A method and apparatus for monitoring sleep characteristics and respiratory rate

Abstract

The invention discloses a method and device for monitoring sleep characteristics and respiratory rate, which belongs to the technical field of physical sign detection, and comprises the following steps: obtaining pressure information of multiple positions of a user at fixed time intervals in a non-contact manner, and converting the pressure information into electronic data; performing differential operation on the electronic data according to time sequence, obtaining differential results, calculating the first-order moment or the second-order moment of the differential results, and normalizing them; calculating the integral probability distribution of the differential results; determining the time region in a stable state of body motion; calculating the sum of electronic data of multiple positions at the same time to form a data block, and performing Fourier transform operation; searching for the frequency point with the largest amplitude in the spectrum; the invention uses a non-contact method to obtain pressure information of multiple positions of a user at fixed time intervals, so as to effectively evaluate personal sleep quality, sleep habits, whether there is sleep breathing disorder, etc., and has a simple structure and convenient calculation.

Description

Technical Field

[0001] The present invention relates to the technical field of physical sign detection, and in particular to a method and device for monitoring sleep characteristics and respiratory rate. Background Art

[0002] With the extension of various application needs in people's daily life, various sensor technologies and products have also gained great application and development, and the accuracy of corresponding sensor data has become higher and higher. Various sensors continuously obtain data in unmanned scenes, and massive device data interacts with each other, providing a foundation for the development of the Internet of Things. The basic data in the Internet of Things is screened, extracted and calculated by increasingly mature big data algorithms, and is provided to various intelligent application scenarios and needs, which has and will continue to change people's lifestyles.

[0003] Paying attention to and improving one's daily health status is one of the most important application needs. How to use various smart devices to monitor one's physical signs and health status in real time without affecting any personal behavioral habits and daily activities, so as to warn of unknown health risks, has become an important part of people's daily life.

[0004] Aiming at the application scenario of sleep health in health needs, the present invention proposes a method and device for real-time monitoring of personal sleep sign data and respiratory rate using pressure sensor technology. The device is a non-wearable device that can senselessly collect and calculate personal sleep data to evaluate personal sleep quality, sleep habits, whether there is sleep breathing disorder, etc. At the same time, the data obtained by the device can be connected to the Internet of Things network, stored in the cloud, and interacted and calculated with other personal body indicator data to further provide data reference for medical diagnosis and treatment. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention provides a method and device for monitoring sleep characteristics and breathing rate, which obtains pressure information of multiple positions of the user at fixed time intervals in a non-contact manner, thereby effectively evaluating the individual's sleep quality, sleeping habits, whether there is sleep breathing disorder, etc. The method and device have a simple structure and are convenient for calculation.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] In a first aspect, the present invention provides a method for monitoring sleep characteristics and breathing rate, comprising the following steps:

[0008] Step 1: Obtain pressure information of multiple positions of the user at fixed time intervals, and convert the pressure information into electronic data;

[0009] Step 2: performing a differential operation on the electronic data according to the time sequence, obtaining a differential result, calculating a first-order moment or a second-order moment of the differential result, and normalizing the calculation result of the first-order moment or the second-order moment;

[0010] Step 3: Calculate the integral probability distribution of the difference result;

[0011] Step 4: determining the time zone in a stable state of body motion according to the difference result;

[0012] Step 5: Calculate the sum of the electronic data at multiple positions at the same time, and form data blocks according to the time sequence, select part of the data blocks located in the time region to perform Fourier transform operation, and obtain the bed body movement spectrum at the corresponding time;

[0013] Step 6: Search for the frequency point with the largest amplitude in the spectrum of the body movement in bed.

[0014] As a preferred technical solution, in step 1, the pressure information is obtained in a non-contact manner.

[0015] As a preferred technical solution, in step 1, multiple locations for obtaining user pressure information are distributed in a matrix or discrete manner.

[0016] As a preferred technical solution, when multiple locations for obtaining user pressure information are distributed in a matrix manner, in step 2, the electronic data of multiple locations at the same time are formed into a matrix, and a normalized difference operation is performed on the matrix according to a time sequence; when multiple locations for obtaining user pressure information are distributed in a discrete manner, in step 2, a normalized difference operation is performed separately on the electronic data of each location according to a time sequence.

