Vital sign detection method and device, terminal equipment and storage medium

Abstract

The invention discloses a vital sign detection method and device, terminal equipment and a storage medium, the method is applied to the terminal equipment, and the method comprises the following steps: determining a first distance between the terminal equipment and a target user; if the first distance is smaller than a preset distance, detecting vital sign information of the target user based on channel state information of a Wi-Fi signal; and if the first distance is not less than a preset distance, detecting vital sign information of the target user based on ultra-wideband echo data.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of communication technology, and more specifically, to a method and apparatus for detecting vital signs, a terminal device, and a storage medium. Background Art

[0002] Vital sign detection is an indispensable part in the field of healthcare. By detecting the respiration rate (RR) and heart rate (HR), medical personnel can diagnose clinical diseases for patients and monitor the condition through vital sign indicators. Currently, the most common vital sign detection is to use contact sensor devices such as pulse oximeters to contact the human body to obtain vital sign information. However, for users such as burn patients or newborns who are not suitable for skin contact, contact vital sign detection equipment cannot be used. In addition, in the case of long-term monitoring, contact vital sign detection equipment has the problem of inconvenience and discomfort in use. Non-contact life detection technology based on wireless signals can solve the above drawbacks, but non-contact life detection technology based on wireless signals still has certain limitations. Summary of the invention

[0003] The embodiments of the present application provide a method and apparatus for detecting vital signs, a terminal device, and a storage medium. The following introduces various aspects of the embodiments of the present application.

[0004] In a first aspect, a method for detecting vital signs is provided, the method being applied to a terminal device, the method comprising: determining a first distance between the terminal device and a target user; if the first distance is less than a preset distance, detecting vital sign information of the target user based on channel state information of a Wi-Fi signal; if the first distance is not less than the preset distance, detecting vital sign information of the target user based on ultra-wideband echo data.

[0005] In a second aspect, a vital sign detection device is provided, which is applied to a terminal device, and includes: a determination module, used to determine that the distance between the terminal device and a target user is a first distance; a detection module, used to detect the vital sign information of the target user based on the channel state information of the Wi-Fi signal if the first distance is less than a preset distance; if the first distance is not less than the preset distance, the vital sign information of the target user is detected based on ultra-wideband echo data.

[0006] In a third aspect, a terminal device is provided, comprising a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to control the terminal device to execute the method described in the first aspect.

[0007] According to a fourth aspect, a computer-readable storage medium is provided, on which executable code is stored. When the executable code is executed, the method described in the first aspect can be implemented.

[0008] The embodiment of the present application proposes a method for detecting vital signs, which is applied to a terminal device, and the method includes: determining that the distance between the terminal device and the target user is a first distance; if the first distance is less than a preset distance, detecting the vital sign information of the target user based on the channel state information of the Wi-Fi signal; if the first distance is not less than the preset distance, detecting the vital sign information of the target user based on the ultra-wideband echo data. This solution is based on the joint perception of the vital sign information of the target user by the Wi-Fi signal and the ultra-wideband signal. The perception ranges of the two are complementary, which reduces the perception blind spots, thereby helping to improve the detection accuracy and detection robustness of the vital sign information. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a schematic diagram of a system architecture applied in an embodiment of the present application.

[0010] Figure 2 It is a schematic diagram of another system architecture applied in an embodiment of the present application.

[0011] Figure 3 It is a flow chart of a method for detecting vital signs provided in one embodiment of the present application.

[0012] Figure 4 It is a flowchart of a vital sign detection method based on Wi-Fi signals provided in one embodiment of the present application.

[0013] Figure 5 It is a sub-flow diagram of a vital sign detection method based on Wi-Fi signals provided in one embodiment of the present application.

[0014] Figure 6 This is a schematic diagram of carrier data in which the human activity provided by an embodiment of the present application is a breathing activity.

[0015] Figure 7 It is a schematic diagram comparing the data with normalization correction introduced and the traditional measurement data provided by an embodiment of the present application.

[0016] Figure 8 It is a schematic diagram of a Wi-Fi antenna layout provided in an embodiment of the present application.

[0017] Figure 9(a)-Figure 9(b) It is a time domain schematic diagram of each subcarrier in the channel state data received by different links provided in an embodiment of the present application.

[0018] Figure 10(a)-Figure 10(b) It is a time domain schematic diagram of link 1 before and after data filtering provided by an embodiment of the present application.

[0019] Fig.11 It is a frequency domain schematic diagram of link 1 data provided by an embodiment of the present application.

[0020] Figure 12(a)-Figure 12(b) It is a time domain schematic diagram of link 2 data before and after filtering provided by an embodiment of the present application.

[0021] Fig.13 It is a frequency domain schematic diagram of link 2 data provided by an embodiment of the present application.

[0022] Figure 14(a)-Figure 14(c) This is a time domain schematic diagram of the first link carrier data provided by an embodiment of the present application.

[0023] Fig.15 It is a frequency domain schematic diagram of the first link carrier data provided by an embodiment of the present application.

[0024] Figure 16(a)-Figure 16(c) It is a time domain schematic diagram of the second link carrier data provided by an embodiment of the present application.

[0025] Fig.17 It is a frequency domain schematic diagram of the second link carrier data provided by an embodiment of the present application.

[0026] Fig.18 This is a flowchart of a sleep quality assessment method provided in one embodiment of the present application.

[0027] Fig.19 It is a module diagram of a vital sign detection method based on WiFi channel provided in one embodiment of the present application.

[0028] Fig. 20 This is a flow chart for obtaining channel state information provided by an embodiment of the present application.

[0029] Fig.21 It is a flowchart of a vital sign detection method based on UWB signals provided in one embodiment of the present application.

[0030] Fig. 22 It is a sub-flow diagram of a vital sign detection method based on UWB signals provided in an embodiment of the present application.

[0031] Fig.23 It is a flowchart of a vital sign detection method based on UWB signals provided in another embodiment of the present application.

[0032] Fig.24 It is a structural schematic diagram of an antenna layout provided in an embodiment of the present application.

[0033] Fig.25 It is a structural diagram of a radio frequency path provided in an embodiment of the present application.

[0034] Fig.26 It is a structural schematic diagram of a radio frequency path provided in another embodiment of the present application.

[0035] Fig. 27 It is a structural schematic diagram of a vital sign detection device provided in one embodiment of the present application.

[0036] Fig.28 It is a structural diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0038] It should be noted that the terminal device mentioned in the embodiments of the present application can be any type of electronic device with wireless communication function. The terminal device can be a portable mobile terminal or a handheld mobile terminal. For example, the terminal device can be a smart phone, a tablet computer, a laptop computer, a PDA, a mobile internet device (MID), a wearable device, etc., and the present application does not make specific restrictions on this.

[0039] Vital sign detection is an essential part of healthcare. By detecting respiratory rate RR and heart rate HR, medical personnel can diagnose clinical diseases for patients and monitor their condition through vital sign indicators. Currently, the most common vital sign detection method is to use contact sensor devices such as pulse oximeters to contact the human body to obtain vital sign information. However, for users such as burn patients or newborns who are not suitable for skin contact, contact vital sign detection equipment cannot be used. In addition, in the case of long-term monitoring, contact vital sign detection equipment is inconvenient and uncomfortable to use.

[0040] Non-contact life detection technology based on wireless signals can solve the above drawbacks. However, wireless signals are easily interfered with and still have certain limitations. For example, vital sign detection based on Wi-Fi signals cannot be detected at a long distance; for another example, vital sign detection based on ultra-wideband signals has a problem of large detection errors at close distances due to signal coupling between antennas.

[0041] In order to solve the above problems, an embodiment of the present application proposes a method for detecting vital signs, which is applied to a terminal device, and the method includes: determining that the distance between the terminal device and the target user is a first distance; if the first distance is less than the preset distance, the vital sign information of the target user is detected based on the channel state information of the Wi-Fi signal; if the first distance is not less than the preset distance, the vital sign information of the target user is detected based on the ultra-wideband echo data. This solution is based on the joint perception of the vital sign information of the target user by Wi-Fi signals and ultra-wideband signals. The perception ranges of the two are complementary, which reduces the perception blind spots, thereby helping to improve the detection accuracy and detection robustness of vital sign information.

[0042] In order to better understand the method in the embodiment of the present application, Figure 1 and Figure 2 Examples are given of the principles of vital signs detection based on ultra-wideband signals and the principles of vital signs detection based on Wi-Fi signals.

[0043] like Figure 1 As shown, the system architecture 100 may include a terminal device 110 and a user 120. The terminal device 110 may be an electronic device such as a mobile phone, a tablet computer, or a smart wearable device. The terminal device 110 may be based on ultra wideband (UWB) radar technology, and may transmit a series of short-duration low-power pulses to the user 120, which are then reflected back by the user 120. Because breathing and heartbeats cause periodic movements of the chest wall, the radar echo signal carries information related to breathing and heartbeats. Therefore, the terminal device 110 may detect the vital signs of the user 120, such as human respiratory rate, heart rate, etc., based on the ultra wideband echo data.

[0044] It is understandable that ultra-wideband radar has the characteristics of low power consumption, anti-interference, anti-multipath, and good penetration compared to other types of radars. The pulse characteristics can not only achieve low power consumption performance, but also allow high-resolution positioning and sensing, which has great advantages in applications such as target detection of hidden moving objects, identification of breathing and heartbeat signals, and short-distance data transmission. However, it has been found that due to the coupling phenomenon of ultra-wideband antenna transceiver signals, when the terminal device 110 is close to the user 120, it will cause a large error in vital sign detection.

