CN117375741A

CN117375741A - Breath detection method, electronic device, storage medium, and program product

Info

Publication number: CN117375741A
Application number: CN202210771443.5A
Authority: CN
Inventors: 孙超; 王皓; 张大庆; 郭兴民; 孙雪; 张博
Original assignee: Peking University; Huawei Technologies Co Ltd
Current assignee: Peking University; Huawei Technologies Co Ltd
Priority date: 2022-06-30
Filing date: 2022-06-30
Publication date: 2024-01-09
Also published as: WO2024002029A1

Abstract

The application provides a breath detection method, an electronic device, a storage medium and a program product. The method includes the first device receiving Wi-Fi signals of at least one second device; the first device determines N signal sets according to the phase difference or the amplitude of the channel state information of Wi-Fi signals of the at least one second device, wherein the phase difference or the amplitude in the N signal sets respectively belong to N different numerical value ranges, and N is an integer greater than or equal to 2; the first device determines environmental change information from the N signal sets, the environmental change information including breath detection information. Breath detection can be performed based on the layered channel state information to obtain accurate breath detection information.

Description

Breath detection method, electronic device, storage medium, and program product

Technical Field

The present application relates to the field of communications technologies, and in particular, to a breath detection method, an electronic device, a storage medium, and a program product.

Background

Long-term respiratory rate monitoring has very high medical guidance value, and electronic equipment is used for monitoring respiratory rate for a long time, so that the method is very helpful for early discovery of some diseases. Current respiratory rate detection is mainly in two ways: 1) Short-term testing using specialized medical equipment; 2) Respiratory monitoring is performed using a wearable device. To measure sleep respiratory rate, an apple wristwatch needs to be purchased. While the contact method is somewhat invasive. The user is not likely to persist with usage monitoring for long periods of time.

In recent years, research has been undertaken to detect respiratory rate using wireless network (Wi-Fi) sensing technology. Wi-Fi equipment can obtain amplitude and phase information on subcarriers in the form of channel state information (Channel State Information, CSI), the channel state information describes attenuation and phase rotation of signals, which are experienced by a signal transmitting end along multiple paths reaching a signal receiving end, the channel state information obtained by the signal receiving end can be influenced by any change of signal propagation paths, and measured values of the channel state information can be changed periodically due to thoracic cavity fluctuation caused by human respiration, so that the possibility is provided for detecting human respiration by using the channel state information.

The inventor finds that noise exists in channel state information extracted from Wi-Fi signals in the implementation process of the embodiment of the application, if layering exists in the extracted channel state information, respiration cannot be accurately detected based on the layered channel state information, and even respiration detection cannot be achieved.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a breath detection method, an electronic device, a storage medium, and a program product that can process channel state information that is layered, and can perform breath detection based on the layered channel state information to obtain accurate breath detection information.

In a first aspect, embodiments of the present application provide a respiration detection method, the method including: the first device receives channel state information of Wi-Fi signals, the first device determines N signal sets according to phase differences or amplitudes of the channel state information, the phase differences or the amplitudes in the N signal sets respectively belong to N different numerical ranges, N is an integer greater than or equal to 2, the first device determines environment change information according to the N signal sets, and the environment change information comprises breath detection information.

Wherein the data in each signal set includes a phase difference or amplitude of the channel state information. If the N signal sets are determined according to the phase differences of the channel state information, the phase differences in the N signal sets respectively belong to N different numerical ranges. If N signal sets are determined according to the magnitudes of the channel state information, the magnitudes in the N signal sets respectively belong to N different numerical ranges.

In the embodiment of the application, N signal sets are determined according to the phase difference or the amplitude of the channel state information, so that when the layered channel state information is processed, the layered channel state information is divided into N signal sets according to the phase difference or the amplitude of the channel state information, each signal set is extracted, and based on the N signal sets, the respiration detection can be performed, so that the respiration detection based on the layered channel state information is realized, and the accurate respiration detection information is obtained.

Preferably, determining the N signal sets according to the phase difference or amplitude of the channel state information comprises: determining a phase difference of channel state information of each subcarrier; dividing the phase difference into N signal sets; or, determining the amplitude of the channel state information of each subcarrier; the amplitude is divided into a signal set to obtain N signal sets.

Preferably, determining the environmental change information from the N signal sets includes: obtaining a target signal set according to the N signal sets; obtaining a frequency domain candidate respiratory frequency and a respiratory energy ratio corresponding to the frequency domain candidate respiratory frequency according to the target signal set; obtaining a time domain candidate respiratory frequency and a first autocorrelation coefficient corresponding to the time domain candidate respiratory frequency according to the target signal set; and outputting environment change information according to the frequency domain candidate respiratory frequency, the respiratory energy ratio, the time domain candidate respiratory frequency and the first autocorrelation coefficient.

In the embodiment of the application, the target signal set of each subcarrier is obtained by extracting the sub-signals of the layered channel state information of each subcarrier. And performing fast Fourier transform and autocorrelation function calculation on the target signal set of each subcarrier to obtain the frequency domain candidate respiratory rate, respiratory energy ratio, time domain candidate respiratory rate and first autocorrelation coefficient of the subcarrier at the same time. And judging the possibility that the subcarrier carries the respiratory information of the perception object by simultaneously and jointly judging the frequency domain candidate respiratory frequency, the respiratory energy ratio, the time domain candidate respiratory frequency and the first autocorrelation coefficient of the subcarrier, so as to verify whether the frequency domain candidate respiratory frequency or the time domain candidate respiratory frequency is the respiratory frequency of the perception object, and ensuring the accuracy of the respiratory detection information through double verification.

Preferably, outputting the environmental change information according to the frequency domain candidate respiratory rate, the respiratory energy ratio, the time domain candidate respiratory rate, and the first autocorrelation coefficient includes: and outputting the frequency domain candidate respiratory frequency or the time domain candidate respiratory frequency as respiratory detection information when the respiratory energy ratio is detected to be larger than the corresponding first threshold value and the first autocorrelation coefficient is detected to be larger than the corresponding second threshold value.

Preferably, outputting the environmental change information according to the frequency domain candidate respiratory rate, the respiratory energy ratio, the time domain candidate respiratory rate, and the first autocorrelation coefficient includes: and when the frequency domain candidate respiratory frequency is detected to be similar to the time domain candidate respiratory frequency and one or more of the respiratory energy ratio and the first autocorrelation coefficient are larger than the corresponding threshold value, outputting the frequency domain candidate respiratory frequency or the time domain candidate respiratory frequency as respiratory detection information.

Preferably, when it is detected that the frequency domain candidate respiratory rate is close to the time domain candidate respiratory rate and one or more of the respiratory energy ratio and the first autocorrelation coefficient is greater than the corresponding threshold value, outputting the frequency domain candidate respiratory rate or the time domain candidate respiratory rate as the respiratory detection information includes: when the frequency domain candidate respiratory frequency is detected to be similar to the time domain candidate respiratory frequency and one or more of the respiratory energy ratio and the first autocorrelation coefficient are larger than the corresponding threshold value, recording the sum of the respiratory energy ratio and the first autocorrelation coefficient; summing the sum of all recorded respiratory energy ratios and the first autocorrelation coefficients as a denominator; taking the maximum value of the sum of the recorded respiratory energy ratio and the first autocorrelation coefficient as a molecule; and when the ratio of the numerator to the denominator is detected to be smaller than a third threshold value, outputting the frequency domain candidate respiratory rate or the time domain candidate respiratory rate as respiratory detection information.

Preferably, the filtering the N signal sets to obtain a target signal set includes: deleting signal sets with phase differences or amplitudes less than a preset threshold value in N signal sets aiming at each subcarrier to obtain the rest signal sets; and obtaining a target signal set according to the rest signal sets.

Preferably, obtaining the target signal set from the remaining signal set includes: for each remaining signal set, one or more of the following operations are performed, taking the final remaining signal set as the target signal set: judging whether the phase difference or amplitude in a preset time period is absent in the rest signal set; if yes, deleting the rest signal set; or judging whether the difference between the first missing amount and the second missing amount in the rest signal set at intervals of preset time is higher than a preset missing threshold value; wherein the first missing amount corresponds to the highest value of the missing amount or the missing amount of the amplitude of the phase difference in the preset time interval, and the second missing amount corresponds to the lowest value of the missing amount or the missing amount of the amplitude of the phase difference in the preset time interval; if yes, deleting the rest signal set.

Preferably, performing analysis processing on the frequency domain on the target signal set to obtain a frequency domain candidate respiratory frequency and a respiratory energy ratio corresponding to the frequency domain candidate respiratory frequency includes: performing fast Fourier transform on the target signal set to obtain a frequency domain signal corresponding to the target signal set; and obtaining a frequency domain candidate respiratory frequency and a respiratory energy ratio corresponding to the frequency domain candidate respiratory frequency according to the respiratory frequency band and the frequency domain signal.

Preferably, obtaining the frequency domain candidate respiratory frequency and the respiratory energy ratio corresponding to the frequency domain candidate respiratory frequency according to the respiratory frequency band and the frequency domain signal comprises obtaining a sub-frequency domain signal with the frequency in the respiratory frequency band in the frequency domain signal; taking the ratio of the sum of the amplitudes of the sub-frequency domain signals to the sum of the amplitudes of the frequency domain signals as the breathing energy ratio; and taking the frequency corresponding to the maximum amplitude value in the sub-frequency domain signal as the frequency domain candidate respiratory frequency.

Preferably, performing analysis processing on the target signal set in the time domain, and obtaining the time domain candidate respiratory frequency and the first autocorrelation coefficient corresponding to the time domain candidate respiratory frequency includes: performing autocorrelation calculation on the target signal set to obtain an autocorrelation coefficient corresponding to the target signal set; and obtaining a time domain candidate respiratory frequency and a first autocorrelation coefficient corresponding to the time domain candidate respiratory frequency according to the respiratory frequency band and the autocorrelation coefficient.

Preferably, obtaining the time-domain candidate respiratory rate and the first autocorrelation coefficient corresponding to the time-domain candidate respiratory rate according to the respiratory rate and the autocorrelation coefficient includes: taking the frequency corresponding to the maximum autocorrelation coefficient in the respiratory frequency band as a candidate respiratory frequency in the time domain; and taking the autocorrelation coefficient corresponding to the time domain candidate respiratory frequency as a first autocorrelation coefficient.

In a second aspect, embodiments of the present application provide an electronic device, where the electronic device includes at least one processor, and a memory, where the memory is configured to store instructions, and the processor is configured to execute the instructions to implement a method as in any one of the above.

In one possible implementation, the electronic device comprises a terminal device or a wireless access node.

In one possible implementation, the electronic device is configured to interface or output speech based on the breath detection information.

In a third aspect, embodiments of the present application provide a computer-readable storage medium storing a program that causes an electronic device to perform a method as any one of the above.

In a fourth aspect, embodiments of the present application provide a computer program product comprising computer readable instructions which, when executed by one or more processors, implement a method as in any of the above.

