Vital signs monitoring device and method of monitoring thereof

Abstract

The present application relates to a vital sign monitoring device and a monitoring method thereof, comprising the following: collecting vital sign related parameters of a user in a monitoring process through a monitoring pad; sending the vital sign related parameters to a cloud server; after the cloud server receives the vital sign related parameters, performing data analysis based on the vital sign related parameters to obtain a vital sign monitoring result; and the cloud server visualizes the vital sign monitoring result and feeds back to a preset IP terminal. On the one hand, the whole monitoring process is non-contact, that is, the user does not need to wear any device, which is convenient to use, does not affect the sleep quality of the user, can be continuously monitored for a long time, and provides comprehensive data; on the other hand, the sleep state of the user is analyzed through a continuation cycle, and the dependence on the accuracy of the data is not high, which can well cope with the problem that the data monitored in the non-contact monitoring scene fluctuates and finally affects the accuracy of the subsequent algorithm analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of health monitoring, in particular to a vital sign monitoring device and a monitoring method thereof. BACKGROUND

[0002] The household vital sign monitoring device refers to a device used in a family environment to monitor physiological parameters of individuals or family members. The original intention of designing such a device is to improve the efficiency of health management and disease prevention, so that users can more conveniently understand their own health status and seek medical treatment in a timely manner when necessary.

[0003] Existing vital sign monitoring devices often need to be worn on the human body, and data is collected through built-in sensors and analyzed through corresponding algorithms to obtain corresponding results. If the vital sign monitoring device is installed on the human body, it will inevitably affect the user experience. If the vital sign monitoring device is not installed on the human body, it may cause fluctuations in the monitored data and ultimately affect the accuracy of subsequent algorithm analysis. SUMMARY

[0004] The purpose of the present application is to at least solve one of the deficiencies of the prior art and provide a vital sign monitoring device and a monitoring method thereof.

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] Specifically, a vital sign monitoring device and a monitoring method thereof are proposed, which include the following:

[0007] The vital sign related parameters of the user in a monitoring process are collected by the monitoring pad;

[0008] The vital sign related parameters are sent to a cloud server;

[0009] After the cloud server receives the vital sign related parameters, data analysis is performed based on the vital sign related parameters to obtain a vital sign monitoring result;

[0010] The cloud server visualizes and feeds back the vital sign monitoring result to a preset IP terminal.

[0011] Further, specifically, the vital sign related parameters include respiratory rate and heart rate, and the collection method of the collected respiratory rate and heart rate is to collect them according to a preset period T, that is, the respiratory rate and heart rate sent to the cloud each time are multi-period collection data, and M real-time respiratory rate and heart rate data are collected in each period.

[0012] Further, specifically, the data analysis based on the vital sign related parameters to obtain a vital sign monitoring result includes,

[0013] based on the collected heart rate data, analyze and determine the proportion of each sleep state of the user;

[0014] based on the collected respiratory rate data, analyze and determine the body movement of the user;

[0015] use the proportion of each sleep state and the body movement as the vital sign monitoring result;

[0016] wherein each sleep state includes a wake state, a light sleep state, and a deep sleep state.

[0017] Further, specifically, based on the collected heart rate data, analyze and determine the proportion of each sleep state of the user, including,

[0018] for the i-th period T of the collected heart rate data, i has a value range of 1 to the maximum period T sampling times, and the average value avg_i is calculated;

[0019] when i is 1, directly determine that the user is in a wake state in the first period;

[0020] when i is not 1, continue the following process,

[0021] determine whether avg_i is greater than a first threshold value, if it is greater than the first threshold value, it is determined that the user is in a wake state at this time;

[0022] if it is not greater than the first threshold value, obtain all M real-time heart rate data collected in the i-1th period T;

[0023] construct a two-dimensional coordinate system with the collection time as the horizontal coordinate and the heart rate data value as the vertical coordinate, at this time, all M real-time heart rate data collected in the i-1th period T form M coordinate points in the two-dimensional coordinate system;

[0024] obtain a data curve by curve fitting on the M coordinate points, then shift the data curve along the x-axis to the right by one period T, at this time, obtain a data discrimination curve for the i-th period T;

[0025] for all M real-time heart rate data collected in the i-th period T, calculate the minimum distance from the data discrimination curve to obtain M minimum distances, count the number of minimum distances greater than a second threshold value, and then calculate the ratio Q of the number of minimum distances greater than the second threshold value to M;

