between them is 50; then 6000 ÷ 50 is used to obtain the heart rate, which is 120. The same applies to respiration. The first respiration point information can be 100, and the second respiration point information can be 500. Therefore, the spacing between the first and second heart rate points is 400; then, using 6000 ÷ 400 = 15, the corresponding respiration rate is obtained. In this embodiment, if the heart rate and respiration are relatively normal, the heart rate and respiration are output. If the heart rate is present but respiration is absent, it indicates apnea. If there is no heart rate or respiration information, then no one is in bed.

[0062] Through such a setting in this embodiment, the user's heart rate parameters and breathing parameters can be determined according to different set intervals and peak data, so that the user can achieve accurate vital signs monitoring in a natural sleep state, improving the user's experience.

[0063] In this embodiment, optionally, each heart rate point information is determined in sequence based on the peak mean and the first set interval, and the user's heart rate parameters are determined according to each heart rate point information, including: determining the first heart rate point information based on the peak mean; wherein the first heart rate point information is a peak point greater than the peak mean; determining the second heart rate point information based on the first heart rate point information and the first set interval; periodically determining each heart rate point information based on the first set interval and the second heart rate point information, and determining the user's heart rate parameters according to each heart rate point information.

[0064] The first heart rate point information may refer to a peak point higher than the peak average as the first point information in each heart rate point information. The second heart rate point information may be the second point information determined based on the determined first heart rate point information and the set first set interval.

[0065] In this embodiment, the first set spacing can be the maximum (t1) and minimum (t2) spacing information, and then the first peak point above the peak mean is determined as the first heart rate point information based on the peak mean, and then the second heart rate point information is determined according to the set first spacing information and the first heart rate point information, and each heart rate point information is periodically determined based on the second heart rate point information and the first set spacing, and the determined each heart rate point information is determined as the user's heart rate parameter.

[0066] For example, in this embodiment Figure 3 A schematic diagram of a heart rate signal is given. The horizontal line corresponding to the vertical coordinate 400 in the figure is the peak mean value determined based on all peak data. Therefore, if Figure 3 The peak mean value shown is 400; the heart rate point information in this embodiment can be determined from each peak point that is greater than the peak mean value.

[0067] In this embodiment, optionally, when there is an abnormality or missing data, the first heart rate point information and the second heart rate point information in each heart rate point information can be determined based on the peak mean and the first set interval; the third heart rate point information is determined based on the second heart rate point information, and it is judged whether the third heart rate point information meets the set conditions; if the third heart rate point information meets the set conditions, a second number of peak data is determined based on the third heart rate point; the corresponding second peak mean is determined based on the second number of peak data; the heart rate point information is continued to be determined based on the second peak mean and the first set interval, and the user's heart rate parameters are determined based on each heart rate point information.

[0068] In this embodiment, each heart rate point information is determined by sequentially judging the collected data based on the determined peak mean and the set spacing. The set condition can be pre-set. In this embodiment, the set condition can be that the first set spacing is not satisfied and the value is lower than the peak mean. The second number of peak data can be the number of peak data in the local area following the third heart rate point information. In this embodiment, the second number is smaller than the first number. The second peak mean can be the mean data determined from the second number of peak data.

[0069] In this embodiment, the first set interval can be the set maximum (t1) and minimum (t2) interval information, and then the first heart rate point information and the second heart rate point information are determined respectively according to the peak average value and the set first interval information, and the third heart rate point information is determined based on the second heart rate point information and the first set interval. If the third heart rate point information does not meet the set first set interval and is lower than the peak average value, it means that the heart rate point information is missing. It may be in the data below the X-axis of the triangular waveform, that is, the second number of peak data located in the part below the X-axis is determined from the third heart rate point information, so as to re-determine the local peak average value, that is, the second number of peak data is averaged to determine its corresponding second peak average value, and then continue to search and determine other heart rate point information based on the second peak average value and the first set interval. The heart rate point information is determined cyclically until the waveform corresponding to the target airbag pressure data within the set time is determined, so that each heart rate point information can be obtained, and then the heart rate parameters of the user are obtained by integration processing based on each heart rate point information.

[0070] Furthermore, in this embodiment, when determining heart rate point information based on the set maximum (t1) and minimum (t2) spacing information, if the spacing between the current heart rate point information and the previous heart rate point information is less than t2, then that round is not counted; if the spacing between the determined heart rate point information is greater than t1, then that round is also not counted. In this embodiment, by searching for two rounds of peak data, the heart rate point information that meets the requirements can be determined, thereby improving the reliability of the heart rate parameters.