It will be appreciated that the electronic device provided in the second aspect, the computer readable storage medium provided in the third aspect and the computer program product provided in the fourth aspect correspond to the methods provided in the first aspect or the second aspect, and therefore, the advantages or various implementation manners achieved by the electronic device or the method provided in the first aspect may be referred to above, and are not repeated herein.

Drawings

Fig. 1 is a schematic diagram of an application scenario of a breath detection method provided in the present application;

fig. 2A is a schematic diagram of channel state information provided in the present application;

fig. 2B is a schematic diagram of another channel state information provided in the present application;

fig. 2C is a schematic diagram of a result of processing the channel state information in fig. 2B;

fig. 3A is a schematic diagram of a result of signal extraction of the channel state information of fig. 2B according to the respiration detection method provided in the present application;

fig. 3B is a schematic diagram of a result of performing fast fourier transform and autocorrelation calculation on the channel state information of fig. 3A according to the respiration detection method provided in the present application;

fig. 4 is a schematic flow chart of a breath detection method according to an embodiment of the present application;

fig. 5A is a schematic diagram of a number 6 subcarrier, a number 16 subcarrier, and a number 26 subcarrier provided in the present application;

fig. 5B is a schematic diagram of extracting a sub-signal of the sub-carrier No. 6 in fig. 5A;

fig. 6 is a histogram obtained by counting the phase differences of subcarriers provided in the present application;

fig. 7 is a schematic diagram of a result obtained by performing a fast fourier transform on a target signal set of all subcarriers;

FIG. 8 is a flowchart of a method for obtaining a frequency domain candidate respiratory rate and respiratory energy ratio provided by the present application;

Fig. 9 is a schematic diagram of a result obtained by performing autocorrelation calculation on a target signal set of one subcarrier;

FIG. 10 is a flowchart of a method for obtaining a time-domain candidate respiratory rate and a first autocorrelation coefficient provided in the present application;

FIG. 11 is a flowchart of a method for outputting breath detection information according to a frequency domain candidate breath frequency, a breath energy ratio, a time domain candidate breath frequency, and a first autocorrelation coefficient provided in the present application;

FIG. 12 is a flowchart of another method for outputting breath detection information according to the frequency domain candidate breath frequency, the breath energy ratio, the time domain candidate breath frequency, and the first autocorrelation coefficient provided in the present application;

fig. 13 is a schematic diagram of a main interface of a user device according to an embodiment of the present application;

fig. 14 is a schematic diagram of a breath detection information interface of a user device according to an embodiment of the present application;

fig. 15 is a schematic diagram of another breath detection information interface of a user device according to an embodiment of the present application;

FIG. 16 is a flowchart of another breath detection method according to an embodiment of the present disclosure;

FIG. 17 is a flowchart of another breath detection method according to an embodiment of the present disclosure;

FIG. 18 is a flowchart of another breath detection method according to an embodiment of the present disclosure;

Fig. 19 is a schematic software architecture diagram of an electronic device according to an embodiment of the present application;

fig. 20 is a schematic structural diagram of a second device according to an embodiment of the present application;

fig. 21 is a schematic structural diagram of a first device according to an embodiment of the present application;

fig. 22 is a schematic structural diagram of a user equipment according to an embodiment of the present application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

Before explaining the embodiments of the present application in detail, application scenarios related to the embodiments of the present application are described.

Referring to fig. 1 together, fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present application. The application scenario comprises a second device 101, a first device 102 and a perception object 103. In fig. 1, an application scenario includes one second device 101, one first device 102, and one perception object 103 as an example, in practical application, two or more second devices 101, two or more first devices 102 may be included, and the number and form of devices, the number of objects, and the form of the devices shown in fig. 1 are not limited to the embodiments of the present application.

Wherein the second device 101 may comprise one or more transmit antennas. The first device 102 may include two or more receiving antennas, which are not particularly limited in this application. One transmitting antenna of the second device 101 and one receiving antenna of the first device 102 are a pair of antennas, which corresponds to one path, and when starting from the frequency domain, one path includes a plurality of subcarriers, and each subcarrier is a frequency domain channel; if one path, from the time domain, includes multiple paths, with straight paths of propagation and other paths of transmission (i.e., multipath propagation).

The second device 101 sends a Wi-Fi signal to the first device 102, which Wi-Fi signal reaches the first device 102 after passing through the perception object 103. The first device 102 obtains channel state information based on the Wi-Fi signal, and determines environment change information of the environment where the first device 102 is located according to the channel state information, including a sensing result of sensing the sensing object 103. The sensing result may be used to indicate a breathing state of the sensing object 103, such as whether the sensing object 103 breathes or a breathing frequency of the sensing object 103, etc.

In some embodiments, the application scenario may also include a user device 104. The user device 104 is configured to receive the environmental change information transmitted by the first device 102, and notify the user of the environmental change information. The notification mode may be interface display or voice playing. The user device 104 communicates with the first device 102, and the connection mode between the first device 102 and the user device 104 may be a wired connection (a wired connection mode such as a network cable, a PLC, etc.) or a wireless connection (a wireless transmission mode such as Wi-Fi, bluetooth, etc.), which is not limited in this application, where the environment change information may be breath detection information of the perception object 103, as shown in fig. 14 and 15.

In some embodiments, the user device 104 in the application scenario is the same device as the second device 101, i.e. the first device 102 may send the perceived environmental change information to the second device 101 (user device 104). In other embodiments, the user device 104 in the application scenario is the same device as the first device 102, and the first device 102 notifies the user of the calculated environmental change information. That is, the user device 104 may perform the breath detection method provided in the embodiments of the present application, and when the user carries (e.g., holds) the user device 104 to move into a new environment in which the first device 102 or the second device 101 is disposed, the user device 104 may still communicate with the second device 101 or the first device 102 in the environment. The user device 104 may perform breath detection according to the received Wi-Fi signal sent by the second device 101, sense environmental change information of the environment in which the user device 104 is located, or the user device 104 may receive environmental change information transmitted by the first device 102 in the new environment.

It should be noted that, the perception object 103 and the first device 102 are both within the coverage area of the second device 101.

In some embodiments, the cloud server 105 may also be included in the application scenario. The cloud server 105 may be in communication with the first device 102, and is configured to receive and store channel state information or environment change information of Wi-Fi signals transmitted by the first device 102. The cloud server 105 may communicate with the user device 104 for transmitting channel state information or environment change information of Wi-Fi signals to the user device 104.

In some embodiments, after the first device 102, the cloud server 105, or the user device 104 receives the channel state information or the environment change information of the Wi-Fi signal, the first device 102, the cloud server 105, or the user device 104 may analyze the received information (the channel state information or the environment change information of the Wi-Fi signal) to obtain the respiration detection information of the perception object 103, and after the user device 104 obtains the respiration detection information from the cloud server 105 or the first device 102 or the user device 104 calculates the respiration detection information, the user device 104 presents the respiration detection information, as shown in fig. 14 and 15.

In other embodiments, the first device 102, the cloud server 105, or the user device 104 may perform fusion processing on the received information (channel state information or environment change information of the Wi-Fi signal), for example, perform fusion calculation according to the received information and other fusion information, and output results such as work and rest detection, sedentary alert, presence of breath in a room, and respiratory rate. The user device 104 obtains the analysis result and presents the analysis result, as shown in fig. 14 and 15.

In other embodiments, the breath detection methods of the present embodiments may be partitioned into one or more modules, which may be a series of computer program instruction segments capable of performing particular functions, the instruction segments describing the execution of the breath detection methods of the present embodiments. The one or more modules may be stored in the first device 102 and/or the user device 104 and/or the cloud service 105. That is, some steps in the breath detection method in the embodiments of the present application may be performed by the first device 102, some steps may be performed by the user device 104, and some steps may be performed by the cloud server 105, which is not specifically limited in this application.

The second device 101 is an entity for transmitting Wi-Fi signals. The second device 101 may be, for example, a bluetooth glasses, a bluetooth watch, a bluetooth headset, a bluetooth speaker, a bluetooth smart screen, a tablet computer, a bluetooth desk lamp, a bluetooth door lock, a bluetooth socket, a bluetooth electronic scale, a mobile phone, a wearable device, a tablet, a computer with a wireless transceiver function, a router, a wireless access point AP, an LTE indoor signaling base station, a Virtual Reality (VR) terminal device, an augmented reality (augmented reality, AR) terminal device, a wireless terminal in an unmanned (self-driving) and the like.

The first device 102 is an entity for receiving Wi-Fi signals. The first device 102 may be, for example, a wireless router or a wireless access point AP, an LTE indoor signaling base station, a mobile phone, a wearable device, a tablet, a computer with wireless transceiver function, and the like.

The user equipment 104 is a terminal device for notifying a user of a sensing result, and the user equipment 104 may be, for example, a mobile phone, a wearable device, a tablet, a computer with a wireless transceiver function, a Virtual Reality (VR) terminal device, an augmented reality (augmented reality, AR) terminal device, or the like.

The application scenario may be an environment where the devices may communicate with each other through Wi-Fi, such as a home environment, an office environment, and the like.

Taking the application scenario in fig. 1 as an example of a home environment, in the home environment, the second device 101 sends a Wi-Fi signal, the first device 102 receives the Wi-Fi signal, and executes the breath detection method provided in the embodiment of the present application according to the channel state information of the Wi-Fi signal, so as to obtain the breath detection information of the sensing object 103 in the home environment. Or, the first device 102 receives the Wi-Fi signal, the first device 102 sends channel state information of the Wi-Fi signal to the user device 104 or the cloud service 105, and the user device 104 or the cloud service 105 executes the breath detection method provided by the embodiment of the present application, and obtains breath detection information of the perception object 103 in the home environment according to the channel state information of the Wi-Fi signal. If the respiration detection method provided in the embodiments of the present application is executed by the first device 102, the user device 104 obtains the respiration detection information of the perception object 103 from the first device 102. If the cloud server 105 executes the breath detection method provided in the embodiments of the present application, the user device 104 obtains the breath detection information of the perception object 103 from the cloud server 105.

Specifically, as in the above-mentioned home environment, the user lives alone, and when the user sleeps, the first device 102 may monitor environmental change information during sleep of the perception object 103 (i.e., the user) in the home environment, and further obtain the breath detection information according to the environmental change information. The first device 102, the second device 101, the cloud server 105 or the user device 104 can help the user to know whether an apnea disorder exists in the sleeping process through the obtained breath detection information, so that the problem that the symptom cannot be timely treated and improved due to the fact that the user suffering from the sleep apnea disorder is not easy to autonomously perceive the time and the severity of the apnea is avoided. The breath detection information obtained by the breath detection method provided by the embodiment of the application is high in accuracy, so that the accuracy and the success rate of human health condition detection of a user are improved.

It should be noted that, the perception object 103 in fig. 1 is only used as an example and is not meant to limit the embodiments of the present application. The perception object 103 may also be an animal, and the specific technology and specific device morphology adopted by the second device 101, the first device 102, and the user device 104, and the perception object 103 are not limited in the embodiments of the present application.

It may be understood that, the application scenario described in the embodiments of the present application is for more clearly describing the technical solution of the embodiments of the present application, and is not limited to the technical solution provided in the embodiments of the present application, and those skilled in the art may know that, with the evolution of the system architecture and the appearance of the new service scenario, the technical solution provided in the embodiments of the present application is applicable to similar technical problems.