[0026] determine whether the ratio Q is greater than a third threshold value, if it is greater than the third threshold value, it is determined that the heart rate fluctuation is large at this time, and it is determined that the user is in a light sleep state at this time, if it is not greater than the third threshold value, it is determined that the heart rate fluctuation is small at this time, and it is determined that the user is in a deep sleep state at this time;

[0027] Through the above manner, corresponding sleep state determination is performed on all cycles, and the number of cycles corresponding to the wake state, the light sleep state and the deep sleep state is counted, and then the proportion of the wake state, the light sleep state and the deep sleep state in the current monitoring process is calculated.

[0028] Further, specifically, the body movement condition of the user is analyzed and judged based on the collected respiratory rate data, including,

[0029] The collected respiratory rate data is preprocessed to obtain high-quality respiratory rate data.

[0030] The high-quality respiratory rate data is subjected to respiratory signal extraction to obtain a respiratory signal.

[0031] The respiratory signal is subjected to body movement detection to obtain a body movement detection result.

[0032] The body movement detection result is input into a pre-trained machine learning model to output the type and intensity of the body movement, and the type and intensity of the body movement are the body movement condition of the user.

[0033] Further, specifically,

[0034] The respiratory rate and heart rate data are collected by one or more combinations of piezoelectric sensors or micro-motion sensors.

[0035] Further, specifically, the cloud server visualizes and feeds back the vital sign monitoring result to a preset IP terminal, including,

[0036] Before registering an account for each user in the cloud server, the corresponding IP terminal is bound, and when the vital sign monitoring result is obtained, the vital sign monitoring result is generated into a corresponding chart in a preset mode, and the chart is sent to the preset IP terminal to inform the user.

[0037] The present application also provides a vital sign monitoring device applying the vital sign monitoring method, including,

[0038] The monitoring mat and its control box are fixedly arranged on the hard bed board and electrically connected to the control box, the control box is connected to the mains, the monitoring mat is used to collect the vital sign related parameters of the user, and the control box is used to upload the collected related information of the user to the cloud server.

[0039] The present application has the following advantages:

[0040] The present application provides a vital sign monitoring device and a monitoring method thereof. On one hand, the vital sign related parameters of the user's respiratory rate and heart rate are obtained through the monitoring mat installed under the mattress. The whole process is non-contact, i.e. the user does not need to wear any device, which is convenient to use, does not affect the sleep quality of the user, can be continuously monitored for a long time, and provides comprehensive data. On the other hand, the sleep state of the user is analyzed through the extension cycle. The accuracy of the data is not highly dependent, and the problem that the accuracy of the subsequent algorithm analysis is affected by the fluctuation of the data monitored in the non-contact monitoring scene can be well solved. BRIEF DESCRIPTION OF DRAWINGS

[0041] The above and other features of the present disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which like reference numerals denote like elements or components, and in which:

[0042] Figure 1 Fig. 1 shows the installation diagram of the vital sign monitoring device of the present application;

[0043] Figure 2 Fig. 2 shows the schematic diagram of two monitoring mats connected in series in one preferred mode of the present application;

[0044] Figure 3 Fig. 3 shows the flow chart of the vital sign monitoring method of the present application. DETAILED DESCRIPTION

[0045] The concept, specific structure and generated technical effects of the present application will be described clearly and completely in conjunction with the embodiments and the accompanying drawings below, so as to fully understand the purpose, scheme and effect of the present application. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The same reference numerals used in the drawings indicate the same or similar parts.

[0046] Embodiment 1, with reference to Figure 3 The present application provides a vital sign monitoring method, comprising the following steps:

[0047] Step 110, collecting the vital sign related parameters of the user in a monitoring process through the monitoring mat;

[0048] Step 120, sending the vital sign related parameters to the cloud server;

[0049] Step 130, after the cloud server receives the vital sign related parameters, performing data analysis based on the vital sign related parameters to obtain a vital sign monitoring result;

[0050] Step 140, the cloud server visualizes the vital sign monitoring result and feeds back to the preset IP terminal.