[0045] like Figure 2As shown, the system architecture 200 may include a terminal device 210, a user 220 and a gateway device 230. The terminal device 210 may be an electronic device such as a mobile phone, a tablet computer, or a smart wearable device. The gateway device 230 may be a hardware device such as a router that can connect to a network or transmit data information. The terminal device 210 and the gateway device 230 may be connected via a wired or wireless communication link to exchange data. The system architecture 200 covers Wi-Fi signals. The terminal device 210 may be connected to the gateway device 230 to detect the vital signs of the user 220, such as human respiratory rate, heart rate, etc., by acquiring the channel state information (CSI) of the Wi-Fi channel.

[0046] Through research and development, it was found that in the process of using the channel state information of the Wi-Fi channel to detect vital signs, the terminal device 210 only receives the channel state information of the Wi-Fi channel, and there is no coupling phenomenon between the sending and receiving signals. Therefore, when the terminal device 210 is close to the user 220, the vital sign detection based on the Wi-Fi signal still has a high detection accuracy. However, due to the limitations of the Wi-Fi communication mechanism, when the terminal device 210 is far away from the user 220, the vital sign detection based on the Wi-Fi signal will result in a large detection error.

[0047] Combined with the following Figure 3 The method for detecting vital signs in the embodiments of the present application is described in detail. Figure 3 As shown, the method for detecting vital signs can be used in a terminal device, and it should be understood that the terminal device can be any type of terminal device mentioned above. The method for detecting vital signs can include steps: S310 to S330.

[0048] In step S310, a first distance between a terminal device and a target user is determined.

[0049] The target user may refer to the user whose vital signs are to be tested. For example, the target user may be Figure 1 User 120 or Figure 2 User 220 in .

[0050] In some embodiments, the distance between the terminal device and the target user can be measured using ultra-wideband technology to obtain a first distance. As an example, the terminal device can transmit a narrowband pulse signal to the user, which is then reflected back by the user. The terminal device can receive ultra-wideband echo data and pre-process the ultra-wideband echo data (e.g., clutter processing). Then, the distance between the terminal device and the target user can be calculated using the time of flight (ToF) of the narrowband pulse signal.

[0051] In some embodiments, the terminal device may include one or more Wi-Fi antennas, and one or more ultra-wideband antennas.

[0052] In step S320, if the first distance is less than the preset distance, the vital sign information of the target user is detected based on the channel state information of the Wi-Fi signal. The preset distance can be set according to the requirements, and this application does not impose specific restrictions on this. The preset distance can be 30 cm, for example.

[0053] In step S330, if the first distance is not less than the preset distance, vital sign information of the target user is detected based on the ultra-wideband echo data.

[0054] This solution is based on the joint perception of the target user's vital signs information by Wi-Fi signals and ultra-wideband signals. The two have complementary perception ranges, reducing perception blind spots, thereby helping to improve the detection accuracy of vital signs information.

[0055] Combine the following Figure 4 A method for detecting vital signs based on the channel state information of Wi-Fi signals is described. Figure 4 As shown, detecting vital sign information of a target user based on channel state information of a Wi-Fi signal may include steps: S410 to S440.

[0056] Step S410, obtaining channel state information of a Wi-Fi signal propagating in a preset space; the channel state information includes subcarrier data of a plurality of subcarriers.

[0057] In wireless communication, channel state information CSI is a kind of data for perceiving Wi-Fi, 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 an embodiment of the present application, the Wi-Fi signal can be propagated in a preset space, which can be any environment or scene, such as a home or a shopping mall. The Wi-Fi 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 Wi-Fi 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 Wi-Fi modules, such as a sweeping robot, a smart TV, etc. The embodiment of the present application can detect a person's vital signs by obtaining the channel state information of the Wi-Fi channel.

[0058] 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 an embodiment of the present application, the channel state information may include subcarrier data of multiple subcarriers.

[0059] Step S420: determining a target subcarrier from the multiple subcarriers based on energy values ​​of the subcarrier data of the multiple subcarriers in the frequency domain.

[0060] After acquiring the channel state information of the subcarrier data including multiple subcarriers, the embodiment of the present application may process the channel state information to determine the target subcarrier from the multiple subcarriers, so as to use the subcarrier data of the target subcarrier to perform subsequent steps. Specifically, the target subcarrier may be screened according to the energy value 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 according to the sorting result, one or more subcarriers with the largest energy values ​​are determined as the target subcarriers, or a preset number of subcarriers are determined as the target subcarriers according to the energy value, 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.

[0061] Step S430: filtering the subcarrier data of the target subcarrier using the frequency range of human activity to obtain second filtered data of the target subcarrier.

[0062] 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 may include breathing frequency, respiratory rate, 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. The embodiment of the present application 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 calm state, the amplitude of chest ups and downs during breathing is 0cm-3cm (centimeter), and the amplitude of chest ups and downs caused by heartbeat is 1.5mm-3.5mm (millimeter).

[0063] The embodiment of the present application 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, 0.15Hz to 0.8Hz can be determined as the frequency range of human activities, and the target subcarrier data is filtered; for heartbeat activities, 0.9Hz to 2Hz can be determined as the frequency range of human activities, and the target subcarrier data is filtered to determine the second filtered data after filtering. The second filtered data may include an indicator energy for characterizing the human body state, such as respiratory steady-state energy, etc. The frequency range of human activities can be adjusted or set according to the range of different activity types, and this application does not specifically limit this.

[0064] Step S440: 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.

[0065] After determining the second filtered data of the target subcarrier, in order to facilitate intuitive analysis of the data, the embodiment of the present application can transform it into the frequency domain to obtain waveform information about the second filtered data. Further, based on the peak information of the second filtered data of the target subcarrier in the frequency domain, the vital sign information of the person being tested can be determined.

[0066] 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.

[0067] In addition, the embodiments of the present application 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.

[0068] In summary, in this exemplary embodiment, channel state information of a Wi-Fi 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; based on energy values ​​of the subcarrier data of the multiple subcarriers in the frequency domain, a target subcarrier is determined from the multiple subcarriers; 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 based on 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. On the one hand, the embodiment of the present application 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 person being detected. 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, the embodiment of the present application 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 at the frequency, and improve the accuracy and effectiveness of the determination of vital sign information by effectively processing the data; on the other hand, the embodiment of the present application utilizes the frequency range of human activity to filter the subcarrier data of the target subcarrier, and combined with the characteristics of human activity, 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.

[0069] In some embodiments, the above step S420 may include:

[0070] 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.

[0071] In an embodiment of the present application, the subcarrier data of multiple subcarriers are transformed into the frequency domain, and the subcarrier data can be subjected to an N-point fast Fourier transform (FFT), 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 highest energy values ​​can be 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 an embodiment of the present application, 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.

[0072] Further, in some embodiments, before transforming the subcarrier data of the multiple subcarriers into the frequency domain, the above-mentioned vital sign detection method based on the Wi-Fi channel may further include: filtering the subcarrier data of the multiple subcarriers using the basic frequency range to obtain first filtered data of the multiple subcarriers. The above-mentioned transforming the subcarrier data of the multiple subcarriers into the frequency domain includes: transforming the first filtered data of the multiple subcarriers into the frequency domain.

[0073] Among them, the basic frequency range can refer to a reference frequency range that can be determined or identified according to the signal, including human activities in the scene. It 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. It 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 0.15Hz~2Hz; or it can be multiple ranges corresponding to different human activities, such as the basic frequency range of breathing activity is 0.15Hz-1Hz, and the basic frequency range of heartbeat activity is 0.8Hz~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.

[0074] In some embodiments, the above-mentioned transforming 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 leading energy values ​​as target subcarriers may include: transforming the subcarrier data of multiple subcarriers into the frequency domain to obtain a first power spectrum density of each subcarrier in the frequency domain; normalizing the first power spectrum density to obtain a normalized first power spectrum density; sorting the largest normalized first power spectrum density under each subcarrier in order from high to low, and selecting a first preset number of subcarriers with leading normalized first power spectrum density as target subcarriers.

[0075] Among them, power spectral density refers to the energy distribution of a signal per unit frequency in the frequency domain. The embodiment of the present application can first determine the power spectrum of the signal based on the result of Fourier transform, and then determine the power spectral density based on the ratio of the power spectrum to the frequency width.

[0076] In the embodiment of the present application, subcarrier data of multiple subcarriers can be transformed into the frequency domain, and the first power spectrum density of each subcarrier in the frequency domain can be determined by calculation. Then, each first power spectrum density can be normalized to obtain a normalized first power spectrum density, which is expressed as: Among them, 0.15Hz~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.

[0077] When selecting a target subcarrier, the embodiment of the present application can first filter the subcarrier data of multiple 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 first as target subcarriers based on a sorting result of 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 the vital sign information of the detected person based on the second filtered data.

[0078] In an embodiment of the present application, 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 such as breathing activities. The bandpass filter can be set to 0.15Hz to 0.8Hz, and the filtered data can be fast Fourier transformed to obtain the second filtered data. Similarly, the target subcarrier data of different links can be bandpass filtered in the frequency range of human activities such as heartbeat activities. The bandpass filter can be set to 0.9Hz to 2Hz, and the filtered data can be fast Fourier transformed to obtain the second filtered data.

[0079] In some embodiments, Figure 5 As shown, the above step S440 may include steps: S510 to S520.

[0080] Step S510: 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.

[0081] Step S520, determining the vital sign information of the detected person according to the human activity frequency and / or the human activity energy value.

[0082] 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.

[0083] 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.