The inventor finds that the current Wi-Fi breath detection method can only process noiseless or slightly noisy channel state information, and as shown in fig. 2A, no layering exists in the channel state information. The inventors have also found that channel state information extracted from Wi-Fi signals is subject to noise, and that causes of noise to the channel state information include, but are not limited to: to maximize communication efficiency, devices (e.g., routers) enable automatic gain control. And the automatic gain control affects the channel state information such that the channel state information is noisy. The channel state information in which noise exists is layered, and the channel state information includes multiple layers of sub-signals. As shown in fig. 2B, the channel state information includes four layers of sub-signals corresponding to curves S1, S2, S3, and S4 of fig. 2B, respectively. There is a spacing between each layer of sub-signals, each layer of sub-signals having a certain continuity over time. As in curve S1, there is a signal between 0 and 3 seconds, a signal between 6 and 8 seconds, and a signal between 11 and 15 seconds.

Referring to fig. 2C together, a curve S5 in fig. 2C indicates that after the channel state information in fig. 2B is subjected to the fast fourier transform (Fast Fourier Transform, FFT) by using the conventional respiration detection method, the amplitude corresponding to each frequency is 0. In fig. 2C, a curve S6 indicates that after the channel state information in fig. 2B is calculated by using the conventional respiration detection method and the autocorrelation function (Autocorrelation Function, ACF), the autocorrelation coefficient corresponding to the respiration frequency band is negative. As can be seen from this, the current respiration detection method cannot obtain the frequency information from the layered channel state information in fig. 2B, so that the respiration detection cannot be performed based on the layered channel state information, and the distance between the respiration detection method and the commercial floor is large.

It should be noted that, in the existing respiration detection methods, the channel state information is subjected to fast fourier transform alone, or the channel state information is subjected to autocorrelation function calculation alone. Fig. 2C does not indicate that the existing breath detection method performs fast fourier transform and auto-correlation function calculation on channel state information.

Based on the above, the breath detection method provided by the embodiment of the application can be used for processing noiseless or slightly noisy channel state information, can also be used for processing channel state information with layering, and can be used for breath detection based on the layered channel state information to obtain accurate breath detection information.

Referring to fig. 3A together, fig. 3A shows channel state information obtained by processing the channel state information of fig. 2B by using the respiration detection method provided in the embodiment of the present application, that is, the extracted sub-signal. The channel state information data obtained after the processing is more uniform and the data distribution is clearer. Fig. 3B illustrates a result obtained by performing fast fourier transform and autocorrelation calculation on the channel state information of fig. 3A using the respiration detection method according to the embodiment of the present application. After the sub-signals are extracted by using the breath detection method provided by the embodiment of the application, the sub-signals are subjected to fast Fourier transform, and then a higher amplitude characteristic value can be obtained in a breath frequency band. After the sub-signals are subjected to autocorrelation calculation, a higher autocorrelation coefficient is obtained in the respiratory frequency band. That is, according to the breath detection method provided in the embodiment of the present application, frequency information may be obtained from the layered channel state information in fig. 2B, and thus breath detection may be performed based on the layered channel state information.

The breath detection method provided in the embodiments of the present application will be described next. The breath detection method provided in the embodiment of the present application may be applied to the first device 102, the user device 104, and the cloud server 105, which is not specifically limited in this application.

A flowchart is used in this application to describe the operations performed by an apparatus according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.

Referring to fig. 4, fig. 4 is a schematic flow chart of a breath detection method according to an embodiment of the present application. The first device shown in fig. 1 is exemplified as the respiration detection method described below.

Step S41: channel state information of each subcarrier is acquired.

In this embodiment, the second device sends out data packets through M subcarriers at a specific rate (e.g., 200Hz per second), and two antennas of the first device simultaneously receive the data packets on the multiple subcarriers, and measure the channel state information of each data packet transmitted by each subcarrier. After the first device receives the message from the second device, the first device extracts the preamble in the message, and divides the received preamble by the known sequence stored in the local terminal thereof to obtain corresponding channel state information. Wherein the preamble in the message is a sequence known to both the second device and the first device.

Where M (i.e., the number of subcarriers) is related to Wi-Fi protocol, bandwidth, and the number of antennas used, for example, 802.11a/g has 52 subcarriers in 20MHz mode and 802.11n has 56 subcarriers in 20MHz mode. The number of subcarriers in the embodiments of the present application is not particularly limited.

The message may be a data message carrying a special training symbol, a Null Data Packet (NDP), or a physical layer protocol data unit (physical layer protocol date units, PPDU).

In some embodiments, the first device samples at a particular sampling frequency (e.g., 50 Hz) with each packet as one sample. In other embodiments, the first device samples each time it receives a message, and samples the message continuously over time, for example, for 15 seconds, to obtain a channel state information sequence in time order.

In the embodiment of the application, the channel state information is used for reflecting the current wireless channel condition. In the 802.11n protocol, measurement is performed for each orthogonal frequency division multiplexing (orthogonal frequency division multiplexing, OFDM) subcarrier group, and a CSI matrix corresponding to the OFDM subcarrier group is obtained. The number of rows of the CSI matrix is the number of transmitting antennas, and the number of columns of the CSI matrix is the number of receiving antennas. The element value of each CSI matrix characterizes the channel response on one subcarrier.

The first device includes K receiving antennas, the second device includes J transmitting antennas, and the number of subcarriers is M, and for each communication link, the CSI matrix is specifically developed as follows:

H＝[h1,h2,…hi]，i∈[1,M]

where H is a CSI matrix, hi represents channel state information of the ith subcarrier, and M is the number of subcarriers.

Each packet index to be analyzed eventually obtains K (the number of transmit antennas) ×j (the number of receive antennas) ×m (the number of subcarriers) pieces of channel state information. The subcarrier is a complex number containing a real part and an imaginary part, the real part corresponds to the amplitude of channel state information, the real part corresponds to the phase of the channel state information, and the specific calculation formula of the channel state information hi of the ith subcarrier is as follows:

hi＝|h|e ^j∠θ

where |h| denotes the amplitude of the subcarrier, and θ denotes the phase of the subcarrier.

Step S42: and processing the phase difference or the amplitude of the channel state information aiming at each subcarrier to obtain N signal sets, wherein the phase difference or the amplitude in the N signal sets respectively belong to N different numerical ranges, and N is an integer greater than or equal to 2.

Wherein the phase difference or the amplitude of the channel state information comprises an amplitude, a phase and a phase difference of the channel state information.

In step S41, for each subcarrier, the channel state information sequence of each subcarrier is obtained continuously with time. The first device has a hierarchy of channel state information obtained in step S41, and for this purpose, in step S42, the first device extracts sub-signals in the channel state information to obtain a corresponding signal set. Based on the fact that the subcarriers are complex, for each subcarrier, the phase or amplitude in the channel state information of the subcarrier is extracted according to the time sequence, so that the phase sequence or the amplitude sequence of the channel state information of the subcarrier is obtained, and the sub-signals can be represented by the phase sequence or the amplitude sequence. The first device may implement extraction of the sub-signals in the channel state information according to a phase sequence or an amplitude sequence.

Taking the second device comprising one transmitting antenna and the first device comprising two receiving antennas (antenna a and antenna B), the sampling time is 15 seconds, a subcarrier No. 6 will be described as an example.

The phase sequence of the 6 th subcarrier received by the antenna a is:

Q1＝[a1,a2,…ai]，i∈[1,T]

wherein Q1 is a phase sequence of the 6 th subcarrier received by the antenna a, ai represents a phase corresponding to the 6 th subcarrier acquired by the i-th sampling point in 15 seconds, and T is the number of sampling points in 15 seconds.

The phase sequence of the 6 th subcarrier received by the antenna B is:

Q2＝[b1,b2,…bi]，i∈[1,T]

wherein, Q2 is the phase sequence of the 6 th subcarrier received by the antenna B, bi represents the phase corresponding to the 6 th subcarrier acquired by the i-th sampling point in 15 seconds, and T is the number of sampling points in 15 seconds.

Calculating the phase difference of the No. 6 subcarrier, and making the phase sequence of the No. 6 subcarrier received by the antenna A and the phase sequence of the No. 6 subcarrier received by the antenna B, namely making the difference between two phases at the same moment (or sampling point), so as to obtain a phase difference sequence:

C＝A-B＝[c1,c2,…ci]＝[a1-b1,a2-b2,…ai-bi]，i∈[1,T]

wherein, C is the phase difference sequence of the No. 6 sub-carrier, ci represents the phase difference corresponding to the No. 6 sub-carrier acquired by the No. 6 sampling point in 15 seconds, and T is the number of sampling points in 15 seconds.

In the embodiment of the present application, a plurality of sub-signals may be included in the channel state information including the respiratory information in a single sub-carrier, where the sub-signals may be understood as a minimum subset of the channel state information including the respiratory frequency information in severe noise (layering), and the reason why the channel state information is layered may be affected by the automatic gain control. The inventors have found that the sub-signals also have the ability to reflect the condition of the current radio channel. For the existence of layered channel state information, the sub-signals of each layer can be extracted, so that the layered channel state information can be processed.

Taking the number of subcarriers as 52 as an example, please refer to fig. 5A, the phase difference sequences corresponding to the 6 th subcarrier, the 16 th subcarrier and the 26 th subcarrier in the 52 subcarriers with the sampling time of 15 seconds are shown. As can be seen from fig. 5A, the channel state information of each subcarrier includes two layers spaced apart from each other, each layer representing a sub-signal. Referring to fig. 5A, the sub-carrier No. 6 includes two curves S7 and S8, the curves S7 and S8 are spaced apart, the curve S7 represents one sub-signal, and the curve S8 represents the other sub-signal.

In the embodiment of the present application, for each subcarrier, a plurality of sub-signals included in the channel state information of the subcarrier are respectively extracted, so as to obtain a plurality of signal sets. If the channel state information of the subcarrier includes N sub-signals, N signal sets may be extracted. In other words, from the phase difference sequence of the subcarriers shown in fig. 5A, the subcarriers have N layers, and N signal sets can be extracted. Referring to fig. 5B together, fig. 5B shows that the sub-signals of the sub-carrier No. 6 in fig. 5A are extracted, for example, the curve S7 is extracted, and each phase difference sequence on the curve S7 is obtained to obtain a signal set. Correspondingly, a curve S8 is extracted, and each phase difference sequence on the curve S8 is acquired to obtain another signal set. The sampling time of both signal sets was 15 seconds. One up-and-down fluctuation of the sub-signal of the sub-carrier No. 6 represents one breath of the perception object. As shown in fig. 5B, the window 51 cuts out a respiratory cycle of the sub-signal of the sub-carrier No. 6, where the respiratory cycle may be breathing out first and then breathing in, or may be breathing out first and then breathing out. That is, the curve 51a in the window 51 corresponds to the phase difference sequence at the time of exhalation, and the curve 51b in the window 51 corresponds to the phase difference sequence at the time of inhalation. Alternatively, the curve 51a in the window 51 corresponds to the phase difference sequence at inhalation, and the curve 51b in the window 51 corresponds to the phase difference sequence at exhalation.