[0017] As a preferred technical solution, in step 3, the integrated probability distribution is used to characterize and evaluate sleep quality.

[0018] As a preferred technical solution, in step 6, the frequency point with the largest amplitude is the respiratory rate.

[0019] In a second aspect, the present invention provides a device for monitoring sleep characteristics and respiratory rate, which is used to implement the aforementioned method for monitoring sleep characteristics and respiratory rate, comprising:

[0020] A pressure sensing unit, used to obtain pressure information of the user in a non-contact manner;

[0021] A data acquisition and preprocessing unit, used for acquiring the pressure information and converting the pressure information into electronic data;

[0022] The application-side data post-processing and index extraction unit characterizes and evaluates the sleep quality and calculates the respiratory rate based on the electronic data.

[0023] As a preferred technical solution, it also includes a data transmission port for connecting the electronic data with the cloud Internet of Things.

[0024] As a preferred technical solution, the pressure sensing unit includes a matrix pressure sensor arranged on the bed surface and / or a single point pressure sensor arranged on the bed legs.

[0025] As a preferred technical solution, a low-pass filter is provided between the data acquisition and preprocessing unit and the application-end data post-processing and index extraction unit, and the low-pass filter is used to filter out interference clutter in the electronic data.

[0026] The beneficial effects of the present invention are as follows:

[0027] The present invention uses a non-contact method to obtain pressure information of multiple positions of the user at fixed time intervals, so as to effectively evaluate personal sleep quality, sleep habits, whether there is sleep breathing disorder, etc., with a simple structure and convenient calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram of a method for monitoring sleep characteristics and respiratory rate according to the present invention;

[0029] Figure 2 A functional block diagram of a device for monitoring sleep characteristics and breathing rate according to the present invention;

[0030] Figure 3 It is a schematic diagram of the positions of a matrix pressure sensor and a single-point pressure sensor in a device for monitoring sleep characteristics and respiratory rate of the present invention;

[0031] In the figure: 1-matrix pressure sensor, 2-single point pressure sensor. DETAILED DESCRIPTION

[0032] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0033] First, please refer to Figure 1 The present invention provides a method for monitoring sleep characteristics and breathing rate, comprising the following steps:

[0034] Step 1: Obtain pressure information of multiple positions of the user at fixed time intervals, and convert the pressure information into electronic data;

[0035] Step 2: performing a differential operation on the electronic data according to the time sequence, obtaining a differential result, calculating a first-order moment or a second-order moment of the differential result, and normalizing the first-order moment or the second-order moment;

[0036] Step 3: Calculate the integral probability distribution of the difference result;

[0037] Step 4: determining the time zone in a stable state of body motion according to the difference result;

[0038] Step 5: Calculate the sum of the electronic data at multiple positions at the same time, and form data blocks according to the time sequence, select part of the data blocks located in the time region to perform Fourier transform operation, and obtain the bed body movement spectrum at the corresponding time;

[0039] Step 6: Search for the frequency point with the largest amplitude in the spectrum of the body movement in bed.

[0040] It should be noted that in order to improve the user experience, the pressure information is preferably obtained in a non-contact manner; the multiple locations for obtaining the user pressure information can be distributed in a matrix or discretely; when the multiple locations for obtaining the user pressure information are distributed in a matrix, in step 2, the electronic data of the multiple locations at the same time are formed into a matrix, and the matrix is ​​normalized and differentiated according to the time sequence; when the multiple locations for obtaining the user pressure information are distributed in a discrete manner, in step 2, the electronic data of each location are separately normalized and differentiated according to the time sequence; the integral probability distribution is used to characterize and evaluate the sleep quality; in step 6, the frequency point with the largest amplitude is the breathing rate.

