Vital sign detection method and device based on WiFi channel, medium and electronic equipment

Abstract

The invention provides a vital sign detection method based on a WiFi channel, a vital sign detection device based on the WiFi channel, a computer readable storage medium and electronic equipment, and relates to the technical field of communication. The method comprises the following steps: acquiring channel state information of a WiFi signal propagated in a preset space; the channel state information comprises subcarrier data of a plurality of subcarriers; determining a target subcarrier from the plurality of subcarriers based on the energy values of the subcarrier data of the plurality of subcarriers in the frequency domain; filtering the subcarrier data of the target subcarrier by using a frequency range of human body activity to obtain second filtering data of the target subcarrier; and determining the vital sign information of the detected person according to the wave crest information of the second filtering data of the target subcarrier on the frequency domain. According to the method and the device, the vital sign information of people can be accurately perceived by effectively processing the channel state information.

Description

Background Art

[0002] WiFi (Wireless Fidelity, a wireless network transmission technology) sensing is a technology that uses WiFi signals to detect the presence, location and movement of objects and people in the environment. By analyzing the reflection and attenuation of WiFi signals, WiFi sensing algorithms can determine the location, presence and movement of objects and people. It has a wide range of applications in indoor navigation, smart home automation, health monitoring and security.

[0003] CSI (Channel State Information) is a parameter that characterizes channel changes and can sense WiFi data, usually collected through a computer network card. Due to problems such as weak received signals, unbalanced quality of received signals on terminal devices, low sampling rate, large environmental interference, and high power consumption of terminal devices, it is difficult for existing technologies to accurately sense and effectively detect human vital signs based on CSI on the terminal device side such as mobile phones by connecting the terminal device to a router. Summary of the invention

[0004] The present disclosure provides a vital sign detection method based on a WiFi channel, a vital sign detection device based on a WiFi channel, a computer-readable storage medium and an electronic device, thereby at least to a certain extent resolving the problem in the prior art that it is difficult to accurately perceive and detect human vital signs based on effective channel state information.

[0005] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0006] According to a first aspect of the present disclosure, a vital sign detection method based on a WiFi channel is provided, comprising: acquiring channel state information of a WiFi signal propagating in a preset space; the channel state information comprising subcarrier data of a plurality of subcarriers; determining a target subcarrier from the plurality of subcarriers based on energy values ​​of the subcarrier data of the plurality of subcarriers in the frequency domain; filtering the subcarrier data of the target subcarrier using a frequency range of human activity to obtain second filtered data of the target subcarrier; and determining vital sign information of the detected person according to peak information of the second filtered data of the target subcarrier in the frequency domain.

[0007] According to a second aspect of the present disclosure, a vital sign detection device based on a WiFi channel is provided, including: a state information acquisition module, used to acquire channel state information of a WiFi signal propagating in a preset space; the channel state information includes subcarrier data of multiple subcarriers; a target subcarrier determination module, used to determine a target subcarrier from the multiple subcarriers based on energy values ​​of the subcarrier data of the multiple subcarriers in the frequency domain; a carrier data filtering module, used to filter the subcarrier data of the target subcarrier using a frequency range of human activity to obtain second filtered data of the target subcarrier; and a vital sign information determination module, used to determine the vital sign information of the detected person according to peak information of the second filtered data of the target subcarrier in the frequency domain.

[0008] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method of the first aspect and possible implementation methods thereof are implemented.

[0009] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the method of the first aspect and possible implementations thereof by executing the executable instructions.

[0010] The technical solution disclosed in this disclosure has the following beneficial effects:

[0011] Acquire channel state information of a WiFi signal propagating in a preset space; the channel state information includes subcarrier data of multiple subcarriers; determine a target subcarrier from the multiple subcarriers based on energy values ​​of the subcarrier data of the multiple subcarriers in the frequency domain; filter the subcarrier data of the target subcarrier using the frequency range of human activity to obtain second filtered data of the target subcarrier; determine vital sign information of a detected person based on peak information of the second filtered data of the target subcarrier in the frequency domain. On the one hand, this exemplary embodiment proposes a new vital sign detection result, which can rely on the processing and analysis of the acquired channel state information to determine the vital sign information of the detected person. The detection process does not require the user to wear a contact detection device and does not require the user to perform additional complex operations, that is, it can achieve non-sensing detection of the user's vital sign information, with good user experience and a wide range of applications; on the other hand, this exemplary embodiment can select the subcarrier data of a suitable target subcarrier for analysis and processing of vital sign information perception based on the energy value of the subcarrier data of the channel state information on the frequency, and improve the accuracy and effectiveness of the determination of vital sign information by effectively processing the data; on another hand, this exemplary embodiment uses the frequency range of human activities to filter the subcarrier data of the target subcarrier, and combined with the characteristics of human activities, it can accurately determine the effective processing data about human vital signs, while saving computing resources, further ensuring the accuracy and efficiency of the determination of vital sign information.

[0012] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0014] Figure 1 A schematic diagram showing a system architecture in this exemplary embodiment;

[0015] Figure 2 A flow chart showing a method for detecting vital signs of a WiFi channel in this exemplary embodiment is shown;

[0016] Figure 3 A sub-flow chart showing a method for detecting vital signs of a WiFi channel in this exemplary embodiment;

[0017] Figure 4A schematic diagram of carrier data in which a human activity is a breathing activity in this exemplary embodiment is shown;

[0018] Figure 5 A schematic diagram showing the comparison between the data of the exemplary embodiment and the measurement data of a conventional wearable device is shown;

[0019] Figure 6 A schematic diagram of an antenna layout in this exemplary embodiment is shown;

[0020] Figure 7 A schematic diagram showing another antenna layout in this exemplary embodiment;

[0021] Figure 8 (a)- Figure 8 (b) shows a time domain schematic diagram of each subcarrier in the channel state data received by different links;

[0022] Fig. 9 (a)- Fig. 9 (b) shows a time domain schematic diagram of link 1 before and after data filtering;

[0023] Fig.10 shows a frequency domain schematic diagram of link one data;

[0024] Fig.11 (a)- Fig.11 (b) shows a time domain schematic diagram of link 2 data before and after filtering;

[0025] Fig.12 shows a frequency domain schematic diagram of link 2 data;

[0026] Fig.13 (a)- Fig.13 (c) shows a time domain schematic diagram of the first link carrier data;

[0027] Fig.14 A frequency domain schematic diagram of first link carrier data is shown;

[0028] Fig.15 (a)- Fig.15 (c) shows a time domain schematic diagram of the second link carrier data;

[0029] Fig.16 A frequency domain schematic diagram of the second link carrier data is shown;

[0030] Fig.17 A flow chart of a sleep quality assessment method in this exemplary embodiment is shown;

[0031] Fig.18 A module diagram of another method for detecting vital signs based on a WiFi channel according to this exemplary embodiment is shown;

[0032] Fig.19 A flow chart for obtaining channel state information in this exemplary embodiment is shown;

[0033] Fig. 20 A structural block diagram of a vital sign detection device based on a WiFi channel in this exemplary embodiment is shown;

[0034] Fig.21 A structural diagram of an electronic device in this exemplary embodiment is shown. DETAILED DESCRIPTION

[0035] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0036] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0037] refer to Figure 1 As shown, the system architecture 100 may include a gateway device 110, a person 120 and a terminal device 130. The gateway device 110 may be a hardware device such as a router that can connect to a network or transmit data information. The terminal device 130 may be an electronic device such as a mobile phone, a tablet computer, a smart wearable device, etc. The gateway device 110 and the terminal device 130 may be connected via a wired or wireless communication link to exchange data. The system architecture 100 covers WiFi signals. The terminal device 130 may be connected to the gateway device 110 to detect the vital signs of the person 120 by acquiring the channel state information of the WiFi channel.

[0038] Combine the following Figure 2 The process of the vital signs detection method based on WiFi channel is described. Figure 2 As shown, the vital sign detection method based on the WiFi channel may include the following steps S210 to S240:

[0039] Step S210, obtaining channel state information of a WiFi signal propagating in a preset space; the channel state information includes subcarrier data of a plurality of subcarriers.