[0071] Exemplarily, in this embodiment, the process of determining the heart rate parameter from the processed airbag pressure data signal can be to cache 2048 AD raw data, divide them into two groups of 1024 to calculate the heart rate twice, and filter each time through 3 - 10 HZ. The first step can be to find the peak points. Specifically, select the maximum value points pk_all after filtering, calculate the average value avg_all of the pk_all peaks, and find the points pk-high that are greater than avg_all. ① Try the first six points in pk_high as the first point respectively and add them to select. The second point is the nearest peak greater than the minimum distance t1 = 50, and add this peak to the selected heart rate points select. ② Cur and pre are two adjacent points in high_pk, pre is the previous one, and cur is the next one. In this embodiment, the judgment method for two adjacent points based on the distance and the set spacing can be: (1) When the distance between two adjacent points <= t2 = 120 and >= t1 = 50, calculate the average value of the difference in select subscripts, calculate the difference between high_pk and the current cur, and select the one with the smallest difference and add it to select; (2) When the distance between two adjacent points > 3 * t2, do not look for subsequent heart rate points; (3) When the distance between two adjacent points < t1, move cur and pre backward; (4) When the distance between two adjacent points 3 * t2, find the peak points among them, calculate high_pk1, and add the nearest peak point to select. ③ If the subsequent distance is greater than the average value avg2, add the high_peaks with the nearest average value of the select subscript difference to select until select[:-1] + avg2 > cur. ④ Flip the signal along the Y-axis, repeat steps ① and ②, and select twelve rounds of different starting point position information. In this embodiment, after determining different heart rate position information, the operation of screening the position information can be performed. Specifically, it can be to calculate the number of valid positions valid_pos of all points greater than avg in 12 groups of positions, calculate the average amplitude avg_ampt of each group of positions, calculate the difference in distance pos_diff of each group of positions, and additionally use Fourier transform to obtain the main frequency of a single segment of the signal. Additionally, calculate the main frequency of the previous minute. First, use the main frequency of the previous minute to determine the final heart rate, add it to the cache, calculate the heart rate. If the difference between the last two data is less than 5, output the latest heart rate, otherwise output the value in the cache, thereby obtaining the corresponding heart rate parameter.

[0072] With such a setting in this embodiment, the user can continuously detect key vital signs such as heart rate parameters and respiratory parameters without wearing any equipment, which not only improves the convenience of use but also avoids the discomfort or inconvenience that wearable devices may bring; moreover, the airbag of the intelligent air cushion can automatically adjust the pressure distribution according to the user's body contour, providing a more natural and comfortable support experience without disturbing the user's sleep.

[0073] This embodiment innovatively utilizes airbag sensing technology through a non-contact health monitoring algorithm. Using a highly sensitive airbag array, it captures real-time fluctuations in the chest and abdomen. After algorithmic analysis, it accurately extracts key physiological parameters such as heart rate and respiratory rate, achieving medical-grade monitoring accuracy. The entire process requires no active user interaction, achieving truly "non-sensing monitoring" without the discomfort of physical contact or disruption to sleep quality, enhancing the user experience.

[0074] The technical solution of this embodiment of the present invention obtains raw airbag pressure data for a set duration of time spent on a smart mattress; preprocesses the raw airbag pressure data to obtain target airbag pressure data; determines multiple peak values ​​contained in the target airbag pressure data; and determines the user's heart rate and breathing parameters based on the multiple peak values ​​and set intervals. This technical solution accurately senses the body's fluctuations through airbag pressure changes, achieving seamless detection of physiological parameters while providing a natural and comfortable sleep experience.

[0075] Example 3

[0076] Figure 4 FIG. 1 is a schematic diagram of a physiological parameter detection device according to the third embodiment of the present invention. Figure 4 As shown, the device includes:

[0077] The data acquisition module 410 is used to obtain the original airbag pressure data within a set time period set by the user on the smart mattress;

[0078] The data preprocessing module 420 is used to perform preprocessing operations on the original airbag pressure data to obtain target airbag pressure data;

[0079] The parameter determination module 430 is used to determine the user's heart rate parameters and breathing parameters according to the target airbag pressure data.

[0080] Optionally, the data preprocessing module 420 includes:

[0081] A data conversion unit is used to perform data conversion processing on the original airbag pressure data to obtain converted airbag pressure data;

[0082] The filtering processing unit is used to filter the transformed airbag pressure data to obtain target airbag pressure data.

[0083] Optionally, the data conversion unit is specifically used to:

[0084] Performing a first transformation processing operation on the original airbag pressure data to obtain first airbag pressure data;

[0085] A second transformation operation is performed on the first airbag pressure data to obtain transformed airbag pressure data.

[0086] Optionally, the parameter determination module 430 includes:

[0087] a peak data determining unit, configured to determine a plurality of peak data included in the target airbag pressure data;

[0088] The parameter determination unit is used to determine the user's heart rate parameter and breathing parameter respectively according to the multiple peak data and the set interval.