[0051] In the embodiment 1, on one hand, the vital sign related parameters of the user's respiratory rate and heart rate are acquired through the monitoring mat installed under the mattress, the whole process is non-contact, that is, the user does not need to wear any equipment, the use is convenient, the sleep quality of the user is not affected, the long time continuous monitoring can be realized, and comprehensive data is provided; on the other hand, the sleep state of the user is analyzed through the extension cycle, the accuracy of the data is not highly dependent, and the problem that the accuracy of the subsequent algorithm analysis is affected by the fluctuation of the data monitored in the non-contact monitoring scene can be well solved.

[0052] As a preferred embodiment of the present application, specifically, the vital sign related parameters include the respiratory rate and the heart rate, and the collection mode of the collected respiratory rate and heart rate is to collect according to a preset period T, that is, the respiratory rate and the heart rate sent to the cloud each time are multi-period collection data, and M real-time respiratory rate and heart rate data are collected in each period.

[0053] In the preferred embodiment, the collection of the vital sign related parameters through the above-mentioned mode can facilitate subsequent analysis and processing.

[0054] As a preferred embodiment of the present application, specifically, the data analysis based on the vital sign related parameters obtains the vital sign monitoring result, including,

[0055] analyzing and judging the proportion of each sleep state of the user based on the collected heart rate data;

[0056] analyzing and judging the body movement of the user based on the collected respiratory rate data;

[0057] taking the proportion of each sleep state and the body movement as the vital sign monitoring result;

[0058] wherein the each sleep state includes a wake state, a light sleep state and a deep sleep state.

[0059] In the preferred embodiment, the proportion of each sleep state of the user is analyzed and judged based on the collected heart rate data, and the body movement of the user is analyzed and judged based on the collected respiratory rate data, so that the vital sign monitoring result corresponding to the user can be accurately obtained.

[0060] As a preferred embodiment of the present application, specifically, the proportion of each sleep state of the user is analyzed and judged based on the collected heart rate data, including,

[0061] For the collected heart rate data of the i-th period T, i ranges from 1 to the maximum period T sampling number N, the average value avg_i is calculated;

[0062] When i is 1, it is directly determined that the user is in a wake state in the first period T;

[0063] When i is not 1, the following process is continued,

[0064] It is determined whether avg_i is greater than the first threshold value, if it is greater than the first threshold value, it is determined that the user is in a wake state at this time;

[0065] If it is not greater than the first threshold value, all M real-time heart rate data collected in the i-1-th period T is obtained;

[0066] A two-dimensional coordinate system is constructed with the collection time as the abscissa and the value of the heart rate data as the ordinate, at this time, all M real-time heart rate data collected in the i-1-th period T forms M coordinate points in the two-dimensional coordinate system;

[0067] The data curve is obtained by curve fitting for the M coordinate points, and then the data curve is translated rightward along the x-axis by one period T, and at this time, the data discrimination curve for the i-th period T is obtained;

[0068] For all M real-time heart rate data collected in the i-th period T, the minimum distance from the data discrimination curve is calculated to obtain M minimum distances, and the number of minimum distances greater than the second threshold value is counted, and then the ratio Q of the number of minimum distances greater than the second threshold value to M is calculated;

[0069] It is determined whether the ratio Q is greater than the third threshold value, if it is greater than the third threshold value, it is determined that the heart rate fluctuation is large at this time, and it is determined that the user is in a light sleep state at this time, if it is not greater than the third threshold value, it is determined that the heart rate fluctuation is small at this time, and it is determined that the user is in a deep sleep state at this time;

[0070] The corresponding sleep state of all periods is determined by the above-mentioned method, and the number of periods corresponding to the wake state, the light sleep state and the deep sleep state is counted, and then the proportion of the wake state, the light sleep state and the deep sleep state in the monitoring process is calculated.

[0071] In the preferred embodiment, considering the heart rate variation characteristics of people in different sleep stages in actual situations,

[0072] Wake state: In the wake state, the heart rate is usually high and fluctuates greatly.

[0073] Early sleep (light sleep stage): After entering the light sleep stage, the heart rate begins to gradually decrease, but still has a certain fluctuation.