[0040] In wireless communication, channel state information CSI is a kind of data for sensing WiFi, which can be used to describe how the signal propagates from the transmitter to the receiver through the channel. It represents a combination of a series of effects, such as scattering, fading, and energy attenuation with distance. In this exemplary embodiment, the WiFi signal can be propagated in a preset space, which can be any environment or scene, such as a home or a shopping mall. The WiFi signal propagates in the preset space, and it can be considered that it is distributed in the preset space. Therefore, when people are active in the scene, it will affect the propagation of the signal. The WiFi signal will scatter on the human body and other physical phenomena, which will be recorded by the channel state information. Among them, the transmitter and the receiver are diverse according to different application scenarios. For example, in a smart home, the transmitter can be a router at home, and the receiver can be a mobile phone, a tablet computer, or various household appliances with WiFi modules, such as a sweeping robot, a smart TV, etc. This exemplary embodiment can detect a person's vital signs by obtaining the channel state information of the WiFi channel.

[0041] In the field of wireless communication technology, a carrier wave, as the physical basis for transmitting information, can be used to transmit information. Carrier data refers to data transmitted through a carrier wave, which may refer to continuous signal data of a predefined preset frequency. In this exemplary embodiment, the channel state information may include subcarrier data of multiple subcarriers.

[0042] Step S220: determining a target subcarrier from the multiple subcarriers based on energy values ​​of the subcarrier data of the multiple subcarriers in the frequency domain.

[0043] After acquiring the channel state information of the subcarrier data including multiple subcarriers, the exemplary embodiment may process the channel state information to determine the target subcarrier from the multiple subcarriers, so as to perform subsequent steps using the subcarrier data of the target subcarrier. Specifically, the target subcarrier may be screened according to the energy values ​​of the subcarrier data of the multiple subcarriers in the frequency domain, for example, the energy values ​​of the subcarrier data of the multiple subcarriers are sorted, and one or more subcarriers with the largest energy values ​​are determined as the target subcarriers according to the sorting results, or a preset number of subcarriers are determined as the target subcarriers according to the energy values, for example, the first 2 or first 3 subcarriers whose energy values ​​are sorted from large to small are selected as the target subcarriers, etc. Among them, the energy of the subcarrier data in the frequency domain can be characterized by transforming the subcarrier data into the power spectrum density in the frequency domain.

[0044] Step S230: filtering the subcarrier data of the target subcarrier using the frequency range of human activity to obtain second filtered data of the target subcarrier.

[0045] Human activity refers to an activity that reflects changes in human body signs, which can generate a series of data, such as breathing data, heartbeat data, etc. Among them, breathing data refers to data determined according to human breathing activity. Breathing is the activity of the respiratory tract and lungs of the human body. Usually, the normal breathing frequency of the human body is 0.2Hz-0.5Hz (hertz). Breathing data can include breathing frequency, breathing frequency, etc. Heartbeat data refers to data determined according to human heartbeat activity, for example, it can be heart rate, which refers to the number of heartbeats per minute of a normal person in a quiet state, also called a quiet heart rate, generally 60-100 times / minute, or 0.8Hz-2.0Hz, which may produce individual differences due to age, gender or other physiological factors. This exemplary embodiment can obtain human breathing data and heartbeat data by analyzing chest ups and downs data. Chest ups and downs data can be the amplitude of chest ups and downs. Usually, in a healthy adult in a quiet state, the amplitude of chest ups and downs during breathing is 0cm-3cm (centimeter), and the amplitude of chest ups and downs caused by heartbeats is 1.5mm-3.5mm (millimeter).

[0046] This exemplary embodiment can determine the frequency range of human activities based on normal human activity data. Different types of activities can determine different frequency ranges of human activities. For example, for respiratory activities, 0.15-0.8 Hz can be determined as the frequency range of human activities, and the target subcarrier data can be filtered; for heartbeat activities, 0.9 Hz-2 Hz can be determined as the frequency range of human activities, and the target subcarrier data can be filtered to determine the second filtered data after filtering. The second filtered data may include indicator energy for characterizing the physical state of a person, such as steady-state energy of breathing, etc. The frequency range of human activities can be adjusted or set according to the range of different activity types, and the present disclosure does not specifically limit this.

[0047] Step S240: determining the vital sign information of the detected person according to the peak information of the second filtered data of the target subcarrier in the frequency domain.

[0048] After determining the second filtered data of the target subcarrier, this exemplary embodiment can transform the data into the frequency domain to obtain waveform information about the second filtered data in order to facilitate intuitive analysis of the data. Furthermore, the vital sign information of the detected person can be determined based on the peak information of the second filtered data of the target subcarrier in the frequency domain.

[0049] Among them, the vital signs information can be the judgment result of whether it is normal or not, for example, it can be the result of whether the vital signs such as breathing and heart rate are normal, or it can be a detailed analysis result, such as a breathing / heart rate report, or generate corresponding functional results in combination with the scene, such as the sleep assessment results generated based on the user's vital signs information (breathing / heart rate information) during the sleep period.

[0050] In addition, this exemplary embodiment can also send prompt information to the terminal device based on the vital signs information to inform the user of the vital signs monitoring results. For example, when abnormal breathing or heart rate occurs, a notification reminder is sent to confirm with the user, or information or notification is sent to the user's emergency contact, etc.

[0051] In summary, in this exemplary embodiment, channel state information of a WiFi signal propagating in a preset space is obtained; a detected person exists in the preset space; the channel state information includes subcarrier data of multiple subcarriers; a target subcarrier is determined from the multiple subcarriers based on energy values ​​of the subcarrier data of the multiple subcarriers in the frequency domain; the subcarrier data of the target subcarrier is filtered using the frequency range of human activity to obtain second filtered data of the target subcarrier; and the vital sign information of the detected person is determined based on the peak information of the second filtered data of the target subcarrier in the frequency domain. On the one hand, this exemplary embodiment proposes a new vital sign detection result, which can rely on the processing and analysis of the acquired channel state information to determine the vital sign information of the detected person. The detection process does not require the user to wear a contact detection device and does not require the user to perform additional complex operations, that is, it can achieve non-sensing detection of the user's vital sign information, with good user experience and a wide range of applications; on the other hand, this exemplary embodiment can select the subcarrier data of a suitable target subcarrier for analysis and processing of vital sign information perception based on the energy value of the subcarrier data of the channel state information on the frequency, and improve the accuracy and effectiveness of the determination of vital sign information by effectively processing the data; on another hand, this exemplary embodiment uses the frequency range of human activities to filter the subcarrier data of the target subcarrier, and combined with the characteristics of human activities, it can accurately determine the effective processing data about human vital signs, while saving computing resources, further ensuring the accuracy and efficiency of the determination of vital sign information.

[0052] In an exemplary embodiment, the above step S220 may include:

[0053] Subcarrier data of multiple subcarriers are transformed into the frequency domain, and energy values ​​of the multiple subcarriers in the frequency domain are sorted from high to low, and a first preset number of subcarriers with front energy values ​​are selected as target subcarriers.

[0054] In this exemplary embodiment, the subcarrier data of multiple subcarriers are transformed into the frequency domain, which can be a fast Fourier transform of N points on the subcarrier data, wherein the value of N can be customized according to actual needs, for example, N can be taken as 256, 512, 1024, etc. Then, the energy values ​​of the multiple subcarriers after the frequency domain transformation can be sorted from high to low in frequency, and the first preset number of subcarriers with the front energy values ​​are selected as the target subcarriers, wherein the first preset number can be customized, for example, the first preset number can be set to 1, 2 or 3, etc. In this exemplary embodiment, in order to ensure the accuracy and convenience of data processing, the first preset number can be set to 1, that is, the subcarrier containing the most human breathing signals can be selected as the target subcarrier, and the subcarrier data of the target subcarrier is used for subsequent processing.

[0055] Furthermore, in an exemplary embodiment, before transforming the subcarrier data of the multiple subcarriers into the frequency domain, the above-mentioned vital sign detection method based on the WiFi channel may also include:

[0056] Filtering subcarrier data of the plurality of subcarriers using a basic frequency range to obtain first filtered data of the plurality of subcarriers;

[0057] The above-mentioned transforming subcarrier data of multiple subcarriers into the frequency domain includes:

[0058] The first filtered data of the plurality of subcarriers is transformed into the frequency domain.