In the embodiment of the present application, after extracting the phase difference or the amplitude of the channel state information for each subcarrier, the phase difference or the amplitude sequence in time sequence may be obtained, such as the phase difference sequence described above. Dividing the phase difference or the amplitude with similar numerical values in the phase difference or the amplitude sequence into the same signal set, so as to divide the phase difference or the amplitude with far numerical values through different signal sets. As illustrated in FIG. 5A, the phase difference on curve S7 is numerically closer to the other phase differences on curve S7, and the phase difference on curve S7 is numerically farther from the phase difference on curve S8, so that the phase difference on curve S7 can be divided into the same signal set, the phase difference on curve S7 is in the same range of values, e.g., (-4.5 to-4.6) degrees, the phase difference on curve S8 is divided into another signal set, and the phase difference on curve S8 is in the same range of values, e.g., (-5 to-4.9) degrees.

It will be appreciated that the range of values to which different signal sets correspond is different. That is, for each subcarrier, if it includes N sub-signals, N signal sets corresponding to N different numerical ranges can be obtained through step S42.

In some embodiments, the phase differences corresponding to all the sampling points in the sampling time can be counted, the number of the sampling points corresponding to each phase difference is counted, and then the values with similar values are combined into a set. Taking B in fig. 2 as an example, counting the phase differences corresponding to all sampling points in 15 seconds, and combining the phase differences with similar values into a group can obtain a histogram as shown in fig. 6. As shown in fig. 6, four signal sets (groupings) may be obtained, wherein the first signal set corresponds to a range of values (2.5-2.8) degrees, the second signal set corresponds to a range of values (3.4-4.1) degrees, the third signal set corresponds to a range of values (4.5-4.9) degrees, and the fourth signal set corresponds to a range of values (5.1-5.4) degrees.

Similarly, if the amplitude in the channel state information of the subcarrier is extracted according to the time sequence, the antenna a may obtain a 52 subcarrier channel state information amplitude sequence, where the channel state information of each subcarrier includes a plurality of sub-signals. For each subcarrier, numerically similar amplitudes are divided into the same set of signals. Correspondingly, the antenna B may obtain a 52 subcarrier channel state information amplitude sequence, and perform the same operation on each subcarrier obtained by the antenna B, to extract a signal set of each subcarrier, which is not described herein again.

In the embodiment of the present application, a plurality of sub-signals of channel state information of each sub-carrier may be respectively extracted according to a phase difference, or according to an amplitude, or according to a phase difference and an amplitude at the same time, which is not specifically limited in the present application.

It can be understood that, under the concept of extracting the sub-signals in the channel state information with noise provided in the embodiment of the present application, there may also be other ways to extract the plurality of sub-signals included in the channel state information respectively to obtain a plurality of signal sets, which is not specifically limited in the embodiment of the present application.

Step S43: and screening the N signal sets to obtain a target signal set.

In this embodiment of the present application, for each subcarrier, the first device screens N signal sets in the subcarrier to obtain a target signal set that may represent the subcarrier, that is, may obtain respiratory frequency information of a perception object carried by the subcarrier according to the target signal set, where data of the target signal set is uniformly distributed.

In some embodiments, step S43 may be implemented as: and deleting signal sets with the phase difference or the amplitude less than a preset threshold value in the N signal sets for each subcarrier to obtain the rest signal sets. Screening each signal set of each subcarrier according to each subcarrier, judging whether the phase difference or the number of the amplitude in each signal set is less than a preset threshold value, if so, deleting the signal set, and if not, reserving the signal set. And then obtaining a target signal set according to the signal set left after screening.

For each subcarrier, the preset threshold is set to 10% of the sampling amount (e.g. 2000), wherein the sampling amount is the number of channel state information of the subcarrier obtained by the first device in the sampling time for each subcarrier. And judging whether the total quantity of the phase differences or the amplitudes included in each signal set is less than 200, if so, deleting the signal set. As shown in fig. 6, assuming that the preset threshold is 100, the phase difference or the total amplitude of the third signal set and the fourth signal set is less than the preset threshold, deleting the third signal set and the fourth signal set, and remaining the first signal set and the second signal set, that is, retaining the data of the curve S3 and the curve S4 in fig. 2B, and deleting the data of the curve S1 and the curve S2 with sparse data distribution.

It will be appreciated that the preset threshold may be set according to practical situations, and may also be set to 20% of all sampling amounts, and in other embodiments, the preset threshold may be set to 10% or 20% of sampling points, etc., which is not specifically limited in this application.

In other embodiments, after deleting signal sets of the N signal sets, the number of phase differences or the number of amplitudes is less than a preset threshold, remaining signal sets are obtained, and one or more of the following operations (a first operation and a second operation) are performed on each remaining signal set, and the final remaining signal set is taken as a target signal set.

A first operation: and judging whether the phase difference or the amplitude in a preset time period is absent in each remaining signal set, and if so, deleting the signal set. If not, the signal set is reserved.

The preset time period may be 2 seconds, 5 seconds or 6 seconds, and the preset time period may be set according to the sampling time, for example, set to 10% or 20% of the sampling time, which is not limited in detail in the present application.

Illustratively, taking a preset period of time of 2 seconds as an example, in the first operation, for each remaining signal set, it is determined whether or not there is a loss of the phase difference or the amplitude for 2 seconds in succession for that signal set. As shown in fig. 2B, in the signal set corresponding to the curve S1, if a signal of 3 seconds to 6 seconds is absent, the curve S1 has a loss of phase difference of 2 seconds continuously, and the phase difference absent in 2 seconds may be on the curve S3 or the curve S4.

And a second operation: judging whether the difference between the first missing amount and the second missing amount in each preset time interval of each remaining signal set is higher than a preset missing threshold value or not; the first missing amount corresponds to the highest value of the phase difference or the amplitude missing amount in the preset time interval, the second missing amount corresponds to the lowest value of the phase difference or the amplitude missing amount in the preset time interval, if yes, the signal set is deleted, and if no, the signal set is reserved.

The preset time interval may be a fixed value, for example, may be 1 second, 2 seconds, or 3 seconds, and the preset deletion threshold may be 40%, 50%, or 60%, which is not limited in this application.

In the embodiment of the present application, the phase difference or the amplitude deletion amount may be determined according to a sampling point or a sampling amount. For example, if the number of the phase differences or the amplitudes acquired at each sampling point is set to 100, and if the number of the phase differences or the amplitudes at a certain sampling point on a signal set is 60, it may be determined that the phase differences or the amplitude missing amounts at the sampling point are 40% for the signal set.

Illustratively, taking a preset time interval of 1 second and a preset deletion threshold of 50% as an example, in the second operation, for each remaining signal set, a first deletion amount and a second deletion amount of the signal set per second are acquired. For example, in the 1 st second, the highest value (first missing amount) of the phase difference or the amplitude missing amount of the signal set at that second is acquired to be 60%. And acquiring the signal set, wherein the lowest value (second missing amount) of the phase difference or the amplitude missing amount in the second is 30%, and if the difference between 60% and 30% is smaller than 50%, the signal set is reserved when the signal set is judged to be 1 st second. And similarly, continuing to judge the 2 nd second. If the difference between the first missing amount and the second missing amount in the signal set is judged to be higher than the preset missing threshold value in 2 seconds, deleting the signal set, and continuing to judge the next signal set of the subcarrier.

The first device may perform the above-mentioned determination on the signal sets of all the subcarriers at the same time, or may determine each subcarrier according to the subcarrier sequence, to obtain the target signal set of each subcarrier.

In this embodiment of the present application, after performing one or more operations of the first operation and the second operation on the multiple signal sets of each subcarrier, the first device uses the remaining signal set as the target signal set of the subcarrier. The number of target signal sets may be plural, such as may be 2, for each subcarrier.

In some embodiments, the first device performs one or more of the first operation and the second operation on the plurality of signal sets for each subcarrier, and then detects whether the number of signal sets remaining finally is 2 or greater than 2. If yes, the signal set which is left finally is further screened, and one signal set is selected as a target signal set. If not, the final remaining signal set is directly used as the target signal set. The first device uses the signal set with the smallest value range interval in the finally left signal set as the target signal set. As shown in fig. 6, the final remaining signal sets include a first signal set and a second signal set, where the range of the value range of the first signal set is 0.3 (2.8-2.5) smaller than the range of the value range corresponding to the second signal set by 0.7 (4-3.4.1), and the first signal set is regarded as the target signal set. Referring to fig. 2B, the remaining first signal set and the second signal set correspond to curves S3 and S4, respectively, that is, S4 with the smallest line width of the curves is selected as the target signal set.

In some embodiments, after the first device selects the target signal set, the first device performs data processing, such as linear interpolation and padding, on the target signal set, and then uses the processed target signal set for processing in the following steps.

Step S44: and performing fast Fourier transform on the target signal set to obtain a frequency domain signal corresponding to the target signal set.

In this embodiment of the present application, for each subcarrier, the first device performs discrete fourier transform on a target signal set of the subcarrier, extracts frequency domain information of the target signal set, and obtains a frequency domain signal corresponding to the target signal set. After conversion to a frequency domain signal, the frequency components in the target signal set can be conveniently analyzed for processing in the frequency domain.

After step S43, the first device may obtain a target signal set corresponding to each subcarrier, and the first device performs fast fourier transform on the target signal sets of all subcarriers to obtain frequency domain signals corresponding to each target signal set. Fig. 7 shows the result of performing the fft on the target signal set of all the subcarriers, where each curve represents the result of performing the fft on the target signal set corresponding to one subcarrier, and it can be seen that the frequency is between 10 times per minute and 20 times per minute, and the frequency has good amplitude characteristics.

Step S45: and obtaining a frequency domain candidate respiratory frequency and a respiratory energy ratio corresponding to the frequency domain candidate respiratory frequency according to the respiratory frequency band and the frequency domain signal.

In the embodiment of the present application, after obtaining the frequency domain signals corresponding to the target signal sets of each subcarrier, the first device obtains, according to each frequency domain signal, a frequency domain candidate respiratory frequency corresponding to each subcarrier and a respiratory energy ratio corresponding to the frequency domain candidate respiratory frequency.

The respiratory frequency band is the respiratory power value of the human body under normal condition. For example, the respiratory frequency band may be 0.1-0.6Hz.

Referring to fig. 8, step S45 may be implemented as follows:

step S81: and acquiring a sub-frequency domain signal with the frequency in the respiratory frequency band in the frequency domain signal.

Illustratively, taking a respiratory frequency range of 0.1-0.6Hz as an example, for a frequency domain signal of each subcarrier, the first device uses a signal with a frequency within 0.1-0.6Hz in the frequency domain signal as a sub-frequency domain signal of the subcarrier, to obtain the sub-frequency domain signal of the subcarrier.

Step S82: and taking the ratio of the sum of the amplitudes of the sub-frequency domain signals to the sum of the amplitudes of the frequency domain signals as the breathing energy ratio.