[0041] Second, please refer to Figure 2 and Figure 3 The present invention provides a device for monitoring sleep characteristics and respiratory rate, which is used to implement the aforementioned method for monitoring sleep characteristics and respiratory rate, including:

[0042] The pressure sensing unit is used to obtain the user's pressure information in a non-contact manner; specifically, the pressure sensing unit includes a matrix pressure sensor 1 arranged on the bed surface and / or a single point pressure sensor 2 arranged on the bed leg. The matrix pressure sensor 1 is used to obtain the pressure distribution data of the contact surface when the human body is lying in bed; the matrix pressure sensor 1 is set OK The single-point pressure sensor 2 can be arranged on the surface of the mattress or embedded in the inside of the mattress; preferably, the matrix pressure sensor 1 can adopt a flexible pressure film sensor, which is more convenient to use and will not affect the sleeping environment. The single-point pressure sensor 2 can be arranged on the four bed legs. Specifically, it can be composed of four single-point pressure sensors 2 numbered 102-1, 102-2, 102-3, and 102-4, which are used to obtain the bed vibration data caused by body movement and breathing when the human body is lying in bed; in other embodiments, single-point pressure sensors 2 can also be set on only some of the bed legs, which will only affect the measurement accuracy of the pressure information and will not affect the calculation accuracy of the present invention. In this embodiment, the matrix pressure sensor 1 and the single-point pressure sensor 2 can be used simultaneously or separately;

[0043] A data acquisition and preprocessing unit is used to collect pressure information and convert the pressure information into electronic data; further, the data acquisition and preprocessing unit should also include a low-pass filter, which is used to filter out interference clutter in the electronic data; further, this embodiment also includes a data transmission port, which is used to connect the electronic data with the cloud Internet of Things, and can be used for data interaction and calculation of other devices in the Internet of Things;

[0044] It should be noted that, in this embodiment, the matrix pressure sensor 1 is at time The original pressure information collected is ,in, , ; Single point pressure sensor 2 at time The original pressure information collected is ,in, The collected data is affected by the hardware characteristics of the matrix pressure sensor 1 and the sampling circuit. Generally, it contains interference clutter components, which need to be The low-pass filter is designed and selected according to the characteristics of the interference noise. Usually, the data collected by the flexible piezoresistive film sensor is filtered using a second-order low-pass filter to obtain the processed electronic data. , , ;in, .

[0045] After the electronic data is filtered through a low-pass filter to remove interference and clutter, the sleep-related indicator data is extracted and calculated through the application-side data post-processing and indicator extraction unit.

[0046] Set the length of a time interval , for a time interval of The differential operation is performed on the electronic data collected by the matrix pressure sensor 1. The differential result of the electronic data collected by the matrix pressure sensor 1 is: ; The first-order moment of the difference result is: ; The second-order moment of the difference result is: The differential result of the electronic data collected by the single-point pressure sensor 2 is: ; The first-order moment of the difference result is: ; The second-order moment of the difference result is: Preferably, the time interval The setting range is 1-10 seconds; the first and second moments of the difference results of the electronic data represent The intensity of the automatic activities of the bedridden person at all times; further, the first-order moment or the second-order moment is normalized. ,in, For all electronic data The maximum value of .

[0047] Calculate the normalized difference result The integral probability distribution of : ,in, is the normalized difference result The value is less than The number of is the normalized difference result The total number of . The curve represents the sleep quality of bedridden people to a certain extent.

[0048] According to the normalized difference results , determine the time domain in stable sleep state , that is, the time when there is almost no other body movement except breathing: ,in, is the body movement threshold that is set.

[0049] Calculate the sum of the electronic data collected by the single-point pressure sensor 2: , set the sliding time window length , in the time domain of stable sleep state The inner length is Data Block ,Right now, ,right Perform Fourier transform and get Spectrum ,Right now ,in, is the digital Fourier transform function, It is the spectrum of slight body vibration caused by normal breathing of the bedridden person at that moment. The main frequency component in the spectrum represents the breathing rate of the bedridden person.

[0050] Search the Spectrum The frequency point with the largest amplitude , that is, satisfy , For bedridden people The respiratory rate at each moment has time-varying characteristics and can be used to assist in assessing whether bedridden people have sleep apnea, a sleep breathing disorder.