[0084] In some embodiments, the above step S510 may include: transforming the second filtered data of the target subcarrier to the frequency domain, and determining the main peak of the second filtered data in the frequency domain; determining the human activity frequency according to the frequency of the main peak, and / or determining the human activity energy value according to the energy value of the main peak.

[0085] To facilitate data processing, an embodiment of the present application 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.

[0086] The embodiment of the present application can determine the frequency of human activity based on the frequency of the main peak, wherein the main peaks corresponding to different types of human activities can determine different frequencies of human activity, for example, the respiratory frequency is determined according to the main peak of the respiratory activity, and the heart rate is determined according to the main peak of the heart rate activity. There are many ways to determine the frequency of human activity based on the frequency of the main peak, for example, the frequency of human activity can be determined by weighted calculation based on each frequency of the main peak; or the frequency corresponding to one of the main peaks among the main peaks can be used as the frequency of human activity; or the energy value proportion of the largest main peak in the peak is calculated to determine the frequency of human activity, etc.

[0087] 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.

[0088] In some embodiments, the channel state information of the Wi-Fi signal includes: channel state information of the Wi-Fi signal received by multiple links of the terminal device; and the second filtered data includes second filtered data corresponding to the multiple links.

[0089] In an embodiment of the present application, the terminal device may receive channel state information of the Wi-Fi signal through multiple links, and the second filtering data may include second filtering data corresponding to the multiple links, wherein the link may be an antenna configured by the terminal device, and the channel state information of the Wi-Fi signal received through different antennas is the channel state information of the Wi-Fi signal received through multiple links of the terminal device.

[0090] Transforming the second filtered data of the target subcarrier to the frequency domain and determining the main peak of the second filtered data in the frequency domain may include the following steps: transforming the second filtered data corresponding to each link to the frequency domain, and determining the second power spectrum density of the target subcarrier in the frequency domain; normalizing the second power spectrum density to obtain a normalized second power spectrum density; for each link, performing a peak search 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 determining the peak whose normalized second power spectrum density exceeds a preset energy threshold as the main peak.

[0091] In an embodiment of the present application, 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 frequency range of human activity for respiratory activity and transformed to the frequency domain, and the second power spectrum density normalized 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. This application does not make specific restrictions on this. When M is 2 to 6, it can be used for the detection of human respiratory activity 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.

[0092] 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. This application does not make specific restrictions. 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 、SNPheart , can be customized according to actual needs, and this application does not make specific limitations on this.

[0093] Furthermore, in some embodiments, the above-mentioned determining the human activity frequency based on the frequency of the main peak includes: comparing the normalized second power spectrum density corresponding to the main peak of each link, and taking the frequency corresponding to the largest normalized second power spectrum density as the human activity frequency.

[0094] 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.

[0095] Figure 6 A schematic diagram of carrier data of a human 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. The embodiment of the present application 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.

[0096] Figure 7 A schematic diagram comparing the respiratory data after the normalized power spectrum density factor correction and the data measured by the traditional wearable device is shown, wherein curve c represents the respiratory data after the normalized power spectrum density factor correction, and curve d represents the respiratory data measured by the traditional wearable device (for example, the corresponding sensor data can be obtained by configuring a gyroscope or an accelerometer). It can be seen that the data determined by the embodiment of the present application is consistent with the traditional measurement data, indicating that the human activity data determined by the embodiment of the present application is more accurate.

[0097] In some embodiments, the terminal device has four sides other than 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.

[0098] Considering the integrated layout architecture of mobile phone Wi-Fi antenna communication perception, and considering the influence of user habits in 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 Wi-Fi 5G antenna or other.

[0099] The terminal device may include two Wi-Fi antennas, that is, a dual-antenna architecture is configured, and the two Wi-Fi 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 the top 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 8 As shown, the two Wi-Fi antennas may include a first Wi-Fi antenna 810 and a second Wi-Fi antenna 820. The first Wi-Fi antenna 810 may be arranged in the middle of the left long side, and the second Wi-Fi antenna 820 may be arranged in the middle of the upper top side, wherein the interval between the phase centers of the two Wi-Fi antennas is ≥ 1 / 2 wavelength, and the preset distance may be 30 mm, that is, the phase interval between the two Wi-Fi antennas is greater than 30 mm.

[0100] Through the above-mentioned antenna layout method, on the one hand, it can ensure that when the Wi-Fi 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 the embodiments of the present application, the problem of perception blind spots of a single antenna can be compensated.

[0101] Figure 9 shows a time domain schematic diagram of each subcarrier in the channel status data received by different links, Figure 9(a) shows a time domain schematic diagram of each subcarrier in link 1, and Figure 9(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 Wi-Fi signals received by different antennas are also quite different.

[0102] By processing the subcarriers of the link 1 in step S420, a schematic diagram of the subcarrier data of the target subcarrier of link 1 in the time domain as shown in FIG. 10(a) can be obtained. By filtering the subcarrier data, a schematic diagram of the second filtered data as shown in FIG. 10(b) can be obtained. Then, the subcarrier data is converted to the frequency domain to obtain the following: Fig.11 By processing the subcarriers of the above-mentioned link 2 in step S420, a schematic diagram of the subcarrier data of the target subcarrier of link 2 in the time domain as shown in FIG12(a) can be obtained. Filtering the subcarrier data can obtain a schematic diagram of the second filtered data as shown in FIG12(b), and then converting the subcarrier data to the frequency domain can obtain the following: Fig.13 The frequency domain diagram of the breathing frequency of link 2 is shown.

[0103] Based on the above Figure 10~ Fig.13 ,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.

[0104] 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.

[0105] As shown in FIG. 14 , FIG. 14 (a) shows the original channel state information of the first link, that is, a schematic diagram of subcarrier data including multiple subcarriers; FIG. 14 (b) shows a time domain schematic diagram of the target subcarrier of the first link; and FIG. 14 (c) shows a time domain schematic diagram of the second filtered data after filtering in the first link; Fig.15 A frequency domain schematic diagram of the breathing data of the second filtered data of the first link is shown. As shown in Figure 16, Figure 16 (a) shows the original channel state information of the second link, that is, a schematic diagram of subcarrier data containing multiple subcarriers; Figure 16 (b) shows a time domain schematic diagram of the target subcarrier of the second link; Figure 16 (c) shows a time domain schematic diagram of the second filtered data after filtering in the second link; Fig.17 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.

[0106] In some embodiments, the vital sign information may include activity status information.

[0107] The above-mentioned step S520 may include: obtaining the human activity energy value within the first preset time when the most recent human activity frequency is stable, and determining the human activity energy reference value based on the human activity energy value within the first preset time; determining the activity status information of the person being detected based on the ratio of the real-time human activity energy value to the human activity energy reference value.

[0108] 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. The embodiment of the present application 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.

[0109] 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.

[0110] In some embodiments, the vital sign information includes sleep quality information.

[0111] The above-mentioned step S520 may include: obtaining human activity energy values ​​of the person being tested at multiple sampling time points during sleep; and determining the sleep quality information of the person being tested 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.

[0112] 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 determined by the system, 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.

[0113] In some embodiments, after the above step S510, the above method for detecting vital signs based on a Wi-Fi channel may further include: preprocessing subcarrier data of a plurality of subcarriers;

[0114] Furthermore, the above step S520 may include: determining a target subcarrier from the multiple subcarriers based on energy values ​​of the preprocessed subcarrier data of the multiple subcarriers in the frequency domain.

[0115] In order to avoid signal interference, signal loss and other problems from affecting the acquired channel state data, and thus affecting the accuracy of vital sign information determination, the embodiment of the present application can pre-process multiple subcarrier data, and determine the target subcarrier from multiple subcarriers based on the energy value of the subcarrier data of the preprocessed multiple subcarriers in the frequency domain.

[0116] Fig.18 A schematic diagram of a process framework for sleep quality assessment in an embodiment of the present application is shown, which may specifically include: a channel state data reporting module 1810, used to obtain channel state data; a channel state data processing module 1820, used to pre-process the channel state data; a human activity data extraction module 1830, used to extract human activity data, such as human activity frequency or human activity energy value, etc.; a vital sign information determination module 1840, used to determine vital sign information based on human activity data; and a sleep quality information determination module 1850, used to assess the sleep quality of the person being tested.

[0117] Specifically, in some embodiments, the above-mentioned preprocessing of subcarrier data of multiple subcarriers may include at least one of the following: interpolating the subcarrier data to reconstruct the lost data packets in the subcarrier data to obtain subcarrier data containing the lost data packets; removing zero-frequency components from the subcarrier data; processing abnormal points in the subcarrier data; and denoising the subcarrier data.

[0118] 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 this application. 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.

[0119] In the embodiment of the present application, 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.

[0120] In some embodiments, the above-mentioned interpolation of subcarrier data to reconstruct lost data packets in the subcarrier data may include: determining a missing timestamp based on a timestamp of a received data packet in the subcarrier data, and interpolating based on received data packets adjacent to the missing timestamp to reconstruct the lost data packet corresponding to the missing timestamp.

[0121] 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.

[0122] In some embodiments, the above-mentioned subcarrier data is a two-dimensional array; removing the zero-frequency component from the subcarrier data includes: calculating the amplitude average value for each row of the subcarrier data, and subtracting the amplitude average value from the amplitude of each row to obtain the subcarrier data with the zero-frequency component removed.

[0123] The embodiment of the present application 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.

[0124] In some embodiments, the above-mentioned processing of abnormal points in the subcarrier data may include: for each data in the subcarrier data, calculating the absolute deviation between the data and the median of two adjacent data, and if the absolute deviation exceeds a preset deviation threshold, replacing the data with the median of the two adjacent data.