In this embodiment of the present application, for each subcarrier, the first device uses a ratio of a sum of magnitudes of a sub-frequency domain signal of the subcarrier to a sum of magnitudes of a frequency domain signal as a respiratory energy ratio, specifically, the first device sums each magnitude in the sub-frequency domain signal of the subcarrier to obtain a sum of magnitudes of the sub-frequency domain signal, sums each magnitude in the frequency domain signal of the subcarrier to obtain a sum of magnitudes of the frequency domain signal, calculates a ratio of the sum of magnitudes of the sub-frequency domain signal to the sum of magnitudes of the frequency domain signal, and uses the ratio as the respiratory energy ratio, where the respiratory energy ratio corresponds to the subcarrier.

In some embodiments, the calculated breathing energy ratio is: and adding the amplitudes of all signals with the frequency in the respiratory frequency band in the frequency domain signal to serve as a numerator, adding all the amplitudes of the frequency domain signal to serve as a denominator, and obtaining the ratio of the numerator to the denominator as the respiratory energy ratio.

Step S83: and taking the frequency corresponding to the maximum amplitude value in the sub-frequency domain signal as the frequency domain candidate respiratory frequency.

In this embodiment of the present application, for each subcarrier, the first device obtains a signal with the largest amplitude in the sub-frequency domain signal of the subcarrier, and uses a frequency corresponding to the signal as the frequency domain candidate respiratory frequency. In other embodiments, the first device calculates a respiratory signal-to-noise ratio (BNR) of each subcarrier, and uses a frequency corresponding to the maximum respiratory signal-to-noise ratio of the subcarrier as a frequency-domain candidate respiratory frequency of the subcarrier. The respiratory signal-to-noise ratio is calculated as the ratio of the maximum amplitude in the sub-frequency domain signal to the sum of the amplitudes of the frequency domain signals.

Step S46: and performing autocorrelation calculation on the target signal set to obtain an autocorrelation coefficient corresponding to the target signal set.

In the embodiment of the present application, the first device performs autocorrelation calculation on the target signal set of each subcarrier, that is, calculates an autocorrelation function (Autocorrelation Function, ACF) on the target signal set, to obtain an autocorrelation coefficient (Autocorrelation Coefficient, AC) corresponding to the target signal set of each subcarrier.

Referring to fig. 9 together, fig. 9 shows the result of autocorrelation calculation performed on a target signal set of a subcarrier, where a curve represents the result of autocorrelation analysis on the subcarrier, and it can be seen that the autocorrelation coefficient is greater between 12 times/min and 16 times/min.

Step S47: and obtaining a time domain candidate respiratory frequency and a first autocorrelation coefficient corresponding to the time domain candidate respiratory frequency according to the respiratory frequency band and the autocorrelation coefficient.

In the embodiment of the present application, after obtaining the autocorrelation coefficients corresponding to the target signal set of each subcarrier, the first device obtains, according to each autocorrelation coefficient, a time-domain candidate respiratory frequency corresponding to each subcarrier and a first autocorrelation coefficient corresponding to the time-domain candidate respiratory frequency.

Referring to fig. 10 together, step S47 may be specifically implemented as:

step S101: and taking the frequency corresponding to the maximum autocorrelation coefficient in the respiratory frequency band as the candidate respiratory frequency in the time domain.

In the embodiment of the application, signals with the frequency within 0.1-0.6Hz in the target signal set are divided into a first set by using the respiratory frequency range of 0.1-0.6Hz, and the first set is obtained. Each channel state information in the first signal set has its corresponding autocorrelation coefficient, and the frequency corresponding to the signal with the largest autocorrelation coefficient in the first signal set is used as the candidate respiratory frequency in the time domain. In other words, after the autocorrelation coefficients corresponding to the target signal sets of the subcarriers are obtained, for each subcarrier, the autocorrelation coefficients with frequencies in the respiratory frequency band are found, and the autocorrelation coefficients in the respiratory frequency band are obtained. And taking the frequency corresponding to the autocorrelation coefficient with the largest value in the autocorrelation coefficients of the respiratory frequency band as the candidate respiratory frequency in the time domain.

Step S102: and taking the autocorrelation coefficient corresponding to the time domain candidate respiratory frequency as a first autocorrelation coefficient.

I.e. the largest autocorrelation coefficient in the first set of signals is the first autocorrelation coefficient. In other words, the autocorrelation coefficient having the largest value among the autocorrelation coefficients of the respiratory band is taken as the first autocorrelation coefficient.

Step S48: and outputting breath detection information according to the frequency domain candidate breath frequency, the breath energy ratio, the time domain candidate breath frequency and the first autocorrelation coefficient.

In this embodiment of the present application, through the foregoing steps S44 to S47, the first device performs frequency domain and time domain calculation on the target signal set of each subcarrier, so as to respectively obtain the frequency domain candidate respiratory frequency of each subcarrier, the respiratory energy ratio corresponding to the frequency domain candidate respiratory frequency, the first autocorrelation coefficient corresponding to the time domain candidate respiratory frequency, and the first device performs joint judgment on the obtained frequency domain candidate respiratory frequency, respiratory energy ratio, time domain candidate respiratory frequency, and the first autocorrelation coefficient in the time domain and the frequency domain, so as to obtain reliable respiratory detection information.

In an embodiment of the present application, step S48 may be implemented to output the frequency domain candidate respiratory rate or the time domain candidate respiratory rate as the respiratory detection information when it is detected that the respiratory energy ratio is greater than the corresponding first threshold and the first autocorrelation coefficient is greater than the corresponding second threshold.

The first threshold value and the second threshold value can be the same or different, and the first threshold value and the second threshold value are determined according to actual conditions, for example, the value ranges of the first threshold value and the second threshold value can be set to be 0.1-10.

When the detected respiratory energy ratio is larger than the corresponding first threshold value, the probability that the frequency domain candidate respiratory frequency is the respiratory frequency of the perception object is high. When the first autocorrelation coefficient is detected to be larger than the corresponding second threshold value, the probability that the time domain candidate respiratory frequency is the respiratory frequency of the perception object is high. And simultaneously judging whether the breathing energy ratio is larger than a corresponding first threshold value and whether the first autocorrelation coefficient is larger than a corresponding second threshold value, so that the joint judgment of the breathing frequency in the time domain and the frequency domain is realized. When the breathing energy ratio is larger than the corresponding first threshold value and the first autocorrelation coefficient is larger than the corresponding second threshold value, the probability that the frequency domain candidate breathing frequency and the time domain candidate breathing frequency are the breathing frequency of the perception object is high, the consistency of the time domain and the frequency domain is also shown, and the frequency domain candidate breathing frequency or the time domain candidate breathing frequency can be guaranteed to be the breathing frequency of the perception object.

In other embodiments, step S48 may be implemented to output the frequency domain candidate respiratory rate or the time domain candidate respiratory rate as the respiratory detection information when it is detected that the frequency domain candidate respiratory rate is close to the time domain candidate respiratory rate and one or more of the respiratory energy ratio and the first autocorrelation coefficient are greater than the corresponding threshold values.

Wherein one or more of the respiratory energy ratio and the first autocorrelation coefficient is greater than a corresponding threshold comprises: the respiratory energy ratio is greater than a corresponding first threshold, the first autocorrelation coefficient is greater than a corresponding second threshold, or the respiratory energy ratio is greater than a corresponding first threshold and the first autocorrelation coefficient is greater than a corresponding second threshold. That is, when the frequency-domain candidate respiratory rate is detected to be close to the time-domain candidate respiratory rate and the respiratory energy ratio is greater than the corresponding first threshold, or when the frequency-domain candidate respiratory rate is detected to be close to the time-domain candidate respiratory rate and the first autocorrelation coefficient is greater than the corresponding second threshold, or when the frequency-domain candidate respiratory rate is detected to be close to the time-domain candidate respiratory rate and the respiratory energy ratio is greater than the corresponding first threshold and the first autocorrelation coefficient is greater than the corresponding second threshold, the frequency-domain candidate respiratory rate or the time-domain candidate respiratory rate is output as respiratory detection information.

Referring to fig. 11 together, step S48 may be specifically implemented as:

step S111: judging whether the difference value between the frequency domain candidate respiratory frequency and the time domain candidate respiratory frequency is smaller than a third threshold value;

the third threshold value may be set according to practical situations, and the value range of the third threshold value may be 0.1-2 times Per Minute (BPM). If the difference value between the frequency domain candidate respiratory frequency and the time domain candidate respiratory frequency is smaller than the third threshold value, the frequency domain candidate respiratory frequency and the time domain candidate respiratory frequency are similar. By verifying that the frequency domain candidate respiratory frequency is similar to the time domain candidate respiratory frequency, in order to avoid false detection caused by separate time domain or frequency domain analysis and calculation, accuracy is improved, and respiratory information of a perception object in a target signal set is ensured.

If not, step S112 deletes the target signal set.

In some embodiments, if the difference between the frequency domain candidate respiratory rate and the time domain candidate respiratory rate is not less than the third threshold, it indicates that the frequency domain candidate respiratory rate and the time domain candidate respiratory rate are far apart, and the maximum probability cannot obtain respiratory information of the perception object from the target signal set.

In the embodiment of the present application, the target signal set is deleted, that is, the target signal set is not processed, but the target signal set of the next subcarrier is processed. Illustratively, the number 6 subcarrier is processed, and if it is determined in step S81 that the difference between the frequency domain candidate respiratory frequency and the time domain candidate respiratory frequency of the number 6 subcarrier is greater than or equal to the third threshold, the target signal set of the number 6 subcarrier is deleted. I.e. no detection is performed on subcarrier 6, and the next subcarrier is continued to be processed.

If yes, step S113: judging whether one or more of the breathing energy ratio and the first autocorrelation coefficient is greater than a corresponding threshold;

in the embodiment of the present application, step S113 includes: whether the breathing energy ratio is larger than a first threshold value or whether the first autocorrelation coefficient is larger than a second threshold value or whether the breathing energy ratio is larger than the first threshold value or whether the first autocorrelation coefficient is larger than the second threshold value is judged. If any of the above determinations is yes, step S114 is performed. If all the above three determinations are negative, step S112 is performed.

The target signal set is deleted, that is, the subcarrier corresponding to the target signal set is not processed, but the next subcarrier is processed.

If yes, step S114: and outputting the frequency domain candidate respiratory frequency or the time domain candidate respiratory frequency as respiratory detection information.

The order of execution of the above-described step S111 and step S113 may be changed.

In other embodiments, referring to fig. 12, step S83 may be implemented as the following steps:

step S121: when the frequency domain candidate respiratory frequency is detected to be similar to the time domain candidate respiratory frequency and one or more of the respiratory energy ratio and the first autocorrelation coefficient are greater than the corresponding threshold, the sum of the respiratory energy ratio and the first autocorrelation coefficient is recorded.

In this embodiment of the present application, for each subcarrier, the first device performs the determination in step S111 and step S113 on the frequency domain candidate respiratory rate, the respiratory energy ratio, the time domain candidate respiratory rate, and the first autocorrelation coefficient of the subcarrier, and if the determination result is yes, records the sum of the respiratory energy ratio and the first autocorrelation coefficient of the subcarrier, and uses the sum of the respiratory energy ratio and the first autocorrelation coefficient of the subcarrier as the weight of the subcarrier. If the judgment result is negative, the sum of the respiratory energy ratio of the subcarrier and the first autocorrelation coefficient is not recorded.