[0051] At the same time, the electronic data measured by the preprocessed matrix pressure sensor , we can get the distribution maps of isopressure lines of bedridden people. Using these maps, we can automatically identify and track sleeping postures, dynamics of important parts of the body such as arms and legs through image technology, so as to further provide more data references for evaluating sleep health.

[0052] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for monitoring sleep characteristics and breathing rate, characterized in that, The following steps are involved: Step 1: Obtain pressure information of multiple positions of the user at fixed time intervals, and the pressure information collected at time t by a matrix pressure sensor is A t ={a ij } t , where 1≤i≤m, 1≤j≤n; the pressure information collected at time t by a single-point pressure sensor is B t = {b l } t , wherein 1≤l≤4; converting the pressure information into electronic data, A t After processing, C t ={c ij } t , 1≤i≤m, 1≤j≤n; where, Step 2: C t Perform differential operation according to the time sequence to obtain the differential result, which is X t ={x ij } t ={c ij } t+τ -{c ij } t , where τ is the length of the time interval; calculate the first-order moment or second-order moment of the difference result, the first-order moment is The second-order moment is The calculation results of the first-order moment or the second-order moment are normalized, and the normalized difference result is Among them, max{d t } is d t The maximum value of Step 3: Calculate e t The integral probability distribution of Among them, num{e t e t <λ} is the normalized difference result e t The value is less than the number of λ, num{e t } is the normalized difference result e t Total number of; Step 4: According to e t , determine the time zone in the stable state of body motion as Ω={te t <δ}, where δ is the set body motion threshold; Step 5: Calculate the sum of the electronic data collected by the single-point pressure sensor as And form data blocks according to time sequence as Where w is the length of the sliding time window; Perform Fourier transform to obtain the body movement spectrum of bed rest at the corresponding moment: Wherein, DFT() is the digital Fourier transform function; Step 6: Search for the frequency point with the largest amplitude in the spectrum of the body movement in bed Among them, f0 is the respiratory rate of the bedridden person at time t0.

2. A method for monitoring sleep characteristics and breathing rate according to claim 1, characterized in that: In step 1, the pressure information is obtained in a non-contact manner.

3. A method for monitoring sleep characteristics and breathing rate according to claim 1, characterized in that: In step 1, multiple locations for obtaining user pressure information are distributed in a matrix or discrete manner.

4. A method for monitoring sleep characteristics and breathing rate according to claim 3, characterized in that: When the multiple positions for obtaining the user pressure information are distributed in a matrix, in step 2, the electronic data of the multiple positions at the same time are formed into a matrix, and a normalized difference operation is performed on the matrix according to the time sequence; When the multiple locations for obtaining the user pressure information are discretely distributed, in step 2, a normalized difference operation is performed separately on the electronic data of each location according to the time sequence.

5. A method for monitoring sleep characteristics and breathing rate according to claim 1, characterized in that: In step 3, the integrated probability distribution is used to characterize and evaluate the sleep quality.

6. A device for monitoring sleep characteristics and respiratory rate, used to implement the method for monitoring sleep characteristics and respiratory rate according to any one of claims 1 to 5, characterized in that: include: A pressure sensing unit, used to obtain pressure information of the user in a non-contact manner; A data acquisition and preprocessing unit, used for acquiring the pressure information and converting the pressure information into electronic data; The application-side data post-processing and index extraction unit characterizes and evaluates the sleep quality and calculates the respiratory rate based on the electronic data.

7. The device for monitoring sleep characteristics and breathing rate according to claim 6, characterized in that: It also includes a data transmission port for connecting the electronic data with the cloud Internet of Things.

8. The device for monitoring sleep characteristics and breathing rate according to claim 6, characterized in that: The pressure sensing unit comprises a matrix pressure sensor arranged on the bed surface and a single point pressure sensor arranged on the bed legs.

9. The device for monitoring sleep characteristics and breathing rate according to claim 6, characterized in that: A low-pass filter is provided between the data acquisition and pre-processing unit and the application-end data post-processing and index extraction unit, and the low-pass filter is used to filter out interference clutter in the electronic data.

Images