[0059] Among them, the basic frequency range may refer to a reference frequency range that can be determined or identified according to a signal, including human activities in the scene, which can be roughly determined according to the normal breathing frequency or normal heartbeat frequency of the human body, and can cover a more suitable range of normal human activities, and can be customized according to actual needs. The basic frequency range can be a range that can determine the range of breathing or heartbeat, such as 0.15-2Hz; or it can be multiple ranges corresponding to different human activities, such as the basic frequency range of breathing activity is 0.15-1Hz, and the basic frequency range of heartbeat activity is 0.8-2Hz. When the terminal device has multiple links, for example, when multiple antennas are used to collect channel state information, different links can determine different basic frequency ranges according to different types of human activities to filter the subcarrier data of multiple subcarriers, for example, each link can determine the basic frequency range for breathing activity, or the basic frequency range for heartbeat activity, and filter through their respective basic frequency ranges. In addition, different links can also determine the same basic frequency range according to the same type of human activity, for example, multiple links can set the same basic frequency range for breathing activity, or the basic frequency range for heartbeat activity, to filter the data of human activity. In addition, a basic frequency range of a first type of human activity may be set for a part of the links, and a basic frequency range of a second type of human activity may be set for another part of the links, so as to filter data of different types of human activities through different links, for example, a basic frequency range of heartbeat activity may be set for the first link, and a basic frequency range of respiratory activity may be set for the second link and the third link, so as to filter data of heartbeat activity through the first link, and filter data of respiratory activity through the second link and the third link, etc. The basic frequency range may be set to be larger than the frequency range of human activity, for example, under respiratory activity, the frequency range of human activity may be set to 0.15 Hz to 0.8 Hz, and the basic frequency range may be set to 0.15 Hz to 1 Hz.

[0060] In an exemplary embodiment, the above-mentioned transforming the subcarrier data of multiple subcarriers into the frequency domain, sorting the energy values ​​of the multiple subcarriers in the frequency domain from high to low, and selecting a first preset number of subcarriers with front energy values ​​as target subcarriers may include:

[0061] Transforming subcarrier data of a plurality of subcarriers into a frequency domain to obtain a first power spectrum density of each subcarrier in the frequency domain;

[0062] Normalizing the first power spectral density to obtain a normalized first power spectral density;

[0063] The largest normalized first power spectrum densities under each subcarrier are sorted in descending order, and a first preset number of subcarriers with the largest normalized first power spectrum densities are selected as target subcarriers.

[0064] The power spectrum density refers to the energy distribution of a signal per unit frequency in the frequency domain. This exemplary embodiment can first determine the power spectrum of the signal based on the result of Fourier transform, and then determine the power spectrum density based on the ratio of the power spectrum to the frequency width.

[0065] In this exemplary embodiment, subcarrier data of multiple subcarriers may be transformed into the frequency domain, and a first power spectrum density of each subcarrier in the frequency domain may be determined by calculation. Then, each first power spectrum density may be normalized to obtain a normalized first power spectrum density, which is expressed as: Among them, 0.15-1Hz can be the basic frequency range for filtering the subcarriers; further, the largest normalized first power spectrum density under each subcarrier can be sorted in descending order, and the first preset number of subcarriers with the front normalized first power spectrum density can be selected as the target subcarriers for subsequent evaluation of human activities such as breathing or heartbeat.

[0066] When selecting a target subcarrier, this exemplary embodiment may first filter subcarrier data of a plurality of subcarriers using a basic frequency range to obtain first filtered data, then perform a fast Fourier transform on the first filtered data to convert it to a frequency, determine a first power spectrum density, and select a first preset number of subcarriers ranked top as target subcarriers based on a result of sorting the normalized first power spectrum density from large to small, and further filter the subcarrier data of the target subcarrier through a frequency range of human activities with a smaller range to obtain second filtered data of the target subcarrier, and determine vital sign information of the detected person based on the second filtered data.

[0067] In this exemplary embodiment, after determining the target subcarriers of different links, the target subcarriers of different links can be serially identified, and the target subcarriers of different links can be bandpass filtered according to the frequency range of human activities. For example, the target subcarrier data of different links can be bandpass filtered in the frequency range of human activities of respiratory activities, and the bandpass filter can be set to 0.15-0.8Hz, and the filtered data is fast Fourier transformed to obtain the second filtered data. Similarly, the target subcarrier data of different links are bandpass filtered in the frequency range of human activities of heartbeat activities, and the bandpass filter can be set to 0.9-2Hz, and the filtered data is fast Fourier transformed to obtain the second filtered data.

[0068] In an exemplary embodiment, if Figure 3 As shown, the above step S240 may include the following steps:

[0069] Step S310, determining the human activity frequency and / or human activity energy value of the detected person according to the peak information of the second filtered data of the target subcarrier in the frequency domain;

[0070] Step S320, determining the vital sign information of the detected person according to the human activity frequency and / or the human activity energy value.

[0071] After the second filtered data is converted to frequency, the peak information of the second filtered data in the frequency domain can be determined, and the human activity frequency, human activity energy value, or human activity frequency and human activity energy value of the detected person can be determined based on the peak information. The human activity frequency is different depending on the type of human activity, for example, the respiratory frequency can be determined for respiratory activity, and the heartbeat frequency can be determined for heartbeat activity. The human activity energy value refers to an indicator value that characterizes human activity in an energy manner, such as respiratory energy.

[0072] Based on the human activity frequency, human activity energy value, or human activity frequency and human activity energy value, the vital signs information of the person being detected can be determined. For example, the human activity frequency and / or human activity energy value within a preset time period are counted, and the vital signs information of the person being detected is determined based on the statistical results. Specifically, when the human activity energy value is greater than a preset energy threshold, the vital signs information of the person being detected in motion can be determined, and so on.

[0073] In an exemplary embodiment, the above step S310 may include:

[0074] Transforming the second filtered data of the target subcarrier into the frequency domain, and determining a main peak of the second filtered data in the frequency domain;

[0075] The human activity frequency is determined according to the frequency of the main wave peak, and / or the human activity energy value is determined according to the energy value of the main wave peak.

[0076] To facilitate data processing, this exemplary embodiment can transform the second filtered data of the target subcarrier into the frequency domain, and determine the main peak in the frequency domain by peak search, wherein the main peak can be one or more, for example, the main peak can be the peak with the largest energy value, or 10 peaks can be determined by sorting according to the energy values, and 5 peaks with obvious peak characteristics can be selected from the 10 peaks as the main peaks as needed.

[0077] This exemplary embodiment can determine the human activity frequency based on the frequency of the main wave peak, wherein the main wave peaks corresponding to different types of human activities can determine different human activity frequencies, for example, the respiratory frequency is determined according to the main wave peak of the respiratory activity, and the heart rate is determined according to the main wave peak of the heart rate activity. There are many ways to determine the human activity frequency according to the frequency of the main wave peak, for example, the human activity frequency can be determined by weighted calculation according to each frequency of the main wave peak; or the frequency corresponding to one of the main wave peaks among the main wave peaks can be used as the human activity frequency; or the energy value proportion of the largest main wave peak in the wave peak is calculated to determine the human activity frequency, etc.

[0078] The human activity energy can be determined based on the human activity energy over a period of time, for example, by frequency statistics of the main wave peak in a preset time period to determine the human activity energy value, and the preset time period can be set according to actual needs, such as the first N seconds or the first 2 minutes of the current moment. The human activity energy can be updated in real time as time passes.

[0079] In an exemplary embodiment, the channel state information of the WiFi signal includes: channel state information of the WiFi signal received by multiple links of the terminal device; the second filtered data includes second filtered data corresponding to the multiple links;

[0080] In this exemplary embodiment, the terminal device can receive channel state information of WiFi signals through multiple links, and the second filtering data may include second filtering data corresponding to multiple links, wherein the link may be an antenna configured by the terminal device, and the channel state information of WiFi signals is received through different antennas, that is, the channel state information of WiFi signals is received through multiple links of the terminal device.

[0081] Transforming the second filtered data of the target subcarrier into the frequency domain and determining the main peak of the second filtered data in the frequency domain may include the following steps:

[0082] Transforming the second filtered data corresponding to each link into the frequency domain, and determining a second power spectrum density of the target subcarrier in the frequency domain;

[0083] Normalizing the second power spectral density to obtain a normalized second power spectral density;

[0084] For each link, a peak search is performed on the normalized second power spectrum density in the frequency domain to determine a second preset number of peaks before the normalized second power spectrum density is sorted from high to low, and the peak whose normalized second power spectrum density exceeds a preset energy threshold is determined as the main peak.