Step S122: all recorded respiratory energy ratios are summed with the first autocorrelation coefficients as denominators.

In this embodiment of the present application, the first device performs the judgment of step S111 and step S113 on all subcarriers, so as to obtain the sum of the respiratory energy ratio corresponding to the subcarriers meeting the conditions (that is, the judgment result of step S111 and step S113 is yes) and the first autocorrelation coefficient, and adds the sum of the respiratory energy ratio of all the recorded subcarriers meeting the conditions and the first autocorrelation coefficient as the denominator. I.e. the weights of all eligible subcarriers are summed as denominators.

Step S123: the maximum value of the sum of the recorded respiratory energy ratio and the first autocorrelation coefficient is taken as the molecule.

In the embodiment of the present application, the recorded ownership values are obtained, that is, the weights of all subcarriers meeting the conditions (that is, the judgment result of step S111 and step S113 is yes) are obtained, and the largest weight is taken as a molecule.

Step S124: and detecting that the ratio of the numerator to the denominator is smaller than a third threshold value, and outputting the frequency domain candidate respiratory rate or the time domain candidate respiratory rate as respiratory detection information.

The third threshold may be set according to practical situations, for example, may be 50%, which is not specifically limited in this application.

In this embodiment of the present application, the first device determines whether a ratio of a weight of the maximum value to a sum of weights of all the subcarriers that meet the condition is smaller than a third threshold. If the frequency domain candidate respiratory rate is smaller than the specific condition, the consistency among all the subcarriers meeting the conditions is shown, the consistency among all the target signal sets is ensured, false detection cannot be caused by the special condition of the single subcarrier, and the frequency domain candidate respiratory rate or the time domain candidate respiratory rate can be used as respiratory detection information. If the frequency is not smaller than the preset threshold, the special condition that a single subcarrier possibly exists at the moment is indicated, and the frequency domain candidate respiratory rate or the time domain candidate respiratory rate at the moment cannot be output as a respiratory detection junction. For this purpose, the respiration detection information may be output as undetectable respiration, or no respiration detectable in the current environment.

In the embodiment of the present application, the existing respiration detection method cannot process layered channel state information, and the existing respiration detection method can only process noiseless or slightly noisy channel state information, which generally performs fast fourier transform on the channel state information alone, or performs autocorrelation function calculation on the channel state information alone. The respiration detection method provided by the application obtains the target signal set of each subcarrier by extracting the sub-signals of the layered channel state information of each subcarrier. And performing fast Fourier transform and autocorrelation function calculation on the target signal set of each subcarrier to obtain the frequency domain candidate respiratory rate, respiratory energy ratio, time domain candidate respiratory rate and first autocorrelation coefficient of the subcarrier at the same time. And judging the possibility that the subcarrier carries the respiratory information of the perception object by simultaneously and jointly judging the frequency domain candidate respiratory frequency, the respiratory energy ratio, the time domain candidate respiratory frequency and the first autocorrelation coefficient of the subcarrier, so as to verify whether the frequency domain candidate respiratory frequency or the time domain candidate respiratory frequency is the respiratory frequency of the perception object, and ensuring the accuracy of the respiratory detection information through double verification. Further, the frequency domain candidate respiratory rate or the time domain candidate respiratory rate of all the subcarriers are verified, consistency among data of all target signal sets is guaranteed, false detection cannot be caused by special conditions of single carriers, and accuracy of respiratory detection information is further provided.

Referring to fig. 13 together, fig. 13 is a schematic diagram of a main interface of a ue according to an embodiment of the present application.

It is appreciated that the breath detection method provided by embodiments of the present application may be applied to or implemented as a breath detection application. Take the example of the installation of a breath detection application on the user device 104 shown in fig. 1. The user device 104 installs a breath detection application thereon and the user (e.g., the perception object 103) clicks on the breath detection application on the main interface before going to sleep. The user equipment 104 responds to the clicking operation of the user, the user equipment 104 establishes communication with the second equipment 101, and the user equipment 104 can acquire channel state information of Wi-Fi signals from the second equipment 101 or the end server 105, so that the breath detection method provided by the embodiment of the application is executed according to the channel state information of the Wi-Fi signals, and breath detection information of the user is obtained. Alternatively, the user device 104 obtains the environmental change information from the second device 101 or the end server 105, and further analyzes the respiration detection information of the user according to the environmental change information. Referring also to fig. 14, the user device 104 enters the breath detection information interface in response to the user clicking on the breath detection application. After the user device 104 obtains the environmental change information or the respiration detection information, corresponding respiration detection information including body movement, a state of deep sleep, and a change in the respiration frequency at night (respiration detection information) detected during the night sleep recording time of 8 hours 9 minutes is displayed on the respiration detection information interface. The user device 104, the first device 102 or the cloud server 105 may further have information analysis processing energy, and further analyze and process according to the environmental change information, and output more detailed respiration detection information includes: sleep score, night sleep duration, awake duration, number of wakefulness, breathing frequency, respiratory variability index, respiratory instructions, and detailed respiratory beneficiation data acquired during the day sleep. As shown in fig. 14, the user clicks on the next page control in the breath detection interface and the user device 104 displays this more detailed breath detection information as shown in fig. 15.

Referring to fig. 16, fig. 16 is a flowchart illustrating another breath detection method according to an embodiment of the present disclosure.

Step S161: the first device receives Wi-Fi signals of at least one second device.

Step S162: the first device determines N signal sets according to the phase difference or the amplitude of the channel state information of Wi-Fi signals of at least one second device, wherein the phase difference or the amplitude in the N signal sets respectively belong to N different numerical ranges, and N is an integer greater than or equal to 2.

Step S163: the first device determines environmental change information from the N signal sets, the environmental change information including breath detection information.

Preferably, step S162 may specifically include: extracting a phase difference of channel state information for each subcarrier; dividing phase differences with similar values into a signal set to obtain N signal sets; or extracting the amplitude of the channel state information for each subcarrier; dividing the amplitude with similar values into a signal set to obtain N signal sets.

Preferably, step S163 may specifically include: screening the N signal sets to obtain a target signal set; performing frequency domain analysis processing on the target signal set to obtain frequency domain candidate respiratory frequency and respiratory energy ratio corresponding to the frequency domain candidate respiratory frequency; performing time domain analysis processing on the target signal set to obtain a time domain candidate respiratory frequency and a first autocorrelation coefficient corresponding to the time domain candidate respiratory frequency; and outputting environment change information according to the frequency domain candidate respiratory frequency, the respiratory energy ratio, the time domain candidate respiratory frequency and the first autocorrelation coefficient.

A flow chart of another respiration detection method is given below by taking the processing of the phase difference of the channel state information as an example.

Referring to fig. 17, fig. 17 is a flowchart of another breath detection method according to an embodiment of the present application. The first device shown in fig. 1 is exemplified as the respiration detection method described below.

Step S170: starting. In the embodiment of the present application, in response to performing the breath detection method of the present application, the first device starts to receive the data packet sent by the second device through the M subcarriers, and measures CSI raw information of each data packet transmitted by each subcarrier therefrom. When the breath detection application is installed on the first device, the operation of executing the breath detection method is that a user clicks the breath detection application on the first device. In other embodiments, the first device may perform the breath detection method of the present application upon power-on. The present application is not particularly limited thereto.

Step S170 corresponds to step S41 in fig. 4, and specific content may refer to step S41.

Step S171: the phase differences of the channel state information of all subcarriers are calculated. In this embodiment of the present application, in step S170, the first device receives channel state information of M subcarriers, and calculates, for each subcarrier, a phase difference of the channel state information of the subcarrier, so that the phase differences of the channel state information of all subcarriers can be calculated.

Step S172: extracting the sub-signals. In the embodiment of the present application, the phase difference of the channel state information of the sub-carrier is obtained in step S171, and the first device extracts the sub-signal according to the phase difference of the channel state information of the sub-carrier. Wherein the sub-signals are the above-mentioned signal sets.

Step S171 and step S172 correspond to step S42 in fig. 4, and specific contents may refer to step S42.

In some embodiments, step S172 is specifically the following steps:

step S1721: counting and counting the bin in the histogram.

In the embodiment of the present application, the phase difference of the channel state information of all subcarriers is used as the bin in the histogram, and counting statistics is performed on the bin in the histogram. Illustratively, the number of respective phase differences (e.g., 5.5 degrees, 5 degrees, 4.5 degrees, etc.) is counted. As shown in fig. 6, the number of bins can be obtained.

Step S1722: and removing sampling points corresponding to the count with too small proportion.

In this embodiment of the present application, the first device may delete the sampling points (i.e. the phase differences) corresponding to the counts with too small a duty ratio, where the number of the sampling points counted is less than a preset threshold (e.g. 10). As shown in fig. 6, if the statistical number of the phase differences of 5.1 degrees is smaller than 10, the phase differences of 5.1 degrees can be deleted, that is, the total number of the phase differences of 5.1 degrees is smaller than 10 among the phase differences of the channel state information of all the subcarriers.

Step S1722 corresponds to a specific implementation of step S43 in fig. 4, for example, for each subcarrier, deleting signal sets with the phase difference or the amplitude less than a preset threshold value from the N signal sets, so as to obtain remaining signal sets, which are not described herein.

Step S1723: numerically consecutive bins are combined into one bin packet.

In the embodiment of the present application, the first device merges the numerically continuous bins into one bin packet, that is, merges the numerically continuous phase differences into one bin packet, that is, merges to obtain a sub-signal. As shown in fig. 6, four bin groups are obtained, the group spacing of the bin groups is (2.5-2.8), (3.4-4.1), (4.5-4.9) and (5.1-5.4), and the four groups correspond to four signal sets.

Step S1723 may be performed first and then step S1722 may be performed, which will not be described herein.

Step S1724: a determination is made for each bin packet whether there is a data loss of more than 2 seconds.

In the embodiment of the present application, the first device determines a bin in each bin packet, and determines whether there is a data loss exceeding 2 seconds in the bin packet.

Step S1724 corresponds to the first operation in fig. 4, and the specific content may refer to the first operation.

If yes, step S1725: the bin packet is deleted. The first device deletes the bin packet, i.e., deletes the signal set.

If not, step S1726: a determination is made for each bin packet as to whether the amount of missing data per second fluctuates by greater than 50%.

In this embodiment of the present application, the first device determines each bin packet, and determines whether there is a data loss exceeding 2 seconds in the bin packet, and if not, performs step S1726. I.e. the first device, after performing step S1724, gets the remaining set of signals. Step S1726 judges the remaining signal set, and judges whether or not the difference between the highest value of the phase difference loss amount and the lowest value of the phase difference loss amount per second in the remaining signal set is higher than 50%. For example, for the first second of signal set a, the highest value of the phase difference deficiency is detected to be 60% in the first second, and the highest value of 60% corresponds to sampling point a. The lowest value of the phase difference missing amount in the first second is detected to be 20%, the lowest value 20% corresponds to the sampling point b, 60% -20% difference is smaller than 50%, and the signal set is reserved when the signal set is judged to be 1 st second.