[0074] Deep sleep (deep sleep stage): in the deep sleep stage, the heart rate further decreases and fluctuates less, and is relatively stable;

[0075] The above targeted algorithm design is performed, that is, first, the first threshold value is determined through experiments, that is, if the mean heart rate of a period is on the first threshold value, it is proved that the user is awake at this time, if the mean heart rate of a period is below the first threshold value, the adjacent continuous period heart rate is further judged in the above manner, whether the proportion of the number of points at which the distance of the ith period to the data discrimination curve formed based on the i-1th period is greater (greater than the second threshold value) is too high (higher than the third threshold value), if so, it is considered that the heart rate fluctuation is larger, if not, it is considered that the heart rate fluctuation is smaller, so as to accurately realize the judgment of the sleep stage of the user, and then the number of periods corresponding to the awake state, the light sleep state and the deep sleep state is obtained by statistics, and the proportion of the awake state, the light sleep state and the deep sleep state in the monitoring process can be calculated, wherein the first threshold value, the second threshold value and the third threshold value are determined in the form of human setting through pre-experiment.

[0076] As a preferred embodiment of the present application, specifically, the body movement of the user is analyzed and judged based on the collected respiratory rate data, including,

[0077] The collected respiratory rate data is preprocessed to obtain high-quality respiratory rate data;

[0078] The high-quality respiratory rate data is subjected to respiratory signal extraction to obtain a respiratory signal;

[0079] The respiratory signal is subjected to body movement detection to obtain a body movement detection result;

[0080] The body movement detection result is input into a pre-trained machine learning model to output the type and intensity of the body movement, and the type and intensity of the body movement are the body movement of the user.

[0081] In the preferred embodiment, the following steps are included: 1. Data collection: First, the user's respiratory rate data needs to be collected using a vital signs monitoring mat or other sensors (such as piezoelectric sensors, micro-motion sensors, radar technology, etc.). These sensors are usually installed under the mattress and can capture the tiny pressure changes or vibrations caused by the user's breathing. 2. Signal preprocessing: The raw data collected usually contains noise and interference, which needs to be preprocessed to improve data quality. Common preprocessing steps include: - Filtering: Use a low-pass filter to remove high-frequency noise and retain the respiratory signal. Generally, the respiratory frequency is between 0.16 and 0.5 Hz. - Signal amplification: Amplify the weak electrical signal for subsequent processing. - Baseline correction: Remove the DC component in the signal to make the signal fluctuations more obvious. 3. Respiratory signal extraction: Extract the respiratory signal from the preprocessed data. The following methods can be used: - Peak detection: Detect the peaks and valleys in the signal and calculate the respiratory period. - Fourier transform: Convert the time-domain signal to the frequency-domain signal and extract the respiratory frequency. - Wavelet transform: Use wavelet transform for multi-scale analysis of the signal and extract respiratory features. 4. Body movement detection: Based on the extracted respiratory signal, the user's body movement can be analyzed. Common methods include: - Respiratory period analysis: Normal respiratory period should be smooth, if sudden changes or interruptions in respiratory period are detected, it may indicate that the user has body movement. - Signal energy change: Body movement will cause sudden increase in signal energy. By calculating the energy change of the signal, body movement can be detected. - Signal frequency analysis: Body movement will cause sudden changes in signal frequency. By analyzing the frequency components of the signal, body movement can be identified. 5. Body movement classification: Further analyze the type and intensity of body movement. The following methods can be used: - Machine learning: Train machine learning models (such as support vector machines, random forests, neural networks, etc.) to classify different types of body movement, such as turning over, sitting up, getting out of bed, etc. - Rule engine: Based on predefined rules and thresholds, classify body movement. For example, if the respiratory period changes exceed a certain threshold, it is judged as turning over.

[0082] As a preferred embodiment of the present application, specifically,

[0083] Respiratory rate and heart rate data are collected by one or more combinations of piezoelectric sensors or micro-motion sensors.

[0084] In the preferred embodiment, the piezoelectric sensor is a sensor that can convert mechanical energy into an electrical signal. When the user lies on the mattress, the slight pressure changes caused by their breathing and heart rate will be captured by the piezoelectric sensor. These pressure changes will be converted into an electrical signal, which can be processed to extract information such as respiratory rate and heart rate. The micro-motion sensor can detect very small movements. When the user breathes or beats, the mattress will produce slight vibrations, which will be captured by the micro-motion sensor. By analyzing these vibration signals, the user's respiratory rate and heart rate can be extracted. Of course, other sensors that can collect relevant data and their combinations are also possible.