[0085] In this exemplary embodiment, after the second filtered data corresponding to each link is transformed into the frequency domain, the second power spectrum density corresponding to the second filtered data can be determined according to the energy distribution in the frequency domain. In order to facilitate subsequent processing, it can be normalized to obtain a normalized second power spectrum density. Then, for each link, a peak search can be performed on the normalized second power spectrum density in the frequency domain to determine the first second preset number of peaks when the normalized second power spectrum density is sorted from high to low. For example, the target subcarrier data of different links are bandpass filtered in the human activity frequency range of respiratory activity and transformed to the frequency domain, and the normalized second power spectrum density in the frequency domain is determined. The physical meaning is the signal-to-noise ratio of the respiratory component. The normalized power spectrum density is searched for local peaks. The local peak search is performed to obtain the first M largest frequency components. M can be 2 to 6. The present disclosure does not make specific restrictions on this. When M is 2 to 6, it can be used for the detection of human respiratory activities in multi-person scenarios. Then it is determined whether the normalized second power spectrum density exceeds the preset energy threshold SNP. resp , when the normalized second power spectrum density exceeds the preset energy threshold SNP resp , it can be considered as a usable signal, and the corresponding peak is taken as the main peak.

[0086] The same is true for heartbeat activity. The human activity frequency range of heartbeat activity is used to filter the heartbeat frequency of the target subcarriers of different links and transform them to the frequency domain. The normalized second power spectrum density in the frequency domain is determined. The physical meaning is the signal-to-noise ratio of the heartbeat component. The normalized power spectrum density is searched for local peaks to obtain the top M frequency components. M can be 2 to 6. The present disclosure does not make specific restrictions on this. When M is 2 to 6, it can be used to detect human heartbeat activity in multi-person scenarios. Then determine whether the normalized second power spectrum density exceeds the preset energy threshold SNP. heart , when the normalized second power spectrum density exceeds the preset energy threshold SNP heart When , it can be considered as an available signal, and then the corresponding peak is taken as the main peak. Among them, the preset energy threshold SNP resp 、SNP heart , can be customized according to actual needs, and this disclosure does not make any specific limitations on this.

[0087] Further, in an exemplary embodiment, determining the human activity frequency according to the frequency of the main peak includes:

[0088] The normalized second power spectrum densities corresponding to the main peaks of each link are compared, and the frequency corresponding to the maximum normalized second power spectrum density is taken as the human activity frequency.

[0089] For example, after determining the main peak corresponding to the human respiratory activity, the normalized second power spectrum densities corresponding to the main peaks of different links can be compared, and the frequency corresponding to the largest normalized power spectrum density can be selected as the final respiratory frequency; after determining the main peak corresponding to the human heartbeat activity, the normalized second power spectrum densities corresponding to the main peaks of different links can be compared, and the frequency corresponding to the largest normalized power spectrum density can be selected as the final heartbeat frequency, and so on.

[0090] Figure 4 A schematic diagram of carrier data of a human body activity as a respiratory activity is shown, wherein curve a represents the respiratory frequency data of the respiratory activity in a sleeping scene, and curve b is the maximum normalized second power spectrum density. This exemplary embodiment can set a preset energy threshold SNP resp is 1, then according to the curve shown in the figure, the respiratory rate is a credible and available signal only when the maximum normalized second power spectrum density exceeds 1, and its corresponding peak can be used as the main peak to determine the respiratory frequency, etc.

[0091] Figure 5 The comparison between the respiratory data after the normalized power spectrum density factor correction and the measurement data of the traditional wearable device is shown. The traditional wearable device can obtain the corresponding sensor data by configuring a gyroscope or an accelerometer. It can be seen that the data determined by this exemplary embodiment is consistent with the traditional measurement data, indicating that the human activity data determined by this exemplary embodiment is more accurate.

[0092] In an exemplary embodiment, the terminal device includes a plurality of WiFi antennas, each WiFi antenna forming a link; the terminal device has four sides except a display plane and a plane opposite to the display plane, wherein a first side and a third side are long sides, a second side and a fourth side are short sides, and the second side and the fourth side are divided into a top side and a bottom side;

[0093] If the terminal device includes two WiFi antennas, the two WiFi antennas are respectively distributed on any long side and the second side, and the phase spacing between the two WiFi antennas is greater than the preset distance;

[0094] If the terminal device includes three WiFi antennas, among the three WiFi antennas, the first WiFi antenna is set on the first side, the second WiFi antenna is set on the top side, and the third WiFi antenna is set at a position between the middle of the third side and close to the fourth side.

[0095] Considering the integrated layout architecture of mobile phone WiFi antenna communication perception, and considering the influence of user's habits of using terminal devices, such as when playing games or watching videos, the terminal is usually held in landscape mode, and different antenna layouts can be configured for the terminal device. The antenna can be a WiFi 5G (5th Generation Mobile Communication Technology) antenna or other.

[0096] In layout one, the terminal device may include two WiFi antennas, that is, a dual-antenna architecture is configured, and the two WiFi antennas are distributed on any long side, such as the first side or the third side, and the top side, such as the second side, and the phase spacing of the two antennas is greater than the preset distance. The specific distribution position can be set according to actual needs or the hardware configuration conditions of the terminal on the side. For example, the antenna can be set in the middle, lower middle or upper middle of the long side, and the other antenna can also be set in the middle of a fixed side, left middle or right middle, etc. The preset distance can refer to a phase distance that enables the two antennas to not affect each other, which can be determined based on a preset wavelength spacing, such as Figure 6 The terminal device 600 shown has the first antenna 610 arranged in the middle of the left long side, and the second antenna 620 arranged in the middle of the upper top side, wherein the spacing between the phase centers of the two antennas is ≥ 1 / 2 wavelength, and the preset distance can be 30 mm, that is, the phase spacing between the two WiFi antennas is greater than 30 mm.

[0097] Layout 2: The terminal device may include three WiFi antennas, and the three WiFi antennas are distributed on the first side, the second side, and the third side of the terminal device, such as the middle of the first side, the middle of the second side, the middle of the third side, and the position between the edge close to the fourth side. Figure 7 The terminal device 700 shown can set the first antenna 710 in the middle of the left long side, the second antenna 720 in the middle of the upper top side, and the third antenna 730 between the middle of the right long side and the edge close to the lower bottom side.

[0098] Through the above-mentioned antenna layout method, on the one hand, it can ensure that when the WiFi antenna is configured on the terminal, the directional patterns are complementary, thereby reducing the impact of the user's handheld terminal device; on the other hand, since the two antennas can be arranged on both sides of the mobile phone, the antenna positions are geometrically orthogonal, and the antenna spacing is greater than 1 / 2 wavelength. That is, through this exemplary embodiment, the problem of perception blind spots of a single antenna can be compensated.

[0099] Figure 8 The time domain diagram of each subcarrier in the channel state data received by different links is shown. Figure 8 (a) shows a time domain schematic diagram of each subcarrier in link 1, Figure 8 (b) shows a time domain schematic diagram of each subcarrier in link 2. It can be seen that due to the differences in the layout of antennas in terminal devices, the WiFi signals received by different antennas are also quite different.

[0100] By processing the subcarriers of the link 1 in step S220, the following can be obtained: Fig. 9 (a) is a schematic diagram of the subcarrier data of the link one target subcarrier in the time domain. After filtering, the following can be obtained: Fig. 9 (b) is a schematic diagram of the second filtered data, and then converted to the frequency domain, the following can be obtained: Fig.10 The frequency domain diagram of the breathing frequency of link 1 is shown in FIG. By processing the subcarriers of link 2 in step S220, the following can be obtained: Fig.11 (a) is a schematic diagram of the subcarrier data of the target subcarrier of link 2 in the time domain. After filtering, the following can be obtained: Fig.11 (b) is a schematic diagram of the second filtered data, and then converted to the frequency domain, the following can be obtained: Fig.12 The frequency domain diagram of the breathing frequency of link 2 is shown.

[0101] Based on the above Figures 9 to 12 ,It can be seen that the data of link 2 has better periodicity, and the main component of breathing accounts for a higher proportion, corresponding to a breathing rate of 20bpm. However, due to the low signal-to-noise ratio of the signal received by link 1, the main component of breathing accounts for an insignificant proportion, which reduces the accuracy of breathing.

[0102] When the terminal device receives high-quality signals through different antennas, no matter which link it is, it can better separate the main components of breathing and obtain an accurate breathing rate. Therefore, accurate and effective layout of the antennas of the terminal device can further improve the accuracy and effectiveness of vital sign information detection.