Step S1724 corresponds to the second operation in fig. 4, and reference may be made to the second operation for specific content.

In the embodiment of the present application, step S1722, step S1724, step S1725, and step S1726 correspond to step S43 in fig. 4, and reference may be made to step S43 for specific details.

The first device determines whether the fluctuation of the missing data amount per second is higher than 50% for each bin packet, if so, performs step S1725, and if not, performs step S1727: the linear interpolation complements the data.

In the embodiment of the present application, the first device obtains the target signal set after executing steps S1722 to S1726. The first device may perform data processing, such as linear interpolation, on the set of target signals, after which the processed set of target signals is used for processing in the following steps.

Step S1728: and acquiring a respiration sub-signal of the current carrier.

In this embodiment of the present application, after performing linear interpolation on the target signal set by the first device, the breathing sub-signal of the current carrier, that is, the target signal set of the current carrier is obtained. The first device obtains the target signal set of all carriers and then uses the target signal set in the subsequent step.

Step S173: and performing joint calculation on the time domain and the frequency domain.

Step S173 corresponds to step S44, step S45, step S46 and step S47 in fig. 4, and specific details can refer to step S44, step S45, step S46 and step S47.

In some embodiments, step S173 is specifically the following steps:

step S1731: a fast fourier transform is performed.

In this embodiment of the present application, the first device performs fast fourier transform on the target signal set (i.e. the respiratory sub-signal obtained in step S1728) to obtain a frequency domain signal corresponding to the target signal set.

Step S1731 corresponds to step S44 in fig. 4.

Step S1732: the respiratory energy ratio, specifically the duty cycle of the sum of the amplitudes of the respiratory bands to the sum of the amplitudes of all the frequencies, is calculated.

The first device takes the sum of the amplitudes of all signals with the frequency in the respiratory frequency band in the frequency domain signal as a numerator, and takes the sum of all the amplitudes of the frequency domain signal obtained after the fast fourier computation in step S173 as a denominator, wherein the ratio of the numerator to the denominator is the respiratory energy ratio.

Step S1732 corresponds to steps S81 to S82 in fig. 8, and specific contents may refer to steps S81 to S82.

Step S1733: the respiratory signal-to-noise ratio, in particular the duty cycle of the maximum amplitude of the respiratory band to the sum of the amplitudes of all the frequencies, is calculated.

In the embodiment of the application, the first device calculates a ratio of a maximum amplitude value in the sub-frequency domain signal to a sum of amplitude values of the frequency domain signals to obtain a respiration signal-to-noise ratio.

Step S1734: frequency domain analysis: calculating frequency domain candidate respiratory frequency and respiratory energy ratio, specifically, taking the frequency corresponding to the highest respiratory signal-to-noise ratio as the frequency domain candidate respiratory frequency.

Step S1732 to step S1734 correspond to step S45 in fig. 4, and step S1734 corresponds to step S83 in fig. 8.

Step S1735: an autocorrelation calculation is performed. In this embodiment of the present application, the first device performs autocorrelation calculation on the target signal set (i.e. the respiratory sub-signal obtained in step S1728) to obtain an autocorrelation coefficient corresponding to the target signal set.

Step S1735 corresponds to step S46 in fig. 4, and specific content may refer to step S46.

Step S1736: time domain analysis: calculating the time domain candidate respiratory frequency, taking an autocorrelation coefficient corresponding to the time domain candidate respiratory frequency as a first autocorrelation coefficient, and specifically taking a frequency corresponding to the highest position of the autocorrelation coefficient in the respiratory frequency band as the time domain candidate respiratory frequency.

In the embodiment of the present application, step S1736 corresponds to step S47 in fig. 4. The specific steps of step S1736 may include steps S101 to S102 shown in fig. 10, and are not described herein.

Step S174: and carrying out joint judgment on the time domain and the frequency domain.

In this embodiment of the present application, the first device performs joint determination on the frequency domain candidate respiratory rate, the respiratory energy ratio, the time domain candidate respiratory rate, and the first autocorrelation coefficient obtained in step S173, and outputs a detection result.

Step S174 corresponds to step S48 in fig. 4, and specific details may refer to step S48.

In some embodiments, step S174 is specifically the following:

step S1741: and judging whether the error between the time domain candidate respiratory frequency and the frequency domain candidate respiratory frequency is smaller than a threshold value. In an embodiment of the present application, the first device determines whether a difference between the frequency domain candidate respiratory rate and the time domain candidate respiratory rate is less than a third threshold.

Step S1741 corresponds to step S111 in fig. 11, and specific details may refer to step S111.

If not, step S1742: the respiratory sub-signal is deleted. In this embodiment of the present application, if the first device determines that the difference between the frequency-domain candidate respiratory rate and the time-domain candidate respiratory rate is greater than or equal to the third threshold, then a difference between results obtained by determining that a target signal set (i.e., a respiratory sub-signal) is subjected to time-domain and frequency-domain analysis is greater, where the target signal set is not suitable for respiratory detection, and the target signal set is to be used for respiratory detection.

Step S1742 corresponds to step S112 in fig. 11, and specific details may refer to step S112.

If yes, step S1743: whether the breathing energy ratio and the first autocorrelation coefficient are greater than a threshold is determined.

In this embodiment of the present application, after the first device performs step S1741, it determines that the error between the time-domain candidate respiratory rate and the frequency-domain candidate respiratory rate is smaller than the third threshold, and then the first device continues to determine whether one or more of the respiratory energy ratio and the first autocorrelation coefficient is greater than a corresponding threshold, for example, whether the respiratory energy ratio is greater than the first threshold, or whether the first autocorrelation coefficient is greater than the second threshold, or whether the respiratory energy ratio is greater than the first threshold and simultaneously whether the first autocorrelation coefficient is greater than the second threshold.

Step S1743 corresponds to step S113 in fig. 11, and specific content may refer to step S113.

Step S1744: and counting all the respiratory sub-signals meeting the conditions, and taking the sum of the respiratory energy ratio of the respiratory sub-signals meeting the conditions and the first autocorrelation coefficient as a weight.

In this embodiment of the present application, the first device counts the breathing sub-signals with the "yes" judgment results of step S1741 and step S1743, records the breathing energy ratio and the first autocorrelation coefficient of the breathing sub-signals meeting the above conditions, and uses the sum of the breathing energy ratio and the first autocorrelation coefficient of each subcarrier as the weight of the subcarrier.

Step S1745: and judging whether the weight ratio of the weight of the highest item in the candidate respiratory frequencies in all candidate respiratory frequencies is smaller than a threshold value.

In an embodiment of the present application, the first device sums the sum of all the recorded respiratory energy ratios and the first autocorrelation coefficients as a denominator and takes the maximum value of the sum of the recorded respiratory energy ratios and the first autocorrelation coefficients as a numerator. And judging whether the weight ratio of the highest weight item in the candidate respiratory frequencies is smaller than a threshold value or not, namely judging whether the ratio of the maximum value of the sum of the respiratory energy ratio and the first autocorrelation coefficient in all recorded respiratory energy ratios and the sum of the first autocorrelation coefficient is smaller than a third threshold value or not.

Step S1746: and taking the candidate respiratory rate as a detection result. The first device detects that the determination result of step S1745 is yes, and outputs the frequency domain candidate respiratory rate or the time domain candidate respiratory rate as respiratory detection information.

Step S1744 to step S1746 correspond to step S121 to step S124 in fig. 12, and specific content may refer to step S121 to step S124.

No, step S1747: no detectable respiration in the environment.

In the embodiment of the present application, the first device detects that the determination result in step S1745 is no, and the first device determines that there is no detectable breath in the current environment.

Step S175: and outputting a respiration detection result.

In the embodiment of the present application, the first device outputs the breath detection result including the breath detection information, as shown in fig. 14 and 15.

Referring to fig. 18, fig. 18 is a flowchart of another breath detection method according to an embodiment of the present application.

Fig. 18 differs from fig. 17 in that: step S1728 of fig. 17 is different from step S1828 of fig. 18 in that step S1828 uses a respiration sub-signal with a smaller current range as a carrier respiration signal. In the embodiment of the application, the first device takes the breathing sub-signals with smaller group distances as carrier breathing signals. As shown in fig. 6, the range of the first signal set is 0.3 (2.8-2.5) smaller than the range of the second signal set corresponding to the range of the first signal set of 0.7 (4-3.4.1), and the first signal set is regarded as the target signal set. Therefore, the calculation amount of the subsequent step S173 and step S174 of the user is reduced, and the inventor finds that the data of the breathing sub-signals with smaller group distance are more concentrated, and the analysis effect is better.

Referring to fig. 19, fig. 19 is a schematic software architecture diagram of an electronic device according to an embodiment of the present application.

Other architectures may also be adopted for the electronic device (e.g., the first device, the second device, or the user device), and the embodiment of the present invention may also be exemplified by a Harmony system, which illustrates a software architecture of the electronic device.

In some embodiments, harmony includes four layers, from bottom to top, a kernel layer, a system base capability layer, a framework layer, and an application layer, respectively.

The Harmony system adopts a multi-kernel design, optionally including a Linux kernel, a Harmony microkernel, and a Liteos. By this design, devices with different device capabilities can select the appropriate system kernel. The kernel layer also includes a kernel abstraction layer (Kernal Abstract Layer) that provides basic kernel capabilities for other Harmony layers, such as process management, thread management, memory management, file system management, network management, peripheral management, and the like.

The system basic service layer is a core capability set of the Harmony system, and supports the Harmony system to provide service for application services through the framework layer in a multi-device deployment scene. The layer optionally comprises the following parts:

system basic capability subsystem set: the method provides basic capability for operation, scheduling, migration and other operations of the distributed application on the Harmony system multi-device, and consists of a distributed soft bus, distributed data management and file management, distributed task scheduling, ark operation, distributed security, privacy protection and the like. Wherein the ark runtime provides a C/c++/JavaScript multi-language runtime and underlying system class library, and also provides runtime for Java programs (i.e., applications or parts of the framework layer developed in Java language) that are static using the ark compiler.

Basic software service subsystem set: common and general software services are provided for the Harmony system, and the Harmony system consists of subsystems such as graphic images, distributed media, distributed AI, multimode input, MSDP & DV, event notification, telephone service, distributed DFX and the like. The basic software service subsystem set can be tailored according to the deployment environment of different device forms, and the interior of each subsystem can be tailored according to the functional granularity.

Enhancement software service subsystem set: the capability-enhanced software service for providing the Harmony system for different devices is composed of subsystems such as tablet service software, intelligent screen service software, car machine service software, ioT service software and the like. The enhanced software service subsystem set can be cut according to the deployment environments of different equipment forms, the granularity of the subsystem can be cut, and the interior of each subsystem can be cut according to the functional granularity.

The HongMony Drive Framework (HDF) and the hardware abstraction adaptation layer (HAL) are the ecologically open foundation of the hardware of the Harmony system, provide hardware capability abstraction for the hardware upwards, and provide development frameworks and running environments of various peripheral drivers downwards.