[0125] The embodiment of the present application can be filtered by 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 composed of x and m / 2 adjacent points on the left and right sides is calculated, and then the absolute deviation of the median value is used to calculate the standard deviation of x about the median value of its window. If the difference between x and the median value exceeds the predefined MAD number, that is, the preset deviation threshold, its value will be replaced by the median value. In the embodiment of the present application, m=10 can be taken.

[0126] Fig.19 The module diagram of another vital sign detection method based on Wi-Fi channel in an embodiment of the present application is shown, which may specifically include: a data acquisition module 1910, a data preprocessing module 1920, a human activity data extraction module 1930, and a vital sign detection module 1940. The data acquisition module 1910 includes a channel data information acquisition unit 1911 and a channel data information parsing unit 1912; the data preprocessing module 1920 includes a linear interpolation unit 1921, a zero-frequency component removal unit 1922, an abnormal point processing unit 1923, and a denoising unit 1924; the human activity data extraction module 1930 includes a target subcarrier determination unit 1931, a frequency domain transformation unit 1932, a local peak estimation unit 1933, and an independent component analysis unit 1934; the vital sign detection module 1940 includes a steady-state breathing energy calculation unit 1941, and a real-time energy ratio classification unit 1942.

[0127] In one implementation, the above-mentioned acquisition of channel state information of Wi-Fi signals propagating in a preset space may include the following steps: initializing a Wi-Fi CSI acquisition tool to establish a socket for communicating with a wireless driver interface; using the Wi-Fi CSI acquisition tool to acquire the channel state information of the Wi-Fi signal through the socket; and the framework layer uses a callback function registered in the Wi-Fi CSI acquisition tool to read the channel state information to the framework layer.

[0128] The Wi-Fi CSI acquisition tool is used to obtain the channel status information of the Wi-Fi signal from the hardware level and report it to the software level. Taking the Android system as an example, Fig. 20 As shown, the Wi-Fi CSI acquisition tool can be configured and triggered by the wpa_supplicant instruction. The Wi-Fi CSI acquisition tool is initialized, and a socket for communicating with the wireless driver interface (such as nl80211) is established. For example, two sockets can be created through the Generic Netlink protocol cluster, wherein the first socket is used for the Wi-Fi CSI acquisition tool to control the Hardware Abstract Layer (HAL) layer, such as cmd_socket, and the second socket is used for the Wi-Fi 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 Wi-Fi signal can be obtained through the socket, such as based on the Linux poll mechanism, from the main() function (main function) to call the socket, such as looping through the event_socket 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 hardware abstraction layer interface definition language (HAL Interface Definition Language, HIDL) interface after powering on, register the callback function (callback) to the Wi-Fi 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 Wi-Fi 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.

[0129] In some embodiments, the above step S410 may include: in response to the terminal device satisfying a preset condition, enabling a Wi-Fi channel state information sensing function of the terminal device; when the Wi-Fi channel state information sensing function is enabled, obtaining channel state information of a Wi-Fi signal propagating in a preset space.

[0130] 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, the embodiment of the present application may set a judgment mechanism to enable a Wi-Fi channel state information perception function of the terminal device when the terminal device meets preset conditions, and obtain channel state information of Wi-Fi signals propagating in a preset space when the Wi-Fi channel state information perception function is enabled.

[0131] Specifically, in some embodiments, the above-mentioned preset conditions may include 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 in a preset time range.

[0132] 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 Wi-Fi channel status information perception function of the terminal device can be activated.

[0133] Alternatively, it is also possible to consider turning on the Wi-Fi 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 Wi-Fi 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 Wi-Fi 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 Wi-Fi channel status information perception function is automatically turned on to obtain the channel status information of the Wi-Fi signal propagated in the preset space, so as to accurately monitor the user's vital signs information.

[0134] Combine the following Fig.21 The method for detecting vital signs based on ultra-wideband echo data is described. Fig.21 As shown, detecting vital sign information of a target user based on ultra-wideband echo data may include steps: S2110 to S2130.

[0135] In step S2110, ultra-wideband echo data is received through an ultra-wideband antenna, where the ultra-wideband echo data includes a plurality of continuous groups of ultra-wideband echo signals.

[0136] In some embodiments, the ultra-wideband antenna may be an ultra-wideband circularly polarized antenna, that is, the ultra-wideband echo data may be received by the ultra-wideband circularly polarized antenna.

[0137] In the embodiment of the present application, the terminal device can continuously transmit a continuous pulse sequence to the target user through the ultra-wideband circularly polarized antenna to detect the vital signs of the target user. When the continuous pulse sequence is transmitted to the target user through the ultra-wideband circularly polarized antenna, it will be reflected back by the target user, and the pulse signal reflected back by the target user can be received by the ultra-wideband circularly polarized antenna to obtain ultra-wideband echo data.

[0138] The continuous multiple groups of ultra-wideband echo signals are ultra-wideband echo signals corresponding to different pulses. Different pulses are continuous pulse sequences emitted by the ultra-wideband circularly polarized antenna. Therefore, the ultra-wideband echo data received by the ultra-wideband circularly polarized antenna will include continuous multiple groups of ultra-wideband echo signals. The target user is an object with vital signs, such as humans and animals.

[0139] As a way, taking into account the multipath effect, since the ultra-wideband echo signal is the reflected signal of the transmitted pulse signal, the circularly polarized wave will change its polarization direction after reflection, such as from left to right. Therefore, in the terminal device, an ultra-wideband circularly polarized antenna can be used as an ultra-wideband radar to detect the vital signs of the target user, and an ultra-wideband circularly polarized antenna can be used as a transceiver antenna to reduce the impact of multipath. Among them, in order to overcome the multipath effect, when using an ultra-wideband circularly polarized antenna as a transceiver antenna, a left-handed circularly polarized antenna can be used as a transmitting antenna, and a right-handed circularly polarized antenna can be used as a receiving antenna. Specifically, a continuous pulse sequence can be transmitted to the target user through an ultra-wideband left-handed circularly polarized antenna, and the pulse signal reflected by the target user can be received by an ultra-wideband right-handed circularly polarized antenna to obtain multiple continuous groups of ultra-wideband echo signals.

[0140] Among them, the circularly polarized antenna is an antenna that can radiate or receive circularly polarized waves.

[0141] In an embodiment of the present application, the echo signal may be returned to the ultra-wideband circularly polarized antenna in the form of a signal matrix, or a matrix array of the echo signal may be generated after the ultra-wideband circularly polarized antenna receives the echo signal, without limitation.

[0142] In an embodiment of the present application, when a continuous pulse sequence emitted by an ultra-wideband circularly polarized antenna detects vital signs, the continuous pulse sequence passes through the vital signs and returns echo signals, and multiple groups of echo signals containing vital signs signals can constitute an echo signal matrix.

[0143] An ultra-wideband circularly polarized antenna is used to transmit continuous periodic pulse sequences for detection, and multiple continuous echo signals containing vital signs are received. The received echo signals of each pulse sequence are arranged in rows to form an echo signal matrix. Since the echo signals corresponding to each pulse sequence are arranged in an echo signal matrix, the waveform change effect of pulse sequence detection can be enhanced, and the accuracy of the detection results can be improved.

[0144] In step S2120, a target ultra-wideband echo signal corresponding to the location of the target user is determined from the multiple groups of ultra-wideband echo signals.

[0145] In an embodiment of the present application, the location of the target user is first determined based on multiple groups of ultra-wideband echo signals. After the location of the target user is determined, the ultra-wideband echo signal corresponding to the location of the target user is determined from the multiple groups of ultra-wideband echo signals.

[0146] In step S2130, the vital sign information of the target user is determined according to the target ultra-wideband echo signal.

[0147] It is understandable that due to the periodic movement of the lungs and heart, the chest cavity will also expand and contract periodically. The target ultra-wideband echo signal carries the original distance information and vital signs information such as breathing and heartbeat. Therefore, the vital signs information of the target user can be determined based on the target ultra-wideband echo signal.

[0148] In some embodiments, the above step S2130 may include: filtering the target ultra-wideband echo signal to obtain a vital sign signal of the target user; and determining vital sign information of the target user according to the vital sign signal.

[0149] In the embodiment of the present application, the vital sign signal may include a breathing signal and a heartbeat signal. Filtering the target ultra-wideband echo signal is to filter the target ultra-wideband echo signal based on a set bandpass filter to obtain the breathing signal and heartbeat signal corresponding to the target user.

[0150] As a method, after determining the location of the target user, the ultra-wideband echo signal corresponding to the target user's location is found, and then the phase signal of the echo signal can be determined based on the target ultra-wideband echo signal, and then the phase signal can be filtered based on a pre-set bandpass filter to obtain a breathing signal and a heartbeat signal.

[0151] When determining the phase signal of the echo signal based on the target ultra-wideband echo signal, the phase signal corresponding to the target ultra-wideband echo signal can be calculated based on the distance between the ultra-wideband antenna and the target user and a preset calculation formula. The preset calculation formula is: in, is the phase corresponding to the target UWB echo signal, R(t) represents the distance between the UWB antenna and the target user, C represents the speed of light, and f c Indicates the transmit pulse frequency.

[0152] In some embodiments, in order to facilitate filtering processing, after extracting the phase signal of the target ultra-wideband echo signal, the phase signal can also be unwrapped to obtain the target phase signal, and then, the target phase signal is filtered based on a pre-set bandpass filter to obtain a breathing signal and a heartbeat signal.