[0103] like Fig.13 As shown, Fig.13 (a) shows the original channel state information of the first link, that is, a schematic diagram of subcarrier data including multiple subcarriers; Fig.13 (b) shows a time domain schematic diagram of the first link target subcarrier; Fig.13 (c) shows a time domain schematic diagram of the second filtered data after filtering in the first link; Fig.14 FIG. 4 is a schematic diagram showing a frequency domain of respiratory data obtained by frequency domain transforming the second filtered data of the first link. Fig.15 As shown, Fig.15 (a) shows the original channel state information of the second link, that is, a schematic diagram of subcarrier data including multiple subcarriers;

[0104] Fig.15 (b) shows a time domain schematic diagram of the second link target subcarrier; Fig.15 (c) shows a time domain schematic diagram of the second filtered data after filtering in the second link; Fig.16 A frequency domain schematic diagram of the respiratory data obtained by frequency domain transforming the second filtered data is shown. It can be seen that both the first link and the second link can better separate the main components of the respiratory rate and obtain a more accurate respiratory rate.

[0105] In an exemplary embodiment, the vital sign information may include activity status information;

[0106] The above step S320 may include:

[0107] Obtaining a human activity energy value within a first preset time when the most recent human activity frequency is stable, and determining a human activity energy reference value according to the human activity energy value within the first preset time;

[0108] The activity status information of the detected person is determined according to the ratio of the real-time human activity energy value to the human activity energy reference value.

[0109] The human activity energy can be energy determined based on human activities, such as the energy of breathing or heartbeat, and the physical state of the detected person can be judged by the human activity energy. This exemplary embodiment can determine the reference value of the human activity energy by obtaining the human activity energy value within the first preset time when the human activity frequency is stable, wherein the stability of the human activity frequency can be judged by whether the person's breathing is stable, and the breathing stability can be analyzed by analyzing the fluctuation of the breathing frequency, for example, when the variance of the breathing frequency is less than the preset threshold, it is considered that the breathing is stable. The first preset time can be a preset time period set according to actual needs, for example, the first N seconds of the most recent human activity can be used as the first preset time, and the human activity energy value is updated according to the passage of the first preset time. The human activity energy value within the first preset time can be directly used as the reference value of the human activity energy, or it can be calculated and processed to determine the reference value of the human activity energy.

[0110] Furthermore, the activity status information of the detected person can be determined based on the ratio of the real-time human activity energy value to the human activity energy reference value, for example, the ratio of the real-time respiratory energy to the energy reference value of the respiratory steady state is determined. When the ratio is greater than a first preset threshold, the user is considered to be in motion; when the ratio is less than a second preset threshold, the user is considered to have respiratory arrest, etc., wherein the first preset threshold and the second preset threshold can be set according to actual conditions, or determined based on machine learning by collecting a large amount of data, or obtained based on empirical analysis, etc.

[0111] In an exemplary embodiment, the vital sign information includes sleep quality information;

[0112] The above step S320 may include:

[0113] Obtaining human activity energy values ​​of the detected person at multiple sampling time points during sleep;

[0114] The sleep quality information of the detected person is determined according to the average value of the human activity energy values ​​at multiple sampling time points and the human activity energy value at each sampling time point.

[0115] Among them, the sleep period can be considered as the monitoring time period when the detected person is in a sleeping state, which can be a user-defined setting or a system judgment, such as the time period between 10 pm and 7 am the next day. During the sleep period, multiple sampling time points T can be set, and the human activity energy values ​​at multiple sampling time points can be obtained. In order to ensure the effectiveness of data processing, after obtaining the monitoring data, invalid data can be eliminated, such as no value, 0 value caused by packet loss, etc.; or large abnormal values ​​caused by data storage, etc. Then calculate the average energy of the human activity energy values ​​at multiple sampling time points, and determine the sleep quality information of the detected person according to the average value of the human activity energy values ​​at multiple sampling time points and the human activity energy value at each sampling time point, such as calculating the ratio of the human activity energy value at each sampling time point to the average value, recording the number exceeding the ratio threshold, and recording it as Y. According to machine learning or empirical analysis, determine the sleep quality evaluation function f(Y, T) during the sleep period, and output the sleep quality evaluation value, so as to analyze the user's sleep state and perform health evaluation.

[0116] In an exemplary embodiment, after the above step S210, the above vital sign detection method based on WiFi channel may further include:

[0117] Preprocessing subcarrier data of a plurality of subcarriers;

[0118] The above step S220 may include:

[0119] Based on the energy values ​​of the preprocessed subcarrier data of the multiple subcarriers in the frequency domain, a target subcarrier is determined from the multiple subcarriers.

[0120] In order to prevent problems such as signal interference and signal loss from affecting the acquired channel status data, and thus affecting the accuracy of determining vital signs information, this exemplary embodiment can preprocess multiple subcarrier data and determine the target subcarrier from multiple subcarriers based on the energy values ​​of the subcarrier data of the preprocessed multiple subcarriers in the frequency domain.

[0121] Fig.17 A schematic diagram of a process framework for sleep quality assessment in this exemplary embodiment is shown, which may specifically include: a channel state data reporting module 1710, used to obtain channel state data; a channel state data processing module 1720, used to pre-process the channel state data; a human activity data extraction module 1730, used to extract human activity data, such as human activity frequency or human activity energy value, etc.; a vital sign information determination module 1740, used to determine vital sign information based on human activity data; and a sleep quality information determination module 1750, used to assess the sleep quality of the detected person.

[0122] Specifically, in an exemplary embodiment, the preprocessing of the subcarrier data of the multiple subcarriers may include at least one of the following:

[0123] Interpolating the subcarrier data to reconstruct the data packet lost in the subcarrier data, and obtaining the subcarrier data including the lost data packet;

[0124] Removing zero-frequency components from subcarrier data;

[0125] Processing abnormal points in subcarrier data;

[0126] De-noise the subcarrier data.

[0127] Among them, interpolation can be performed in a variety of ways, such as natural neighbor interpolation, nearest neighbor interpolation, or local polynomial interpolation, etc., which is not limited in the present disclosure. Denoising can also be achieved by setting a filter, such as using a Savitzky-Golay filter to perform smoothing and denoising based on a local polynomial least squares fitting filtering method in the time domain.

[0128] In this exemplary embodiment, one or more of the above-mentioned preprocessing methods can be selected for data processing. When multiple methods are used for preprocessing, the processing order can be set as needed. For example, linear interpolation, removal of zero-frequency components, removal of outliers, and smoothing and denoising can be performed in sequence.

[0129] In an exemplary embodiment, the interpolation of the subcarrier data to reconstruct the lost data packet in the subcarrier data may include:

[0130] The missing timestamp is determined according to the timestamp of the received data packet in the subcarrier data, and the lost data packet corresponding to the missing timestamp is reconstructed by interpolation based on the received data packets adjacent to the missing timestamp.

[0131] The collection of channel state data faces a major problem, namely packet loss in the actual environment. For example, in office buildings, shopping malls and residential areas, channel interference may occur from various signal sources. Many deployed wireless access points interfere with each other greatly, and the low sampling rate of mobile phone perception leads to serious packet loss. In order to solve the problem caused by packet loss, the original two-dimensional complex matrix R(m, n1) of the channel state data, where m corresponds to the subcarrier number, n1 is the number of received data packets, and corresponds to the time dimension, is reconstructed by linear interpolation to obtain a new two-dimensional complex matrix R(m, n2), where n2 is the number of reconstructed data packets.

[0132] In an exemplary embodiment, the subcarrier data is a two-dimensional array; removing zero-frequency components from the subcarrier data includes:

[0133] The amplitude average value is calculated for each row of the subcarrier data, and the amplitude average value is subtracted from the amplitude of each row to obtain the subcarrier data with the zero-frequency component removed.

[0134] This exemplary embodiment can take the average amplitude value of each subcarrier of the data packet R(m, n2) after linear interpolation, that is, each row of the two-dimensional array, and subtract the average value from each subcarrier to obtain a two-dimensional amplitude matrix RA(m, n2) after removing the zero-frequency component.

[0135] In an exemplary embodiment, the processing of abnormal points in the subcarrier data may include:

[0136] For each data in the subcarrier data, the absolute deviation between the data and the median of two adjacent data is calculated. If the absolute deviation exceeds a preset deviation threshold, the data is replaced with the median of the two adjacent data.

[0137] This exemplary embodiment can filter with a Hampel filter to remove abnormal points. The principle is that for each x value of the basic signal, the median value of the window consisting of x and m / 2 adjacent points on the left and right sides is calculated, and then the standard deviation of x about the median value of its window is calculated using the absolute deviation of the median value. If the difference between x and the median value exceeds a predefined MAD number, that is, a preset deviation threshold, its value will be replaced by the median value. In this exemplary embodiment, m=10 can be taken.