Hardware service subsystem set: the system provides public and adaptive hardware service for the Harmony system, and consists of hardware service subsystems such as a universal Sensor, a position, a power supply, USB, biological identification and the like. The hardware service subsystem set can be cut according to the deployment environment of different equipment forms and the functional granularity inside each subsystem.

Proprietary hardware service subsystem: differentiated hardware services for different devices are provided for the Harmony system, optionally including subsystems of tablet-specific hardware services, car-specific hardware services, wearable-specific hardware services, ioT-specific hardware services, and the like. The proprietary hardware service subsystem may be tailored to subsystem granularity, and each subsystem temporal may be tailored to functional granularity.

The framework layer provides a Java/C/C++/JavaScript multi-language user program framework and a meta-capability framework for the application program of the Harmony system, and a multi-language framework API for opening various software and hardware services.

The application layer comprises system applications and three-party applications, and can comprise cameras, gallery, calendar, call, map, navigation, WLAN, bluetooth, music, video, short message and other applications. Applications in the Harmony system build applications based on AA and FA.

Referring to fig. 20, a schematic structural diagram of an embodiment of the second device is provided herein. May be used to perform the breath detection methods of the embodiments of figures 4, 8, 10, 11, 12, 16, 17, 18.

As shown in fig. 20, the second device 101 may include: processor 201, memory 202, etc. These components are connected and communicate via one or more buses.

The processor 201 is a control center of the second device, and connects various parts of the entire base station using various interfaces and lines, by running or executing software programs and/or modules stored in the memory 202, and invoking data stored in the memory 202 to perform various functions of the base station and/or process data. The processor 201 may be composed of an integrated circuit (integrated circuit, simply referred to as IC), for example, may be composed of a single packaged IC, or may be composed of a plurality of packaged ICs connected to the same function or different functions. For example, the processor 201 may be a communication processor (communication processor, CP for short).

The memory 202 may be used to store software programs and modules, and the processor 201 executes various functional applications of the second device and implements data processing by running the software programs and modules stored in the memory 202. In the specific embodiment of the present application, the memory 202 may include volatile memory, such as nonvolatile dynamic random access memory (nonvolatile random access memory, abbreviated as NVRAM), phase change RAM (PRAM), magnetoresistive RAM (MRAM), and the like, and may further include nonvolatile memory, such as at least one magnetic disk storage device, an EEPROM (Electrically erasable programmable read-only memory), a flash memory device, such as a nand flash memory (NOR flash memory) or a nand flash memory (NAND flash memory).

Referring to fig. 21, a schematic structural diagram of an embodiment of a first apparatus is provided herein. May be used to perform the breath detection method in corresponding embodiments of the present application.

As shown in fig. 21, the first device 102 may include: processor 211, memory 212, etc. In addition, these components may also be connected and communicate via one or more buses or the like.

The processor 211 is a control center of the first device, connects various parts of the entire user device using various interfaces and lines, by running or executing software programs and/or modules stored in the memory 212, and invoking data stored in the memory 212 to perform various functions of the terminal and/or process data. The processor 211 may be composed of an integrated circuit (Integrated Circuit, simply referred to as IC), for example, a single packaged IC, or may be composed of a plurality of packaged ICs connected to the same function or different functions. For example, the processor 211 may be a CP.

In a specific implementation, the application further provides a computer storage medium, where the computer storage medium may store a program, and the program may include some or all of the steps in each embodiment of the breath detection method provided in the application when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (random access memory, RAM), or the like.

It should also be appreciated that the memory in embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. The volatile memory may be random access memory (random access memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DRRAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.

Referring to fig. 22, a schematic structural diagram of an embodiment of a user equipment provided in the present application is provided. May be used to perform the breath detection method of embodiments of the present application and present corresponding breath detection information.

As shown in fig. 22, the user equipment 104 may include: processor 221, memory 222, and display 223. In addition, these components may also be connected and communicate via one or more buses or the like.

The processor 221 is a control center of the first device, connects various parts of the entire user device using various interfaces and lines, by running or executing software programs and/or modules stored in the memory 222, and invoking data stored in the memory 222 to perform various functions of the terminal and/or to process data. The processor 221 may be formed by an integrated circuit (Integrated Circuit, simply referred to as IC), for example, a single packaged IC, or may be formed by connecting a plurality of packaged ICs having the same function or different functions. For example, the processor 221 may be a CP.

The display 223 is used for displaying corresponding environmental change information, such as breath detection information shown in fig. 14 and 15.

In the present application, the correspondence between a and B may be understood as that a is associated with B, or that a has an association relationship with B.

It should be understood that the manner, the case, the category, and the division of the embodiments in the embodiments of the present application are for convenience of description only, and should not be construed as a particular limitation, and the features in the various manners, the categories, the cases, and the embodiments may be combined without contradiction.

It should also be understood that the "first" and "second" in the examples of the application are for distinguishing only, and should not be construed as limiting the application in any way.

It should be understood that the term "and/or" is merely an association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

It will be appreciated that the various numerical numbers referred to in the embodiments of the present application are merely for ease of description and are not intended to limit the scope of the embodiments of the present application. The above embodiments are provided for the purpose of not limiting the present application, but rather, any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the present application are intended to be included within the scope of the present application.

Claims

1. A method of breath detection, the method comprising:

the first device receives Wi-Fi signals of at least one second device;

the first device determines N signal sets according to the phase difference or the amplitude of the channel state information of Wi-Fi signals of the at least one second device, wherein the phase difference or the amplitude in the N signal sets respectively belong to N different numerical value ranges, and N is an integer greater than or equal to 2;

the first device determines environmental change information from the N signal sets, the environmental change information including breath detection information.

2. The method of claim 1, wherein the determining N signal sets from a phase difference or an amplitude of channel state information of Wi-Fi signals of the at least one second device comprises:

determining a phase difference of the channel state information of each subcarrier;

obtaining N signal sets according to the phase difference;

or, determining the amplitude of the channel state information of each subcarrier;

and obtaining N signal sets according to the amplitude.

3. The method according to claim 1 or 2, wherein said determining environmental change information from said N signal sets comprises:

Obtaining a target signal set according to the N signal sets;

obtaining a frequency domain candidate respiratory frequency and a respiratory energy ratio corresponding to the frequency domain candidate respiratory frequency according to the target signal set;

obtaining a time domain candidate respiratory frequency and a first autocorrelation coefficient corresponding to the time domain candidate respiratory frequency according to the target signal set;

and outputting environment change information according to the frequency domain candidate respiratory frequency, the respiratory energy ratio, the time domain candidate respiratory frequency and the first autocorrelation coefficient.

4. A method according to any one of claims 1 to 3, wherein said outputting environmental change information from said frequency domain candidate respiratory rate, said respiratory energy ratio, said time domain candidate respiratory rate, and said first autocorrelation coefficient comprises:

and outputting the frequency domain candidate respiratory rate or the time domain candidate respiratory rate as respiratory detection information when the respiratory energy ratio is detected to be larger than a corresponding first threshold value and the first autocorrelation coefficient is detected to be larger than a corresponding second threshold value.

5. The method of any of claims 1 to 4, wherein the outputting environmental change information from the frequency domain candidate respiratory rate, the respiratory energy ratio, the time domain candidate respiratory rate, and the first autocorrelation coefficient comprises:

And when the frequency domain candidate respiratory frequency is detected to be similar to the time domain candidate respiratory frequency, and one or more of the respiratory energy ratio and the first autocorrelation coefficient are larger than a corresponding threshold value, outputting the frequency domain candidate respiratory frequency or the time domain candidate respiratory frequency as respiratory detection information.

6. The method of claim 3, wherein the deriving a set of target signals from the N sets of signals comprises:

for each subcarrier, acquiring a remaining signal set, wherein the remaining signal set is a signal set with the phase difference or the amplitude less than a preset threshold value in the N signal sets;

and obtaining a target signal set according to the rest signal set.

7. The method of claim 6, wherein said deriving a set of target signals from said remaining set of signals comprises:

judging whether the phase difference or the amplitude in a preset time period is absent in the rest signal set;

if not, taking the rest signal set as a target signal set;

or judging whether the difference between the first missing amount and the second missing amount in the rest signal set at intervals of preset time is higher than a preset missing threshold value; the first missing amount corresponds to the highest value of the missing amount of the phase difference or the missing amount of the amplitude in the preset time interval, and the second missing amount corresponds to the lowest value of the missing amount of the phase difference or the missing amount of the amplitude in the preset time interval;

And if not, taking the rest signal set as a target signal set.

8. The method according to claim 6 or 7, wherein the deriving a frequency domain candidate breathing frequency and a breathing energy ratio corresponding to the frequency domain candidate breathing frequency from the set of target signals comprises:

performing fast Fourier transform on the target signal set to obtain a frequency domain signal corresponding to the target signal set;

and obtaining a frequency domain candidate respiratory frequency and a respiratory energy ratio corresponding to the frequency domain candidate respiratory frequency according to the respiratory frequency band and the frequency domain signal.

9. The method of claim 8, wherein deriving a frequency-domain candidate breathing frequency from the breathing frequency band and the frequency-domain signal and a ratio of breathing energies corresponding to the frequency-domain candidate breathing frequency comprises:

acquiring a sub-frequency domain signal with frequency in a respiratory frequency band in the frequency domain signal;

determining the ratio of the sum of the amplitudes of the sub-frequency domain signals to the sum of the amplitudes of the frequency domain signals as a respiratory energy ratio;

and determining the frequency corresponding to the maximum amplitude value in the sub-frequency domain signals as the frequency domain candidate respiratory frequency.

10. The method of claim 3, 6 or 7, wherein the deriving a time-domain candidate breathing frequency and a first autocorrelation coefficient corresponding to the time-domain candidate breathing frequency from the set of target signals comprises:

Performing autocorrelation calculation on the target signal set to obtain an autocorrelation coefficient corresponding to the target signal set;

and obtaining a time domain candidate respiratory frequency and a first autocorrelation coefficient corresponding to the time domain candidate respiratory frequency according to the respiratory frequency band and the autocorrelation coefficient.

11. The method of claim 10, wherein the deriving a time-domain candidate breathing frequency and a first autocorrelation coefficient corresponding to the time-domain candidate breathing frequency from the breathing frequency band and the autocorrelation coefficient comprises:

determining the frequency corresponding to the maximum autocorrelation coefficient in the respiratory frequency band as a time domain candidate respiratory frequency;

and determining the autocorrelation coefficient corresponding to the time domain candidate respiratory frequency as a first autocorrelation coefficient.

12. An electronic device comprising at least one processor, a memory, wherein the memory is configured to store instructions, the processor configured to execute the instructions to implement the method of any of claims 1-11.

13. The electronic device of claim 12, wherein the electronic device comprises a terminal device or a wireless access node.

14. The electronic device of claim 12 or 13, wherein the electronic device is configured to interface or output speech based on breath detection information.

15. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a program that causes an electronic device to execute the method according to any one of claims 1 to 11.

16. A computer program product comprising computer readable instructions which, when executed by one or more processors, implement the method of any one of claims 1 to 11.