[0085] As a preferred embodiment of the present application, specifically, the cloud server visualizes and feeds back the vital sign monitoring results to the preset IP terminal, including,

[0086] Before registering an account in the cloud server for each user, bind the corresponding IP terminal, when the vital sign monitoring results are obtained, generate the corresponding chart according to the preset mode, and send the chart to the preset IP terminal to inform the user.

[0087] Referring to Figure 1 and Figure 2 The present application also provides a vital sign monitoring device, which applies the vital sign monitoring method, including,

[0088] The monitoring pad and its control box are fixedly arranged on the hard bed plate and electrically connected to the control box, the control box is connected to the mains, the monitoring pad is used to collect the vital sign related parameters of the user, and the control box is used to upload the collected related information of the user to the cloud server.

[0089] In this embodiment 2, by forming the detection module into a monitoring pad and a monitoring box, and electrically separating the detection box, the monitoring pad can be conveniently installed on the bed plate while avoiding the risk of electric shock,

[0090] Specifically, in actual application, the sleep monitor collects physiological parameters of the user through professional sensors in multiple dimensions, including heart rate, respiratory rate, body movement, etc., and analyzes the user's sleep state through algorithms: including wakefulness, sleep, leaving the bed, etc. With the software system, the real-time dynamics of the elderly can be monitored, including physiological parameters, sleep state, abnormal conditions, long-term tracking of physiological data and sleep habits of the elderly, better arrangement of nursing resources, and improvement of nursing quality and efficiency.

[0091] The specific installation process is as follows,

[0092] 1. Confirm whether the bed frame and mattress are suitable

[0093] Bed frame

[0094] The bed board must be a hard flat surface, if placed on the rack, the width of the rack must be greater than the width of the monitoring pad

[0095] (7cm);

[0096] 2. Install the SIM card in the off state, insert the Internet of Things card (metal facing down, corner outward) into the SIM card slot, and then use your nails to push it in. When you hear a "click" and the edge of the SIM card is inside the slot, it means the card is in place.

[0097] Method for removing the SIM card:

[0098] Push the SIM card in with your nails, and after hearing a "click", release your finger. The SIM card will then pop out partially, and you can then pinch the SIM card and pull it out.

[0099] 3. Place the monitoring pad

[0100] After lifting the mattress, place the monitoring pad in the middle of the bed frame, about 50-60 cm from the head of the bed (about 1.5 times the length of the monitoring pad).

[0101] Two monitoring pad connection methods:

[0102] Connect the connection line of one monitoring pad to the USB port in the middle of the other monitoring pad, and then place it horizontally on the bed board (this takes into account that the bed may be too large in some scenarios, so this method can effectively deal with it)

[0103] 4. Connect the control box and monitoring pad, and turn on the power

[0104] Insert the monitoring pad connection line and adapter power line into the control box, then plug the adapter into the socket. If the networking indicator light is on, it means the power is on and the machine has started successfully. If the monitoring pad indicator light is always green, it means the monitoring pad is inserted tightly. If it does not light up, it means the monitoring pad is not inserted tightly.

[0105] 5. Cover the bottom hatch

[0106] Align the monitoring pad connection line and power line with the bottom hatch, then push the bottom hatch inwards. When you hear a "click", it means the bottom hatch is installed in place.

[0107] Method for removing the bottom hatch:

[0108] Press the buckle, then the bottom hatch will be pushed open. Then remove the bottom hatch in the direction of the arrow.

[0109] 6. Secure the monitoring pad and control box

[0110] If installed on a lifting bed, please attach the back adhesive to the back of the monitoring pad, then secure it to the bed board.

[0111] 7. Place mattress Place the mattress over the device.

[0112] In addition, each function module in various embodiments of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module.

[0113] The integrated module, if realized in the form of a software function module and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. When the processor executes the computer program, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer-readable medium can include any entity or system that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0114] Although the description of the present application has been quite detailed and particularly described with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any special embodiment, but should be considered to be a broad interpretation of the claims provided by the appended claims in view of the prior art, thus effectively encompassing the intended scope of the present application. In addition, the present application is described above in embodiments that the inventors can foresee, and the purpose is to provide a useful description, and non-essential modifications to the present application that have not yet been foreseen can still represent equivalent modifications to the present application.

[0115] The above is only the preferred embodiment of the present application, and the present application is not limited to the above-described embodiments, but any means that achieves the technical effects of the present application should be within the scope of protection of the present application. The technical solutions and / or embodiments within the scope of protection of the present application can have various modifications and changes.