[0138] Fig.18 The module diagram of another vital sign detection method based on WiFi channel of this exemplary embodiment is shown, which may specifically include: a data acquisition module 1810, a data preprocessing module 1820, a human activity data extraction module 1830, and a vital sign detection module 1840. The data acquisition module 1810 includes a channel data information acquisition unit 1811 and a channel data information parsing unit 1812; the data preprocessing module 1820 includes a linear interpolation unit 1821, a zero-frequency component removal unit 1822, an abnormal point processing unit 1823, and a denoising unit 1824; the human activity data extraction module 1830 includes a target subcarrier determination unit 1831, a frequency domain transformation unit 1832, a local peak estimation unit 1833, and an independent component analysis unit 1834; the vital sign detection module 1840 includes a steady-state breathing energy calculation unit 1841, and a real-time energy ratio classification unit 1842.

[0139] In one implementation, the above-mentioned acquisition of channel state information of WiFi signals propagating in a preset space may include the following steps:

[0140] Initialize the WiFi CSI acquisition tool and establish a socket for communicating with the wireless driver interface;

[0141] Use the WiFi CSI acquisition tool to obtain the channel status information of the WiFi signal through the socket;

[0142] The framework layer uses the callback function registered in the WiFi CSI acquisition tool to read the channel status information to the framework layer.

[0143] The WiFi CSI acquisition tool is used to obtain the channel status information of the WiFi signal from the hardware level and report it to the software level. Taking the Android system as an example, Fig.19As shown, the WiFi CSI acquisition tool can be configured and triggered by the wpa_supplicant instruction. Initialize the WiFi CSI acquisition tool, establish a socket that communicates with the wireless driver interface (such as nl80211), for example, two sockets can be created through Generic Netlink (generic network interface protocol cluster), wherein the first socket is used for the WiFi CSI acquisition tool to control the HAL (Hardware Abstract Layer, Hardware Abstract Layer) layer, such as cmd_socket, and the second socket is used for the WiFi CSI acquisition tool to notify the upper layer of events, such as event_socket. After the initialization is completed, the channel state information of the WiFi signal can be obtained through the socket, such as based on the Linux poll mechanism, from the main() function (main function) to enter the socket to call, such as through the event_socket loop to read the buffer in the socket, to obtain the channel state information from the wireless driver interface, and the channel state information obtained at this time can be in Hex (hexadecimal) form. The framework layer can implement the HIDL (HAL Interface Definition Language) interface after booting, register the callback function (callback) to the WiFi CSI acquisition tool, and define the callback function list (callbacks_list). In this way, the callback function list can be monitored to monitor the channel status information. After the WiFi CSI acquisition tool obtains the channel status information and caches it, it notifies the framework layer through the HIDL interface, so that the framework layer reads the channel status information to the framework layer. The framework layer can parse the physical quantity according to the Hex byte order of the channel status information, such as obtaining subcarrier data.

[0144] In an exemplary embodiment, the above step S210 may include:

[0145] In response to the terminal device meeting a preset condition, enabling a WiFi channel status information sensing function of the terminal device;

[0146] When the WiFi channel status information perception function is turned on, the channel status information of the WiFi signal propagating in the preset space is obtained.

[0147] In order to improve the timeliness and accuracy of the terminal device's determination of a person's vital signs information and avoid invalid calculations that increase system power consumption, this exemplary embodiment may set up a judgment mechanism to enable the terminal device's WiFi channel status information perception function when the terminal device meets preset conditions, and to obtain the channel status information of the WiFi signal propagating in a preset space when the WiFi channel status information perception function is enabled.

[0148] Specifically, in an exemplary embodiment, the above-mentioned preset condition may include at least one of the following:

[0149] Open the default application;

[0150] In charging state;

[0151] The device is in a stationary state, the battery level is greater than the battery threshold, and is currently within the preset time range.

[0152] Among them, the preset application may refer to an application related to the determination of vital signs information, or an application with the need to predict vital signs information. For example, when a user opens a related health application, the vital signs information of the user at the current moment or in the current time period is determined, or when the user opens games, short videos and other applications, in order to monitor the health of the user, the WiFi channel status information perception function of the terminal device can be activated.

[0153] Alternatively, it is also possible to consider turning on the WiFi channel status information perception function of the terminal device when the terminal device is in an idle state or in an application scenario with low power consumption. For example, the WiFi channel status information perception function can be turned on when the terminal device is in a charging state; or when it is in a stationary state, the power is greater than the power threshold, and it is currently in a preset time range, the WiFi channel status information perception function is turned on. Among them, the stationary state can be that the posture state of the terminal device has not changed or has not changed significantly within a preset time period, and its posture state can be detected and determined by configuring a gyroscope or accelerometer in the terminal device. The power threshold and the preset time range can be customized according to user needs, or intelligently adjusted according to the user's preference for using the terminal device. For example, it can be set that when the mobile phone is in a stationary state, the power is greater than 80%, and it is currently in the time range between 10 pm and 8 am the next morning, the WiFi channel status information perception function is automatically turned on to obtain the channel status information of the WiFi signal propagated in the preset space, so as to accurately monitor the user's vital signs information.

[0154] The exemplary embodiment of the present disclosure also provides a vital sign detection device based on a WiFi channel. Fig. 20As shown, the vital sign detection device 2000 based on the WiFi channel may include: a state information acquisition module 2010, used to obtain channel state information of the WiFi signal propagated in a preset space; the channel state information includes subcarrier data of multiple subcarriers; a target subcarrier determination module 2020, used to determine the target subcarrier from multiple subcarriers based on the energy values ​​of the subcarrier data of the multiple subcarriers in the frequency domain; a carrier data filtering module 2030, used to filter the subcarrier data of the target subcarrier using the frequency range of human activity to obtain second filtered data of the target subcarrier; a vital sign information determination module 2040, used to determine the vital sign information of the detected person according to the peak information of the second filtered data of the target subcarrier in the frequency domain.

[0155] In an exemplary embodiment, the target subcarrier determination module includes: an energy sorting unit, which is used to transform subcarrier data of multiple subcarriers into the frequency domain, and sort the energy values ​​of the multiple subcarriers in the frequency domain from high to low, and select a first preset number of subcarriers with front energy values ​​as target subcarriers.

[0156] In an exemplary embodiment, the vital sign detection device based on the WiFi channel also includes: a first filtering unit, which is used to filter the subcarrier data of multiple subcarriers using a basic frequency range before transforming the subcarrier data of multiple subcarriers to the frequency domain to obtain first filtered data of multiple subcarriers; an energy sorting unit, including: a frequency domain transformation unit, which is used to transform the first filtered data of multiple subcarriers to the frequency domain.

[0157] In an exemplary embodiment, the energy sorting unit includes: a first power spectrum density acquisition subunit, used to transform subcarrier data of multiple subcarriers into the frequency domain to obtain a first power spectrum density of each subcarrier in the frequency domain; a normalization processing subunit, used to normalize the first power spectrum density to obtain a normalized first power spectrum density; a first sorting unit, used to sort the largest normalized first power spectrum density under each subcarrier in descending order, and select a first preset number of subcarriers with a front normalized first power spectrum density as target subcarriers.

[0158] In an exemplary embodiment, the vital sign information determination module includes: a peak information analysis unit, used to determine the human activity frequency and / or human activity energy value of the detected person based on the peak information of the second filtered data of the target subcarrier in the frequency domain; a vital sign information determination unit, used to determine the vital sign information of the detected person based on the human activity frequency and / or human activity energy value.

[0159] In an exemplary embodiment, the peak information analysis unit includes: a main peak determination subunit, used to transform the second filtered data of the target subcarrier into the frequency domain, and determine the main peak of the second filtered data in the frequency domain; an activity data determination subunit, used to determine the frequency of human activity based on the frequency of the main peak, and / or determine the energy value of human activity based on the energy value of the main peak.

[0160] In an exemplary embodiment, the channel state information of the WiFi signal includes: the channel state information of the WiFi signal received by multiple links of the terminal device; the second filtered data includes second filtered data corresponding to multiple links; the main peak determination subunit includes: a frequency domain transformation subunit, which is used to transform the second filtered data corresponding to each link into the frequency domain, and determine the second power spectrum density of the target subcarrier in the frequency domain; a second power spectrum density determination subunit, which is used to normalize the second power spectrum density to obtain a normalized second power spectrum density; a main peak determination subunit, which is used to perform a peak search on the normalized second power spectrum density in the frequency domain for each link, determine a second preset number of peaks before the normalized second power spectrum density is sorted from high to low, and determine the peak whose normalized second power spectrum density exceeds the preset energy threshold as the main peak.