[0153] In an embodiment of the present application, since the frequency ranges corresponding to the heartbeat signal and the respiratory signal are different, different bandpass filters can be set for the heartbeat signal and the respiratory signal, respectively. Among them, an IIR filter can be used to filter out interference signals. The IIR filter has a low order and a small amount of calculation. Commonly used IIR filters mainly include Butterworth filters, Chebyshev filters, elliptical filters, etc. In an embodiment of the present application, a Chebyshev bandpass filter of 0.1Hz to 0.6Hz is used to filter the above-obtained phase signal to obtain a respiratory signal; the above-obtained phase signal is filtered by a bandpass filter of 0.7Hz to 2.5Hz to obtain a heartbeat signal.

[0154] In some embodiments, determining the vital sign information of the target user based on the vital sign signal may include: performing a fast Fourier transform on the vital sign signal to obtain corresponding spectrum information; and using the maximum value of the spectrum peak in the spectrum information as the vital sign information of the target user.

[0155] In the embodiment of the present application, performing fast Fourier transform on the vital sign signal may refer to performing fast Fourier transform on the heartbeat signal and the respiratory signal respectively to obtain a heartbeat spectrum corresponding to the heartbeat signal and a respiratory spectrum corresponding to the respiratory signal.

[0156] In the embodiment of the present application, based on the breathing spectrum, the breathing frequency of the target user is obtained by searching for the maximum value of the spectrum peak; based on the heartbeat spectrum, the heartbeat frequency of the target user is obtained by searching for the maximum value of the spectrum peak.

[0157] It should be noted that after obtaining the vital sign signal in the aforementioned manner, in addition to using the fast Fourier transform algorithm to obtain the corresponding spectrum information, the signal can also be decomposed by using algorithms such as multiple signal classification (MUSIC) algorithm or empirical mode decomposition (EMD) / variational mode decomposition (VMD) to obtain the vital sign information of the target user. This application does not impose any specific restrictions on this.

[0158] In an embodiment of the present application, the vital sign information may include information such as the heart rate and breathing rate of the target user, and may also include information such as the voice, temperature, blood pressure, etc. of the target user, which is not specifically limited here. Among them, breathing is the activity of the respiratory tract and lungs. According to statistics from medical institutions, a normal adult breathes about 12-20 times per minute in a calm state. Newborns have the highest respiratory rate, which is 40-44 times per minute. The respiratory rate of the elderly will decrease, and the respiratory rate of athletes is also slightly lower than that of ordinary people. In general, the normal respiratory rate of the human body is 0.2Hz to 0.5Hz.

[0159] Heart rate refers to the number of heartbeats per minute in a normal person's quiet state, also called resting heart rate, which is generally 60-100 beats / minute, and may vary from person to person due to age, gender or other physiological factors. Generally speaking, the younger the age, the faster the heart rate. The heart rate of the elderly is slower than that of the young, and the heart rate of women is faster than that of men of the same age. These are all normal physiological phenomena. In general, in a quiet state, the heart rate of a normal adult is 0.8Hz to 2.0Hz. In a healthy adult's calm state, the chest fluctuation amplitude during breathing is 0cm-3cm, and the chest fluctuation amplitude caused by the heartbeat is 1.5mm-3.5mm.

[0160] After obtaining the breathing signal and the heartbeat signal in the above manner, the breathing signal and the heartbeat signal can be subjected to a fast Fourier transform to convert the breathing signal and the heartbeat signal into frequency domain signals, and the spectrum information corresponding to the breathing signal and the spectrum information corresponding to the heartbeat signal can be obtained. Then, the breathing frequency and the heartbeat frequency of the target user can be obtained based on the obtained spectrum information.

[0161] In some embodiments, Fig. 22 As shown, the above step S2120 may include steps: S2210 to S2230.

[0162] In step S2210, a clutter removal process is performed on the multiple groups of ultra-wideband echo signals to obtain multiple groups of ultra-wideband echo signals from which clutter is removed.

[0163] Performing clutter filtering on multiple sets of ultra-wideband echo signals may refer to eliminating static clutter in multiple sets of ultra-wideband echo signals. The static clutter is background clutter reflected by stationary objects. Stationary objects are stationary objects in the scene, that is, objects without life signs.

[0164] As an example, multiple groups of ultra-wideband echo signals are subjected to clutter filtering to obtain multiple groups of ultra-wideband echo signals with clutter filtered out, including: obtaining an average value of the multiple groups of ultra-wideband echo signals in a slow time domain; and performing clutter filtering on the multiple groups of ultra-wideband echo signals based on the average value to obtain multiple groups of ultra-wideband echo signals with clutter filtered out.

[0165] In the embodiment of the present application, since the clutter is unknown, the idea of ​​minimum mean square error can be used to estimate the clutter signal from the received echo signal. The multiple groups of ultra-wideband echo signals are averaged in the slow time domain to obtain the signal average value in the slow time domain.

[0166] After calculating the signal average, subtract the signal average from multiple groups of ultra-wideband echo signals to obtain multiple ultra-wideband echo signals with static clutter filtered out. That is, the average value of R(m, n) is calculated in the slow time domain to obtain the signal average, and then the signal average is subtracted from R(m, n) to obtain R'(m, n).

[0167] It should be noted that in the application embodiment, the signal sampling frequency can be set to Fs (GHz), the repetition time interval of the transmitted continuous pulse sequence can be set to RFRI (s), and the corresponding continuous pulse sequence repetition frequency can be set to 1 / RFRI, and multiple groups of continuous echo signals containing vital signs signals can be received, and the received echo signals of each pulse sequence can be arranged in rows to form an echo signal matrix R (m, n), where m represents the mth pulse, corresponding to the slow time domain, and n represents the nth Tap, corresponding to the fast time domain. M and n can also represent the number of samples of the same pulse wave and the number of pulses in the continuous periodic pulse sequence, respectively.

[0168] Among them, the fast time domain and the slow time domain can be two time dimensions, namely the fast time dimension and the slow time dimension. For a pulse radar, which transmits a periodic pulse sequence, we store the received echo of each pulse sequence by row, for example, the received echo of the first pulse is placed in the first row, and the received echo of the second pulse is placed in the second row, and so on. This storage method lays a good foundation model for understanding the signal processing process. Therefore, we define the dimension viewed in the row direction as the fast time dimension. In addition, since the data sampling interval between rows is often greater than the pulse duration, the dimension viewed in the column direction is defined as the slow time dimension.

[0169] In step S2220, the location of the target user is determined based on the multiple groups of ultra-wideband echo signals from which clutter is filtered out.

[0170] After obtaining multiple groups of ultra-wideband echo signals after filtering out clutter, the multiple groups of ultra-wideband echo information after filtering out clutter are analyzed to determine the location of the target user. When analyzing the multiple groups of ultra-wideband echo signals after filtering out clutter to determine the location of the target user, the location of the target user can be found in the frequency domain or the time domain.

[0171] As a method, the corresponding spectrum signal can be obtained by performing fast Fourier transform on multiple groups of ultra-wideband echo signals with clutter filtered out in the slow time domain, and then the point with the largest energy can be found in the spectrum signal, that is, the location of the target user can be found in the frequency domain. Based on the multiple groups of ultra-wideband echo signals with clutter filtered out, the location of the target user is determined, including: performing fast Fourier transform on multiple groups of ultra-wideband echo signals with clutter filtered out in the slow time domain to obtain corresponding multiple groups of frequency domain signals; determining the first position of the target user from the multiple groups of frequency domain signals. The first position is the position of the target user determined in the frequency domain.

[0172] As another method, variance analysis can be performed in the slow time domain on multiple groups of ultra-wideband echo signals that have been filtered out of clutter. Since the echo signal reflected by the target user fluctuates relatively greatly, the greater the fluctuation, the greater the variance. Then, the point with the largest variance can be found by variance analysis, that is, the location of the target user can be found in the time domain. Based on multiple groups of ultra-wideband echo signals that have been filtered out of clutter, the location of the target user is determined, including: obtaining multiple variances corresponding to the multiple groups of ultra-wideband echo signals that have been filtered out of clutter in the slow time domain; based on the multiple variances, determining the second location of the target user. The second location is the location of the target user determined in the time domain.

[0173] Before determining the multiple variances corresponding to the multiple groups of ultra-wideband echo signals from which clutter is filtered out in the slow time domain, the average values ​​corresponding to the multiple groups of ultra-wideband echo signals from which clutter is filtered out in the slow time domain are first calculated, and then based on the multiple groups of ultra-wideband echo signals from which clutter is filtered out and the average values, the multiple variances corresponding to the multiple groups of ultra-wideband echo signals from which clutter is filtered out in the slow time domain are calculated.

[0174] In step S2230, a target ultra-wideband echo signal in the slow time domain corresponding to the position of the target user is obtained from the multiple groups of ultra-wideband echo signals from which clutter is filtered out.

[0175] In the embodiment of the present application, after the location of the target user is determined in the above manner, an ultra-wideband echo signal in the slow time domain corresponding to the location of the target user can be obtained.

[0176] Since there are many ways to determine the location of the target user, the following two situations may occur: first, the location of the target user determined in the frequency domain is the same as the location of the target user determined in the time domain; second, the location of the target user determined in the frequency domain is different from the location of the target user determined in the time domain.

[0177] As a method, when the position of the target user determined in the frequency domain is the same as the position of the target user determined in the time domain, a target ultra-wideband echo signal in the slow time domain corresponding to the position of the target user is obtained from multiple groups of ultra-wideband echo signals from which clutter is filtered out, including: if the first position is the same as the second position, a target ultra-wideband echo signal in the slow time domain corresponding to the first position or the second position is obtained from multiple groups of ultra-wideband echo signals from which clutter is filtered out.