[0089] Optionally, the set spacing includes a first set spacing and a second set spacing;

[0090] A parameter determination unit, comprising:

[0091] a spacing acquisition subunit, configured to respectively acquire a first set spacing corresponding to the heart rate parameter and a second set spacing corresponding to the breathing parameter;

[0092] a mean value determination subunit, configured to determine a peak value mean of a plurality of peak value data;

[0093] The parameter determination subunit is used to determine the user's heart rate parameter and breathing parameter based on the peak mean and in combination with the first set interval and the second set interval.

[0094] Optional parameter determination subunit, specifically used for:

[0095] determining each heart rate point information in sequence based on the peak average and the first set interval, and determining the user's heart rate parameter according to each heart rate point information;

[0096] Based on the peak value average and the second set interval, each breathing point information is determined in sequence, and the user's breathing parameters are determined according to the breathing point information.

[0097] Optional parameter determination subunit, specifically used for:

[0098] Determine first heart rate point information and second heart rate point information based on the peak average and the first set interval respectively;

[0099] determining a third heart rate point information based on the second heart rate point information, and determining whether the third heart rate point information meets a set condition;

[0100] If the third heart rate point information satisfies the set condition, determining a second number of peak data based on the third heart rate point;

[0101] determining a corresponding second peak mean value according to the second amount of peak data;

[0102] The heart rate point information is continued to be determined based on the second peak average and the first set interval, and the user's heart rate parameters are determined according to the information of each heart rate point.

[0103] A physiological parameter detection device provided by an embodiment of the present invention can execute a physiological parameter detection method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0104] Example 4

[0105] Figure 5 1 is a schematic diagram of the structure of an electronic device provided according to embodiment four of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0106] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0107] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0108] The processor 11 can be various general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the physiological parameter detection method.

[0109] In some embodiments, the physiological parameter detection method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the physiological parameter detection method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the physiological parameter detection method in any other suitable manner (e.g., via firmware).

[0110] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0112] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0113] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0114] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0115] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0116] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0117] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A physiological parameter detection method, characterized in that: include: Obtain the original airbag pressure data within the time period set by the user on the smart mattress; performing a preprocessing operation on the original airbag pressure data to obtain target airbag pressure data; The user's heart rate parameters and breathing parameters are determined according to the target airbag pressure data.

2. The method according to claim 1, characterized in that Performing a preprocessing operation on the original airbag pressure data to obtain target airbag pressure data includes: Performing data transformation processing on the original airbag pressure data to obtain transformed airbag pressure data; The transformed airbag pressure data is filtered to obtain target airbag pressure data.

3. The method according to claim 2, characterized in that Performing data transformation processing on the original airbag pressure data to obtain transformed airbag pressure data includes: Performing a first transformation processing operation on the original airbag pressure data to obtain first airbag pressure data; A second transformation operation is performed on the first airbag pressure data to obtain transformed airbag pressure data.

4. The method according to claim 1, wherein Determining the user's heart rate parameters and breathing parameters according to the target airbag pressure data includes: determining a plurality of peak data included in the target airbag pressure data; The user's heart rate parameter and breathing parameter are determined respectively according to the plurality of peak data and the set interval.

5. The method according to claim 4, characterized in that The set spacing includes a first set spacing and a second set spacing; Determining the user's heart rate parameter and breathing parameter based on the plurality of peak data and the set intervals, respectively, includes: Respectively obtaining a first set interval corresponding to the heart rate parameter and a second set interval corresponding to the breathing parameter; determining a peak mean of the plurality of peak data; Based on the peak average, the user's heart rate parameter and breathing parameter are determined in combination with the first set interval and the second set interval.

6. The method according to claim 5, characterized in that Determining a heart rate parameter and a breathing parameter of the user based on the peak average and combining the first set interval and the second set interval respectively includes: determining each heart rate point information in sequence based on the peak-mean value and the first set interval, and determining the user's heart rate parameter according to each heart rate point information; Based on the peak average and the second set interval, each breathing point information is determined in sequence, and the user's breathing parameters are determined according to the breathing point information.

7. The method according to claim 6, characterized in that Determining each heart rate point information in sequence based on the peak average and the first set interval, and determining the user's heart rate parameter according to each heart rate point information, including: Determining first heart rate point information based on the peak mean value; wherein the first heart rate point information is a peak point greater than the peak mean value; Determining second heart rate point information according to the first heart rate point information and the first set distance; Based on the first set interval and the second heart rate point information, each heart rate point information is periodically determined, and the user's heart rate parameter is determined according to each heart rate point information.

8. A physiological parameter detection device, characterized in that: include: A data acquisition module is used to obtain the original airbag pressure data within a set time period set by the user on the smart mattress; A data preprocessing module, configured to perform a preprocessing operation on the original airbag pressure data to obtain target airbag pressure data; The parameter determination module is used to determine the user's heart rate parameters and breathing parameters based on the target airbag pressure data.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can perform the physiological parameter detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the physiological parameter detection method according to any one of claims 1 to 7 when executed.

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