[0161] In an exemplary embodiment, the activity data determination subunit includes: a spectral density comparison subunit, which is used to compare the normalized second power spectrum density corresponding to the main peak of each link, and take the frequency corresponding to the largest normalized second power spectrum density as the human activity frequency.

[0162] In an exemplary embodiment, the terminal device includes multiple WiFi antennas, each WiFi antenna forms a link; the terminal device has four sides except the display plane and the plane relative to the display plane, wherein the first side and the third side are long sides, the second side and the fourth side are short sides, and the second side and the fourth side are the top side and the bottom side respectively; if the terminal device includes two WiFi antennas, the two WiFi antennas are respectively distributed on any long side and the second side, and the phase spacing of the two WiFi antennas is greater than a preset distance; if the terminal device includes three WiFi antennas, then among the three WiFi antennas, the first WiFi antenna is arranged on the first side, the second WiFi antenna is arranged on the top side, and the third WiFi antenna is arranged at a position between the middle of the third side and close to the fourth side.

[0163] In an exemplary embodiment, the vital sign information includes activity status information; the vital sign information determination unit includes: a reference value determination subunit, used to obtain the human activity energy value within a first preset time when the most recent human activity frequency is stable, and determine the human activity energy reference value based on the human activity energy value within the first preset time; an energy ratio judgment subunit, used to determine the activity status information of the detected person based on the ratio of the real-time human activity energy value to the human activity energy reference value.

[0164] In an exemplary embodiment, the vital sign information includes sleep quality information; the vital sign information determination unit includes: an energy value acquisition subunit, used to obtain human activity energy values ​​of the detected person at multiple sampling time points during sleep; a sleep information determination subunit, used to determine the sleep quality information of the detected person based on the average value of the human activity energy values ​​at multiple sampling time points and the human activity energy value at each sampling time point.

[0165] In an exemplary embodiment, the vital sign detection device based on the WiFi channel also includes: a preprocessing unit, which is used to preprocess the subcarrier data of multiple subcarriers after acquiring the channel state information of the WiFi channel; and a target subcarrier determination module, which is used to determine the target subcarrier from the multiple subcarriers based on the energy values ​​of the subcarrier data of the preprocessed multiple subcarriers in the frequency domain.

[0166] In an exemplary embodiment, subcarrier data of multiple subcarriers are preprocessed, including at least one of the following: an interpolation unit, used to interpolate the subcarrier data to reconstruct the lost data packets in the subcarrier data, and obtain subcarrier data containing the lost data packets; a component removal unit, used to remove zero-frequency components from the subcarrier data; an outlier removal unit, used to process outliers in the subcarrier data; and a denoising unit, used to denoise the subcarrier data.

[0167] In an exemplary embodiment, the interpolation unit is used to determine the missing timestamp according to the timestamp of the received data packet in the subcarrier data, and to reconstruct the lost data packet corresponding to the missing timestamp by interpolating based on the received data packets adjacent to the missing timestamp.

[0168] In an exemplary embodiment, the component removal unit is used to calculate an amplitude average value for each row of subcarrier data, and subtract the amplitude average value from the amplitude of each row to obtain subcarrier data with zero-frequency components removed.

[0169] In an exemplary embodiment, the outlier removal unit is used to calculate the absolute deviation between each data in the subcarrier data and the median of two adjacent data, and if the absolute deviation exceeds a preset deviation threshold, replace the data with the median of the two adjacent data.

[0170] In an exemplary embodiment, the status information acquisition module includes: a socket establishment unit, which is used to initialize the WiFi CSI acquisition tool and establish a socket for communicating with the wireless driver interface; an information acquisition unit, which is used to use the WiFi CSI acquisition tool to obtain the channel status information of the WiFi signal through the socket; an information reading unit, which is used by the framework layer to read the channel status information to the framework layer using the callback function registered in the WiFi CSI acquisition tool.

[0171] In an exemplary embodiment, the status information acquisition module includes: a function activation unit, which is used to activate the WiFi channel status information perception function of the terminal device in response to the terminal device meeting a preset condition; an information acquisition unit, which is used to acquire the channel status information of the WiFi signal propagated in a preset space when the WiFi channel status information perception function is activated.

[0172] In an exemplary embodiment, the preset condition includes at least one of the following: opening a preset application; being in a charging state; being in a stationary state, the power level is greater than a power threshold, and is currently within a preset time range.

[0173] The specific details of each part of the above device have been described in detail in the implementation method part, so they will not be repeated here.

[0174] The exemplary embodiments of the present disclosure further provide a computer-readable storage medium, which can be implemented in the form of a program product, including program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification, for example, to execute Figure 2 or Figure 3 The program product may be in the form of a portable compact disk read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.

[0175] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0176] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0177] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0178] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0179] The exemplary embodiment of the present disclosure further provides an electronic device. The electronic device may include a processor and a memory, wherein the memory is used to store executable instructions of the processor, and the processor is configured to execute the above-mentioned vital sign detection method based on a WiFi channel by executing the executable instructions.

[0180] Below Fig.21The mobile terminal 2100 in FIG. 1 is taken as an example to exemplify the structure of the electronic device. It should be understood by those skilled in the art that, in addition to the components specifically used for mobile purposes, Fig.21 The construction in can also be applied to fixed type equipment.

[0181] like Fig.21 As shown, the mobile terminal 2100 may specifically include: a processor 2101, a memory 2102, a bus 2103, a mobile communication module 2104, an antenna 1, a wireless communication module 2105, an antenna 2, a display screen 2106, a camera module 2107, an audio module 2108, a power module 2109 and a sensor module 2110.

[0182] Processor 2101 may include one or more processing units, for example: processor 2101 may include AP (Application Processor), modem processor, GPU (Graphics Processing Unit), ISP (Image Signal Processor), controller, encoder, decoder, DSP (Digital Signal Processor), baseband processor and / or NPU (Neural-Network Processing Unit), etc.

[0183] The encoder can encode (i.e. compress) an image or video to reduce the data size for easy storage or transmission. The decoder can decode (i.e. decompress) the encoded data of the image or video to restore the image or video data. The mobile terminal 2100 can support one or more encoders and decoders, such as: image formats such as JPEG (Joint Photographic Experts Group), PNG (Portable Network Graphics), BMP (Bitmap), and video formats such as MPEG (Moving Picture Experts Group) 1, MPEG10, H.1063, H.1064, and HEVC (High Efficiency Video Coding).

[0184] The processor 2101 may be connected to the memory 2102 or other components via a bus 2103 .

[0185] The memory 2102 may be used to store computer executable program codes, which may include instructions. The processor 2101 executes various functional applications and data processing of the mobile terminal 2100 by running the instructions stored in the memory 2102. The memory 2102 may also store application data, such as images, videos, and other files.

[0186] The communication function of the mobile terminal 2100 can be implemented by the mobile communication module 2104, antenna 1, wireless communication module 2105, antenna 2, a modulation and demodulation processor, and a baseband processor. Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. The mobile communication module 2104 can provide 3G, 4G, 5G and other mobile communication solutions applied to the mobile terminal 2100. The wireless communication module 2105 can provide wireless communication solutions such as wireless LAN, Bluetooth, and near field communication applied to the mobile terminal 2100.

[0187] The display screen 2106 is used to implement display functions, such as displaying user interfaces, images, videos, etc. The camera module 2107 is used to implement shooting functions, such as shooting images, videos, etc. The audio module 2108 is used to implement audio functions, such as playing audio, collecting voice, etc. The power module 2109 is used to implement power management functions, such as charging the battery, powering the device, monitoring the battery status, etc. The sensor module 2110 may include one or more sensors for implementing corresponding sensing detection functions. For example, the sensor module 2110 may include an inertial sensor, which is used to detect the motion posture of the mobile terminal 2100 and output inertial sensing data.

[0188] It will be appreciated by those skilled in the art that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or implementations combining hardware and software aspects, which may be collectively referred to herein as "circuit", "module" or "system". Those skilled in the art will readily think of other implementations of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and implementation are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0189] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A vital sign detection method based on WiFi channel, characterized in that: include: Obtain channel status information of WiFi signals propagating in a preset space; The channel state information includes subcarrier data of a plurality of subcarriers; Determining a target subcarrier from the multiple subcarriers based on energy values ​​of subcarrier data of the multiple subcarriers in the frequency domain; Filtering the subcarrier data of the target subcarrier using the frequency range of human activities to obtain second filtered data of the target subcarrier; The vital sign information of the detected person is determined according to the peak information of the second filtered data of the target subcarrier in the frequency domain.