[0178] The first position of the target user determined in the frequency domain is Tap M, and the second position of the target user determined in the time domain is Tap N. When M≈N, if the sampling frequency Fs of the terminal device is 1 GHz, then its sampling period Tap is 1 / Fs(s), then 1 / Fs=10 -9 (s). Furthermore, the distance between the UWB antenna and the target user can be calculated Where, t = N*T ap , N is the location of the target user, and C is the speed of light, which is approximately equal to 3*10 8 The acquired target ultra-wideband echo signal in the slow time domain corresponding to the first position or the second position is R'(:, N).

[0179] As another way, when the position of the target user determined in the frequency domain is different from the position of the target user determined in the time domain, a target ultra-wideband echo signal in the slow time domain corresponding to the position of the target user is obtained from multiple groups of ultra-wideband echo signals from which clutter is filtered out, including: if the first position and the second position are different, determining the position range of the target user based on the first position and the second position; obtaining the target ultra-wideband echo signal in the slow time domain corresponding to the position included in the position range from the multiple groups of ultra-wideband echo signals from which clutter is filtered out; performing a fast Fourier transform on the target ultra-wideband echo signal in the slow time domain corresponding to the position included in the position range to obtain a corresponding plurality of spectrum signals; determining the target position of the target user based on the plurality of spectrum signals; obtaining the target ultra-wideband echo signal in the slow time domain corresponding to the target position from the multiple groups of ultra-wideband echo signals from which clutter is filtered out.

[0180] When the position of the target user determined in the frequency domain is different from the position of the target user determined in the time domain, that is, the first position is different from the second position, a position range is determined based on the first position and the second position, and then the position of the target user is determined from the position range. When determining the position range based on the first position and the second position, the size of the first position and the second position can be first determined, and the larger position of the first position and the second position is added with a preset distance to obtain a first reference position, and the smaller position of the first position and the second position is subtracted from the preset distance to obtain a second reference position, and then the position between the second reference position and the first reference position is determined as the position range, wherein the preset distance includes a plurality of preset positions.

[0181] Exemplarily, if the first position of the target user determined in the frequency domain is Tap M, and the second position of the target user determined in the time domain is Tap N, when M≠N, since the target user's weak movement usually spans the distance of L Taps, the determined position range is min(M, N)-L to max(M, N)+L. The target ultra-wideband echo signal in the slow time domain corresponding to the different TAP positions included in the position range is obtained as R'(:, min(M, N)-L:max(M, N)+L). For example, if L is set to 2, the first position of the target user determined in the frequency domain is Tap 5, and the second position of the target user determined in the time domain is Tap 7, then the determined position range is 5-2 to 7+2, that is, including positions 3, 4, 5, 6, 7, 8, 9, etc., and then the target ultra-wideband echo signals in the slow time domain corresponding to the positions included in the position range are the ultra-wideband echo signals in the slow time domain corresponding to the positions 3, 4, 5, 6, 7, 8, 9, etc.

[0182] Since the target user's breathing frequency in a calm state is 0.2Hz to 0.5Hz, and the heart rate is 0.9Hz to 2Hz, it is necessary to find the signal with the largest spectrum energy of breathing and heartbeat as the final measurement signal. In order to find the exact position of the target user in the position range, the ultra-wideband echo signal corresponding to the different positions obtained above can be fast Fourier transformed to obtain the corresponding multiple spectrum information, and then the position with the highest proportion of spectrum energy in the range of 0.2Hz to 2Hz is obtained from the determined multiple spectrum information, that is, the position of the target user is determined.

[0183] After the location of the target user is determined, an ultra-wideband echo signal corresponding to the location is obtained from multiple groups of ultra-wideband echo signals from which clutter is filtered out.

[0184] In order to deepen the understanding of the vital sign detection method based on ultra-wideband echo data, the following Fig.23 A more detailed example is given below. Fig.23As shown, the vital sign detection method based on ultra-wideband echo data may include the following steps: S2310-S2380.

[0185] Step S2310: Receive ultra-wideband echo data through the ultra-wideband antenna. The ultra-wideband radar echo channel impulse response (CIR) data acquisition process includes the analysis of the in-phase quadrature (IQ) signal to obtain the real and imaginary parts of the complex signal. The CIR data of different pulses are reorganized to obtain a two-dimensional data matrix R (m, n), where m represents the mth pulse, corresponding to the slow time domain, and n represents the nth Tap, representing the fast time domain.

[0186] Step S2320: Perform clutter filtering on the received ultra-wideband echo data to eliminate static clutter (unchanged in the slow time domain). Since the clutter is unknown, the clutter signal is estimated from the received signal using the idea of ​​minimum mean square error. R(m, n) is averaged in the slow time domain, and the signal is subtracted from the original data to obtain the echo data R'(m, n) with static clutter filtered out.

[0187] Step S2330: Perform fast Fourier transform (FFT) on the echo data in the slow time domain, or perform variance analysis in the slow time domain (since the echo signal reflected by the human body fluctuates relatively greatly, the greater the fluctuation, the greater the variance), select the point with the largest energy, that is, find the location of the human body in the frequency domain or time domain, corresponding to Tap M and Tap N. In general, M≈N, and the corresponding distance of the person is 15×N (cm). When M≠N, since the weak movement of the human body usually spans the distance of several Taps, take L=2. Next, we process the slow time data corresponding to the TAP between min(M, N)-L and max(M, N)+L.

[0188] Furthermore, the echo data R'(:, min(M, N)-L: max(M, N)+L) in the slow time domain at the above different TAPs are taken to obtain the phase φ of the echo data, and then the phase is unwrapped. Using the relationship between phase and distance It can describe the movement of the chest cavity. The next step is to find the exact TAP position. Since the breathing frequency of the human body in a calm state is 0.2-0.5Hz and the heart rate is 0.9-2Hz, we need to find the signal with the largest spectrum energy of breathing and heart rate as the final measurement signal. By performing FFT on the phase at different TAPs, we can obtain the spectrum information, and then find the TAP with the highest spectrum energy in the range of 0.2-2Hz, so that we can find the position of the human body.

[0189] Step S2340: Bandpass filter the echo data. In order to filter out the interference of signals in other frequency bands, corresponding bandpass filters are designed to filter out the corresponding breathing signals and heartbeat signals. IIR filters have low order and small computational complexity, and are usually used to filter out interference signals. Commonly used IIR filters include Butterworth filters, Chebyshev filters, and elliptic filters. Here we use a Chebyshev bandpass filter of 0.1-0.6 Hz to obtain breathing data, and a bandpass filter of 0.7-2.5 Hz to obtain heartbeat data.

[0190] Step S2350: Perform FFT on the filtered respiratory data to obtain corresponding frequency spectrum information.

[0191] Step S2360: Obtain the respiratory rate by searching for the maximum value of the spectrum peak.

[0192] Step S2370: Perform FFT on the filtered heartbeat data to obtain corresponding frequency spectrum information.

[0193] Step S2380: Obtain the heart rate by searching for the maximum value of the spectrum peak.

[0194] In some embodiments, the terminal device has four sides other than 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; the terminal device includes two ultra-wideband antennas (a first UWB antenna and a second UWB antenna), then the two ultra-wideband antennas are respectively distributed on any long side and on the top side, and the phase spacing between the two ultra-wideband antennas is greater than a preset distance.

[0195] In some embodiments, the "Wi-Fi antenna + UWB antenna" in the terminal device may be an independent radiator architecture. That is, the "Wi-Fi antenna + UWB antenna" may correspond to different antenna radiators. The terminal device may include two Wi-Fi antennas and two ultra-wideband (UWB) antennas, the two Wi-Fi antennas may be a first Wi-Fi antenna and a second Wi-Fi antenna, and the two UWB antennas may be a first UWB antenna and a second UWB antenna. Among them, the first Wi-Fi antenna may be arranged on a long side of the terminal device, the second Wi-Fi antenna is arranged on the top side of the terminal device, the first UWB antenna may be arranged on a long side of the terminal device, and the second UWB antenna is arranged on the top side of the terminal device. Of course, the first Wi-Fi antenna and the first UWB antenna may be arranged on the same long side of the terminal device, or on different long sides of the terminal device. The phase spacing between the first Wi-Fi antenna and the second Wi-Fi antenna is greater than the first preset distance, and the phase spacing between the first UWB antenna and the second UWB antenna is greater than the second preset distance. The first preset distance and the second preset distance may be the same or different. The first preset distance and the second preset distance can be set according to requirements. For example, the first preset distance and the second preset distance can be set to be greater than 30 mm.

[0196] In some other embodiments, the "Wi-Fi antenna + UWB antenna" in the terminal device may be a co-radiator structure, where the Wi-Fi antenna and the UWB antenna reuse the same antenna radiator. The terminal device may include a first antenna and a second antenna. The first antenna may be a co-radiator antenna of the first Wi-Fi antenna and the first UWB antenna mentioned above, and the second antenna may be a co-radiator antenna of the second Wi-Fi antenna and the second UWB antenna mentioned above. Fig.24 As shown, the first antenna 2410 may be arranged on a long side of the terminal device 2400, and the second antenna 2420 may be arranged on the top side of the terminal device 2400. For example, the first antenna 2410 may be arranged in the middle of the left long side, and the second antenna 2420 may be arranged in the middle of the upper top side. The phase spacing between the first antenna 2410 and the second antenna 2420 is greater than the third preset distance. The third preset distance may be set as required, for example, the third preset distance may be set to be greater than 30 mm.