Claims

1. A method for monitoring vital signs, characterized in that: These include: The monitoring pad is used to collect the user's vital sign related parameters during a monitoring process; Sending the vital sign related parameters to a cloud server; After receiving the vital sign related parameters, the cloud server performs data analysis based on the vital sign related parameters to obtain vital sign monitoring results; The cloud server visualizes the vital signs monitoring results and feeds them back to the preset IP terminal; Specifically, the vital sign related parameters include respiratory rate and heart rate, and the collected respiratory rate and heart rate are collected according to a preset period T, that is, each respiratory rate and heart rate sent to the cloud are multi-cycle collection data, and M real-time respiratory rate and heart rate data are collected in each period; Specifically, the vital sign monitoring results are obtained by performing data analysis based on the vital sign related parameters, including: Analyze and determine the proportion of each sleep state of the user based on the collected heart rate data; Analyze and determine the user's physical activity based on the collected respiratory rate data; The proportion of each type of sleep state and body movement status are used as vital sign monitoring results; The various sleep states include awake state, light sleep state and deep sleep state; Specifically, based on the collected heart rate data, the user's sleep status is analyzed and determined, including: For the heart rate data of the i-th cycle T collected, the value of i ranges from 1 to the maximum cycle T. The number of sampling times is assumed to be N, and its average value avg_i is calculated; When i is 1, it is directly determined that the user is in the awake state for one cycle; When i is not 1, continue the following process: Determine whether avg_i is greater than a first threshold, and if so, determine that the state is awake; If it is not greater than the first threshold, all M real-time heart rate data collected in the i-1th period T are obtained; A two-dimensional coordinate system is constructed with the acquisition time as the horizontal coordinate and the heart rate data value as the vertical coordinate. At this time, all M real-time heart rate data collected in the i-1th period T form M coordinate points in the two-dimensional coordinate system; A data curve is obtained by curve fitting for the M coordinate points, and then the data curve is translated rightward along the x-axis by one period T to obtain a data discrimination curve for the i-th period T; For all M real-time heart rate data collected in the i-th period T, calculate the minimum distance to the data discriminant curve to obtain M minimum distances, count the number of minimum distances greater than a second threshold, and then calculate the ratio Q of the number of minimum distances greater than the second threshold to M; Determine whether the ratio Q is greater than a third threshold value. If so, it is determined that the heart rate fluctuation is large and the patient is in a light sleep state. If not, it is determined that the heart rate fluctuation is small and the patient is in a deep sleep state. Through the above judgment steps, the corresponding sleep state of all cycles is determined, and the number of cycles corresponding to the awake state, light sleep state and deep sleep state are counted, and then the proportion of the awake state, light sleep state and deep sleep state in this monitoring process is calculated.

2. The vital signs monitoring method according to claim 1, characterized in that: Specifically, the user's body movement is analyzed and judged based on the collected respiratory rate data, including: Preprocessing the collected respiratory rate data to obtain high-quality respiratory rate data; Extracting respiratory signals from high-quality respiratory rate data to obtain respiratory signals; Performing body motion detection on the respiratory signal to obtain a body motion detection result; The body movement detection result is input into a pre-trained machine learning model to output the type and intensity of the body movement, and the type and intensity of the body movement are the user's body movement conditions.

3. The vital signs monitoring method according to claim 1, characterized in that: Specifically, Respiration rate and heart rate data are collected through one or more combinations of piezoelectric sensors or micro-motion sensors.

4. The vital signs monitoring method according to claim 1, characterized in that: Specifically, the cloud server visualizes the vital signs monitoring results and feeds them back to the preset IP terminal, including: A corresponding IP terminal is bound to each user's registered account in the cloud server in advance. When the vital signs monitoring results are obtained, the vital signs monitoring results are generated into a corresponding chart according to a preset mode, and the chart is sent to the preset IP terminal to inform the user.

5. A vital signs monitoring device, characterized in that: The method for monitoring vital signs according to any one of claims 1 to 4 is applied, comprising: A monitoring mat and a control box thereof, wherein the monitoring mat is fixedly mounted on a hard bed board and electrically connected to the control box, the control box is connected to the mains electricity, the monitoring mat is used to collect parameters related to the user's vital signs, and the control box is used to upload the collected relevant information of the user to a cloud server.

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