2. The method according to claim 1, characterized in that The determining a target subcarrier from the multiple subcarriers based on energy values ​​of subcarrier data of the multiple subcarriers in the frequency domain comprises: Subcarrier data of the multiple subcarriers are transformed into the frequency domain, and energy values ​​of the multiple subcarriers in the frequency domain are sorted from high to low, and a first preset number of subcarriers with front energy values ​​are selected as the target subcarriers.

3. The method according to claim 2, characterized in that Before transforming the subcarrier data of the plurality of subcarriers into the frequency domain, the method further comprises: Filtering the subcarrier data of the plurality of subcarriers using a basic frequency range to obtain first filtered data of the plurality of subcarriers; The transforming the subcarrier data of the plurality of subcarriers into the frequency domain comprises: The first filtered data of the plurality of subcarriers is transformed into a frequency domain.

4. The method according to claim 2, characterized in that: The converting the subcarrier data of the multiple subcarriers into the frequency domain, sorting the energy values ​​of the multiple subcarriers in the frequency domain from high to low, and selecting a first preset number of subcarriers with front energy values ​​as the target subcarriers, includes: Transforming subcarrier data of the multiple subcarriers into the frequency domain to obtain a first power spectrum density of each of the subcarriers in the frequency domain; Normalizing the first power spectral density to obtain a normalized first power spectral density; The largest normalized first power spectrum densities under each of the subcarriers are sorted in descending order, and a first preset number of subcarriers with front normalized first power spectrum densities are selected as the target subcarriers.

5. The method according to claim 1, characterized in that The determining the vital sign information of the detected person according to the peak information of the second filtered data of the target subcarrier in the frequency domain includes: Determine the human activity frequency and / or human activity energy value of the detected person according to the peak information of the second filtered data of the target subcarrier in the frequency domain; The vital sign information of the detected person is determined according to the human activity frequency and / or the human activity energy value.

6. The method according to claim 5, characterized in that The determining the human activity frequency and / or human activity energy value of the detected person according to the peak information of the second filtered data of the target subcarrier in the frequency domain includes: Transforming the second filtered data of the target subcarrier into the frequency domain, and determining a main peak of the second filtered data in the frequency domain; The human activity frequency is determined according to the frequency of the main wave peak, and / or the human activity energy value is determined according to the energy value of the main wave peak.

7. The method according to claim 6, characterized in that The channel state information of the WiFi signal includes: channel state information of the WiFi signal received by multiple links of the terminal device; the second filtered data includes second filtered data corresponding to the multiple links; The transforming the second filtered data of the target subcarrier into the frequency domain and determining the main peak of the second filtered data in the frequency domain includes: Transforming the second filtered data corresponding to each link into the frequency domain, and determining a second power spectrum density of the target subcarrier in the frequency domain; Normalizing the second power spectral density to obtain a normalized second power spectral density; For each link, a peak search is performed on the normalized second power spectral density in the frequency domain to determine a second preset number of peaks before the normalized second power spectral density is sorted from high to low, and the peak in which the normalized second power spectral density exceeds a preset energy threshold is determined as the main peak.

8. The method according to claim 7, characterized in that Determining the human activity frequency according to the frequency of the main peak includes: The normalized second power spectrum densities corresponding to the main peaks of each link are compared, and the frequency corresponding to the maximum normalized second power spectrum density is taken as the human activity frequency.

9. The method according to claim 7, characterized in that: The terminal device comprises a plurality of WiFi antennas, each WiFi antenna forming a link; the terminal device has four sides except a display plane and a plane opposite to the display plane, wherein a first side and a third side are long sides, a second side and a fourth side are short sides, and the second side and the fourth side are a top side and a bottom side respectively; If the terminal device includes two WiFi antennas, the two WiFi antennas are respectively distributed on any one of the long sides and the second side, and a phase spacing between the two WiFi antennas is greater than a preset distance; If the terminal device includes three WiFi antennas, among the three WiFi antennas, the first WiFi antenna is set on the first side, the second WiFi antenna is set on the top side, and the third WiFi antenna is set at a position between the middle of the third side and close to the fourth side.

10. The method according to claim 5, characterized in that The vital sign information includes activity status information; the determining the vital sign information of the detected person according to the human activity frequency and / or the human activity energy value includes: Acquire a human activity energy value within a first preset time when the most recent human activity frequency is stable, and determine a human activity energy reference value according to the human activity energy value within the first preset time; The activity state information of the detected person is determined according to the ratio of the real-time human activity energy value to the human activity energy reference value.

11. The method according to claim 5, characterized in that The vital sign information includes sleep quality information; the determining the vital sign information of the detected person according to the human activity frequency and / or the human activity energy value includes: Obtaining human activity energy values ​​of the detected person at multiple sampling time points during sleep; The sleep quality information of the detected person is determined according to an average value of the human activity energy values ​​at the multiple sampling time points and the human activity energy value at each sampling time point.

12. The method according to claim 1, characterized in that After acquiring the channel status information of the WiFi channel, the method further includes: Preprocessing subcarrier data of the multiple subcarriers; The determining a target subcarrier from the multiple subcarriers based on energy values ​​of subcarrier data of the multiple subcarriers in the frequency domain comprises: Based on the energy values ​​of the preprocessed subcarrier data of the multiple subcarriers in the frequency domain, a target subcarrier is determined from the multiple subcarriers.

13. The method according to claim 12, characterized in that The preprocessing of the subcarrier data of the multiple subcarriers includes at least one of the following: interpolating the subcarrier data to reconstruct the lost data packet in the subcarrier data, and obtaining subcarrier data including the lost data packet; Removing zero-frequency components from the subcarrier data; Processing abnormal points in the subcarrier data; The subcarrier data is denoised.

14. The method according to claim 13, characterized in that The interpolating the subcarrier data to reconstruct the lost data packet in the subcarrier data includes: The missing timestamp is determined according to the timestamp of the received data packet in the subcarrier data, and the lost data packet corresponding to the missing timestamp is reconstructed by interpolation based on the received data packets adjacent to the missing timestamp.

15. The method according to claim 13, characterized in that The subcarrier data is a two-dimensional array; removing the zero-frequency component from the subcarrier data includes: An amplitude average value is calculated for each row of the subcarrier data, and the amplitude average value is subtracted from the amplitude of each row to obtain subcarrier data with zero-frequency components removed.

16. The method according to claim 13, characterized in that The processing of abnormal points in the subcarrier data includes: For each data in the subcarrier data, an absolute deviation between the data and a median value of two adjacent data is calculated; if the absolute deviation exceeds a preset deviation threshold, the data is replaced with the median value of the two adjacent data.

17. The method according to claim 1, characterized in that The obtaining of channel state information of WiFi signals propagating in a preset space includes: Initialize the WiFi CSI acquisition tool and establish a socket for communicating with the wireless driver interface; Using the WiFi CSI acquisition tool to acquire channel state information of the WiFi signal through the socket; The framework layer uses the callback function registered in the WiFi CSI acquisition tool to read the channel state information to the framework layer.

18. The method according to claim 1, characterized in that The obtaining of channel state information of WiFi signals propagating in a preset space includes: In response to the terminal device meeting a preset condition, enabling a WiFi channel status information sensing function of the terminal device; When the WiFi channel state information perception function is turned on, channel state information of WiFi signals propagating in a preset space is obtained.

19. The method according to claim 18, characterized in that The preset conditions include at least one of the following: Open the default application; In charging state; The device is in a stationary state, the battery level is greater than the battery threshold, and is currently within the preset time range.

20. A vital sign detection device based on WiFi channel, characterized in that: include: A status information acquisition module, used to acquire channel status information of WiFi signals propagating in a preset space; The channel state information includes subcarrier data of a plurality of subcarriers; a target subcarrier determination module, configured to determine a target subcarrier from the multiple subcarriers based on energy values ​​of subcarrier data of the multiple subcarriers in the frequency domain; A carrier data filtering module, used to filter the subcarrier data of the target subcarrier using the frequency range of human activities to obtain second filtered data of the target subcarrier; The vital sign information determination module is used to determine the vital sign information of the detected person according to the peak information of the second filtered data of the target subcarrier in the frequency domain.

21. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 19 is implemented.

22. An electronic device, characterized in that: include: processor; A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 19 by executing the executable instructions.

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