[0197] In some embodiments, when the "Wi-Fi antenna + UWB antenna" is a co-radiator structure, see Fig.25, a combiner 2510 can be added to the front end of the RF path, that is, by setting the combiner 2510, the two RF paths of the Wi-Fi chip and the UWB chip can share the antenna 2520 to reduce the cost of the terminal device. A matching circuit 2530 can also be included between the combiner 2510 and the antenna 2520. The matching circuit 2530 can be used to tune the matching impedance of the antenna 2520 so that the antenna 2520 can support the transmission and reception of Wi-Fi frequency band signals and UWB frequency band signals at the same time. Among them, the antenna 2520 is a co-radiator antenna of "Wi-Fi antenna + UWB antenna".

[0198] In other embodiments, when the "Wi-Fi antenna + UWB antenna" is a co-radiator structure, see Fig.26 , two matching circuits, namely matching circuit 2610 and matching circuit 2620, may also be provided in the radio frequency path, and the matching impedance of antenna 2630 may be tuned by matching circuit 2610 and matching circuit 2620, so that antenna 2630 may support the transmission and reception of Wi-Fi band signals and UWB band signals at the same time. Among them, antenna 2630 is a co-radiator antenna of "Wi-Fi antenna + UWB antenna".

[0199] It should be noted that when the "Wi-Fi antenna + UWB antenna" is an independent radiator structure, it can also be reused. Fig.26 The antenna 2630 may include an independent Wi-Fi antenna and a UWB antenna. As an example, see again Fig.26 , the matching circuit 2610 can be connected to the Wi-Fi antenna in the antenna 2630, and the matching circuit 2620 can be connected to the UWB antenna in the antenna 2630. The matching circuit 2610 can be used to tune the matching impedance of the Wi-Fi antenna, and the matching circuit 2620 can be used to tune the matching impedance of the UWB antenna 2640.

[0200] Combination of the above Figures 1 to 26 The method embodiment of the present application is described in detail. Figure 27 to Figure 28 The device embodiment of the present application is described in detail. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment, so the parts not described in detail can refer to the previous method embodiment.

[0201] Fig. 27 The structure diagram of the vital sign detection device provided in one embodiment of the present application is as follows: The vital sign detection device 2700 can be applied to a terminal device, and the vital sign detection device 2700 can include a determination module 2710 and a detection module 2720 .

[0202] The determination module 2710 is used to determine that the distance between the terminal device and the target user is a first distance.

[0203] The detection module 2720 is configured to detect the vital sign information of the target user based on the channel state information of the Wi-Fi signal if the first distance is less than the preset distance; and detect the vital sign information of the target user based on the ultra-wideband echo data if the first distance is not less than the preset distance.

[0204] Optionally, the determination module 2710 is used to: measure the distance between the terminal device and the target user using ultra-wideband technology to obtain the first distance.

[0205] Optionally, the detection module 2720 is used to: obtain channel state information of the Wi-Fi signal propagated in a preset space, where the channel state information of the Wi-Fi signal 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 by using a frequency range of human activity to obtain second filtered data of the target subcarrier; and determine the vital sign information of the target user according to peak information of the second filtered data in the frequency domain.

[0206] Optionally, determining the target subcarrier from the multiple subcarriers based on the energy values ​​of the subcarrier data of the multiple subcarriers in the frequency domain includes: transforming the subcarrier data of the multiple subcarriers to 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 the subcarriers with forward energy values ​​as the target subcarriers.

[0207] Optionally, the filtering module 2730 is used to filter the subcarrier data of the multiple subcarriers using a basic frequency range before transforming the subcarrier data of the multiple subcarriers to the frequency domain to obtain first filtered data of the multiple subcarriers; transforming the subcarrier data of the multiple subcarriers to the frequency domain includes: transforming the first filtered data of the multiple subcarriers to the frequency domain.

[0208] Optionally, the transforming of 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 the first preset number of subcarriers with the leading energy values ​​as the target subcarriers includes: transforming the subcarrier data of the multiple subcarriers into the frequency domain to obtain a first power spectrum density of each subcarrier in the frequency domain; normalizing the first power spectrum density to obtain a normalized first power spectrum density; sorting the largest normalized first power spectrum density under each subcarrier in order from high to low, and selecting the first preset number of subcarriers with the leading normalized first power spectrum density as the target subcarriers.

[0209] Optionally, determining the vital sign information of the target user based on the peak information of the second filtered data in the frequency domain includes: determining the human activity frequency and / or human activity energy value of the target user based on the peak information of the second filtered data in the frequency domain; determining the vital sign information of the target user based on the human activity frequency and / or the human activity energy value.

[0210] Optionally, determining the human activity frequency and / or human activity energy value of the target user based on the peak information of the second filtered data in the frequency domain includes: transforming the second filtered data to the frequency domain, and determining the main peak of the second filtered data in the frequency domain; determining the human activity frequency according to the frequency of the main peak, and / or determining the human activity energy value according to the energy value of the main peak.

[0211] Optionally, the channel state information of the Wi-Fi signal includes: channel state information of the Wi-Fi 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 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 spectrum density to obtain a normalized second power spectrum density; for each link, performing a peak search on the normalized second power spectrum density in the frequency domain, and determining a second preset number of peaks before the normalized second power spectrum density is sorted from high to low; and taking a peak of the normalized second power spectrum density that exceeds a preset energy threshold as the main peak.

[0212] Optionally, the terminal device includes multiple Wi-Fi antennas, each Wi-Fi antenna forms a link; the terminal device has four sides except a display plane and a 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 Wi-Fi antennas, the two Wi-Fi antennas are respectively distributed on any one of the long sides and the top side, and the phase spacing of the two Wi-Fi antennas is greater than a preset distance.

[0213] Optionally, the detection module 2720 is used to: receive the ultra-wideband echo data through an ultra-wideband antenna, the ultra-wideband echo data including multiple groups of continuous ultra-wideband echo signals; determine the target ultra-wideband echo signal corresponding to the location of the target user from the multiple groups of ultra-wideband echo signals; and determine the vital sign information of the target user based on the target ultra-wideband echo signal.

[0214] Optionally, determining the target ultra-wideband echo signal corresponding to the position of the target user from the multiple groups of ultra-wideband echo signals includes: performing clutter filtering on the multiple groups of ultra-wideband echo signals to obtain multiple groups of ultra-wideband echo signals from which clutter is filtered out; determining the position of the target user based on the multiple groups of ultra-wideband echo signals from which clutter is filtered out; and obtaining the target ultra-wideband echo signal in the slow time domain corresponding to the position of the target user from the multiple groups of ultra-wideband echo signals from which clutter is filtered out.

[0215] Optionally, determining the position of the target user based on the multiple groups of ultra-wideband echo signals from which clutter is filtered out includes: performing fast Fourier transform on the multiple groups of ultra-wideband echo signals from which clutter is filtered out in a slow time domain to obtain multiple groups of corresponding frequency domain signals; determining the first position of the target user from the multiple groups of frequency domain signals; or performing variance analysis on the multiple groups of ultra-wideband echo signals from which clutter is filtered out in a slow time domain to obtain multiple variances; and determining the second position of the target user based on the multiple variances.

[0216] Optionally, obtaining a target ultra-wideband echo signal in the slow time domain corresponding to the position of the target user from the multiple groups of ultra-wideband echo signals from which clutter is filtered out includes: if the first position is the same as the second position, obtaining the target ultra-wideband echo signal in the slow time domain corresponding to the first position or the second position from the multiple groups of ultra-wideband echo signals from which clutter is filtered out.

[0217] Optionally, the device 2700 also includes: an acquisition module 2740, which is used to determine the location range of the target user based on the first position and the second position if the first position is different; obtain the ultra-wideband echo signal in the slow time domain corresponding to the position included in the position range from the multiple groups of ultra-wideband echo signals from which clutter is filtered out; perform fast Fourier transform on the ultra-wideband echo signal in the slow time domain corresponding to the position included in the position range to obtain a corresponding multiple spectrum signals; determine the target position of the target user based on the multiple spectrum signals; and obtain the target ultra-wideband echo signal in the slow time domain corresponding to the target position from the multiple groups of ultra-wideband echo signals from which clutter is filtered out.

[0218] Optionally, determining the vital sign information of the target user according to the target ultra-wideband echo signal includes: filtering the target ultra-wideband echo signal to obtain the vital sign signal of the target user; and determining the vital sign information of the target user according to the vital sign signal.

[0219] Optionally, determining the vital sign information of the target user based on the vital sign signal includes: performing fast Fourier transform on the vital sign signal to obtain corresponding spectrum information; and taking a spectrum peak maximum value in the spectrum information as the vital sign information of the target user.

[0220] Optionally, the terminal device has four side edges except a display plane and a plane relative to the display plane, wherein the first side edge and the third side edge are long edges, the second side edge and the fourth side edge are short edges, and the second side edge and the fourth side edge are top edges and bottom edges, respectively; the terminal device includes two ultra-wideband antennas, and the two ultra-wideband antennas are respectively distributed on any one of the long edges and the top edge, and a phase spacing between the two ultra-wideband antennas is greater than a preset distance.

[0221] Optionally, the receiving the ultra-wideband echo data through an ultra-wideband antenna includes: receiving ultra-wideband echo data through the ultra-wideband circularly polarized antenna.

[0222] Combine the following Fig.28 This is an introduction to a terminal device 2800 in an embodiment of the present application. The terminal device 2800 can be used to implement the method described in the above method embodiment. It should be understood that Fig.28 The components in the dotted boxes are optional.

[0223] It should be understood that the terminal device 2800 can be applied to any type of terminal device mentioned above.

[0224] The terminal device 2800 may include one or more processors 2810. The processor 2810 may support the terminal device 2800 to implement the method described in the foregoing method embodiment.

[0225] The processor 2810 may be a general-purpose processor or a special-purpose processor. For example, the processor may be a central processing unit (CPU). Alternatively, the processor may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0226] The terminal device 2800 may further include one or more memories 2820. The memory 2820 stores a program, which can be executed by the processor 2810 to control the terminal device 2800 to execute the method described in the above method embodiment. The memory 2820 may be independent of the processor 2810 or integrated in the processor 2810.

[0227] The memory 2820 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the nonvolatile memory may be a read only memory (ROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, the memory 2820 may also be various types of random access memory (RAM), such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous-link DRAM (SLDRAM) and direct ram-bus RAM (DR RAM).

[0228] The terminal device 2800 may further include a transceiver 2830. The processor 2810 may communicate with other devices through the transceiver 2830. For example, the processor 2810 may transmit and receive data with other devices through the transceiver 2830.

[0229] The present application also provides a chip in an embodiment, including a processor, which can be used to call and run a computer program from a memory, so that a device equipped with the chip performs the method described in the above method embodiment. It is understood that the processor can be any type of processor mentioned above. It is understood that the memory can be independent of the chip or integrated in the chip.

[0230] The embodiment of the present application also provides a machine-readable storage medium for storing a program, and the program enables a computer to execute the method in each embodiment of the present application.

[0231] The embodiment of the present application also provides a computer program product. The computer program product includes a program. The program enables a computer to execute the method in each embodiment of the present application.

[0232] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a machine-readable storage medium, or transmitted from one machine-readable storage medium to another machine-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The machine-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that contains one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0233] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments of the present application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0234] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0235] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0236] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0237] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for detecting vital signs, characterized in that: Applied to a terminal device, the method comprises: Determining a first distance between the terminal device and a target user; If the first distance is less than a preset distance, detecting vital sign information of the target user based on channel state information of the Wi-Fi signal; If the first distance is not less than a preset distance, vital sign information of the target user is detected based on ultra-wideband echo data.

2. The method according to claim 1, characterized in that The determining a first distance between the terminal device and the target user includes: The distance between the terminal device and the target user is measured using ultra-wideband technology to obtain the first distance.

3. The method according to claim 1, characterized in that The detecting the vital sign information of the target user based on the channel state information of the Wi-Fi signal includes: Acquire channel state information of the Wi-Fi signal propagating in a preset space, where the channel state information of the Wi-Fi signal 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 target user is determined according to the peak information of the second filtered data in the frequency domain.

4. The method according to claim 3, 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: transforming subcarrier data of the plurality of subcarriers into a frequency domain; The energy values ​​of the multiple subcarriers in the frequency domain are sorted from high to low, and a first preset number of subcarriers with top energy values ​​are selected as the target subcarriers.

5. The method according to claim 4, 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.

6. The method according to claim 4, 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 the first preset number of subcarriers with the front normalized first power spectrum densities are selected as the target subcarriers.

7. The method according to claim 3, characterized in that The determining the vital sign information of the target user according to the peak information of the second filtered data in the frequency domain includes: Determining the human activity frequency and / or human activity energy value of the target user according to the peak information of the second filtered data in the frequency domain; The vital sign information of the target user is determined according to the human activity frequency and / or the human activity energy value.

8. The method according to claim 7, characterized in that The determining the human activity frequency and / or human activity energy value of the target user according to the peak information of the second filtered data in the frequency domain includes: transforming the second filtered data 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.

9. The method according to claim 8, characterized in that The channel state information of the Wi-Fi signal includes: channel state information of the Wi-Fi signal received by multiple links of the terminal device; the second filtered data includes second filtered data corresponding to the multiple links; and the transforming the second filtered data 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, performing a peak search 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; The peak of the normalized second power spectrum density exceeding a preset energy threshold is taken as the main peak.

10. The method according to claim 1, characterized in that The detecting the vital sign information of the target user based on the ultra-wideband echo data includes: Receiving the ultra-wideband echo data through an ultra-wideband antenna, wherein the ultra-wideband echo data includes a plurality of continuous groups of ultra-wideband echo signals; Determining a target ultra-wideband echo signal corresponding to the position of the target user from the multiple groups of ultra-wideband echo signals; The vital sign information of the target user is determined according to the target ultra-wideband echo signal.

11. The method according to claim 10, characterized in that The step of determining, from the multiple groups of ultra-wideband echo signals, a target ultra-wideband echo signal corresponding to the position of the target user comprises: Performing clutter filtering on the multiple groups of ultra-wideband echo signals to obtain multiple groups of ultra-wideband echo signals with clutter filtered out; Determining the location of the target user based on the multiple groups of ultra-wideband echo signals from which clutter is filtered out; The target ultra-wideband echo signal in the slow time domain corresponding to the position of the target user is obtained from the multiple groups of ultra-wideband echo signals after clutter is filtered out.

12. The method according to claim 11, characterized in that The determining the location of the target user based on the multiple groups of ultra-wideband echo signals after filtering out clutter includes: Performing fast Fourier transform on the multiple groups of ultra-wideband echo signals after clutter is filtered out in the slow time domain to obtain corresponding multiple groups of frequency domain signals; Determine a first position of the target user from the multiple groups of frequency domain signals; or Performing variance analysis on the plurality of groups of ultra-wideband echo signals from which clutter is filtered out in a slow time domain to obtain a plurality of variances; A second location of the target user is determined according to the multiple variances.

13. The method according to claim 12, characterized in that The step of obtaining a target ultra-wideband echo signal in a slow time domain corresponding to the position of the target user from the plurality of groups of ultra-wideband echo signals from which clutter is filtered out comprises: If the first position is the same as the second position, the target ultra-wideband echo signal in the slow time domain corresponding to the first position or the second position is obtained from the multiple groups of ultra-wideband echo signals from which clutter is filtered out.

14. The method according to claim 12, characterized in that The method further comprises: If the first location and the second location are different, determining a location range of the target user based on the first location and the second location; Acquire, from the plurality of groups of ultra-wideband echo signals from which clutter has been filtered, ultra-wideband echo signals in a slow time domain corresponding to positions included in the position range; Performing a fast Fourier transform on the ultra-wideband echo signal in the slow time domain corresponding to the positions included in the position range to obtain a corresponding plurality of spectrum signals; Determining a target position of the target user based on the multiple spectrum signals; The target ultra-wideband echo signal in the slow time domain corresponding to the target position is obtained from the multiple groups of ultra-wideband echo signals from which clutter is filtered out.

15. The method according to claim 10, characterized in that The determining the vital sign information of the target user according to the target ultra-wideband echo signal includes: Filtering the target ultra-wideband echo signal to obtain a vital sign signal of the target user; The vital sign information of the target user is determined according to the vital sign signal.

16. The method according to claim 15, characterized in that The determining the vital sign information of the target user according to the vital sign signal includes: Performing a fast Fourier transform on the vital sign signal to obtain corresponding frequency spectrum information; The maximum value of the spectrum peak in the spectrum information is used as the vital sign information of the target user.

17. The method according to claim 10, characterized in that The receiving the ultra-wideband echo data through the ultra-wideband antenna includes: Ultra-wideband echo data is received via the ultra-wideband circularly polarized antenna.

18. The method according to claim 1, characterized in that 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; The terminal device includes two Wi-Fi antennas and two ultra-wideband antennas, the two Wi-Fi antennas include a first Wi-Fi antenna and a second Wi-Fi antenna, and the two ultra-wideband antennas include a first UWB antenna and a second UWB antenna; When the two Wi-Fi antennas and the two ultra-wideband antennas are independent radiator structures, the first Wi-Fi antenna is arranged on a long side of the terminal device, the second Wi-Fi antenna is arranged on the top side, the first UWB antenna is arranged on a long side of the terminal device, the second UWB antenna is arranged on the top side, the phase spacing between the first Wi-Fi antenna and the second Wi-Fi antenna is greater than a first preset distance, and the phase spacing between the first UWB antenna and the second UWB antenna is greater than a second preset distance; When the two Wi-Fi antennas and the two ultra-wideband antennas are a co-radiator architecture, the terminal device includes a first antenna and a second antenna, the first antenna is a co-radiator antenna of the first Wi-Fi antenna and the first UWB antenna, the second antenna is a co-radiator antenna of the second Wi-Fi antenna and the second UWB antenna, the first antenna is arranged on a long side of the terminal device, the second antenna is arranged on the top side, and the phase spacing between the first antenna and the second antenna is greater than a third preset distance.

19. The method according to any one of claims 1 to 18, characterized in that The vital sign information includes the heart rate and breathing rate of the target user.

20. A vital sign detection device, characterized in that: The device is applied to a terminal device, and the device includes: A determination module, configured to determine that the distance between the terminal device and the target user is a first distance; A detection module is configured to detect the vital sign information of the target user based on the channel state information of the Wi-Fi signal if the first distance is less than the preset distance; and to detect the vital sign information of the target user based on the ultra-wideband echo data if the first distance is not less than the preset distance.

21. A terminal device, characterized in that: include: A processor and a memory, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to control the terminal device to execute the method as described in any one of claims 1-19.

22. A computer-readable storage medium having executable code stored thereon, characterized in that: The code is used to implement the method according to any one of claims 1 to 19.

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