A method for monitoring respiration of a human body by moving an RFID device

Abstract

The application discloses a method for monitoring human respiration by a mobile RFID device, comprising the following steps: setting a target tag and a reference tag, collecting tag signal data by a mobile receiver; the target tag is used for monitoring micro-movement caused by respiration of a subject, the reference tag is a static tag, and receiver movement information is acquired according to phase and RSSI signal data provided by the reference tag; signal separation and screening are performed on signal data of the target tag and the reference tag, segmenting is performed on reference tag signal data based on direction segmentation of RSSI; phase unfolding is performed on the separated and screened reference tag and target tag signal data, and signal data is denoised by wavelet domain denoising; and the preprocessed signal data is used to eliminate movement information in the target tag according to receiver movement information acquired by the reference tag, normalize the waveform and obtain human respiration monitoring data. The application can realize human respiration monitoring in a device movement state.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of wireless sensing technology, and particularly relates to a method for monitoring human respiration by moving RFID equipment. BACKGROUND

[0002] In recent years, with the continuous development of wireless sensing technology, people pay more and more attention to its broad application prospect in human physiological feature recognition. The signals commonly used in wireless sensing technology include Wi-Fi, LoRa, RFID, millimeter wave, UWB, acoustic signal and ultrasonic wave, etc. Using these signals can identify human physiological features in coarse granularity and fine granularity. Coarse granularity applications include activity recognition, positioning, gait recognition and posture recognition, etc. Fine granularity applications include respiration monitoring, gesture recognition and vibration sensing, etc.

[0003] Although wireless sensing technology has made significant progress in various aspects, there are still problems. In specific experiments, it is often required that the radio frequency equipment (i.e. the transmitter and receiver) remains stationary. When the equipment remains stationary, the change of the signal is caused only by the target movement, so the target information can be directly analyzed. However, once the equipment and the target move simultaneously, the signal change is caused not only by the target movement, but also by the movement of the equipment. In this case, it is difficult to separate the signal changes caused by the two, resulting in a decrease in sensing accuracy and even possible sensing failure. In actual applications, it is required that the transmitter and receiver remain stationary, which greatly limits its application scenarios in real life. For example, in a nursing home, if a robot equipped with sensors is designed to monitor the physiological features of the elderly in a wider range to ensure timely discovery and handling of emergencies, it is required that the sensors can also work normally during movement. SUMMARY

[0004] In order to overcome the shortcomings of the prior art, the application aims to provide a method for monitoring human respiration by moving RFID equipment, which solves the problem that the application scenarios are limited by the requirement that the transmitter and receiver remain stationary in the conventional technology by deploying tags on the monitoring object for physiological feature monitoring in a moving state.

[0005] The application is achieved by the following technical solutions:

[0006] A method for monitoring human respiration by moving RFID equipment, comprising the following steps:

[0007] Signal data collection: setting a target tag and a reference tag, collecting signal data of the target tag and the reference tag by a moving receiver; the target tag is used to monitor the micro-movement caused by the respiration of a subject, and the reference tag is a static tag, and the distance and direction information of the moving receiver is obtained according to the phase and RSSI signal data provided by the reference tag;

[0008] Signal data preprocessing: the signal data of the target tag and the reference tag are separated and filtered, the reference tag signal data is segmented based on RSSI direction segmentation; and the separated and filtered reference tag and target tag signal data are phase unfolded, and the signal data is denoised by wavelet domain denoising to obtain preprocessed signal data;

[0009] Target respiratory waveform recovery: using the preprocessed signal data, the distance and direction information of the receiver movement obtained by the reference tag is eliminated, the waveform is normalized and recovered to obtain human respiratory monitoring data.

[0010] Further, the receiver is connected to a circularly polarized antenna, and the receiver and the circularly polarized antenna are arranged on a movable device for realizing movement of the receiver; the movable device moves along different paths within the sensing range of the target tag and the reference tag; the target tag is arranged above the belly of the subject, and the reference tag is arranged on a static object around the subject.

[0011] Further, the signal data of the target tag and the reference tag can be represented as:

[0012]

[0013] In the formula, j is an imaginary unit, y r is the reference tag signal data, y t is the target tag signal data, f is the frequency of the receiver, v a,r is the speed of the path change introduced by the movement of the movable device relative to the reference tag, v d,t is the speed of the path change introduced by the movement of the movable device relative to the target tag.

[0014] Further, the RSSI is represented as:

[0015]

[0016] In the formula, G t represents the gain of the reference tag, P T represents the transmit power of the receiver, T b represents the transmit loss of the receiver, G r represents the antenna gain, and d represents the distance between the reference tag and the receiver.

[0017] Further, the target respiratory waveform recovery is specifically:

[0018] S1: for any time t1 and t2, the distance moved by the movable device is Δd, and the signal path length change of the target tag is Δdt = Δd sin θ t , the signal path length of the reference tag changes Δd r = Δd sin θ r , wherein θ t and θ r are the angles between the respective perpendicular lines and the moving direction of the device respectively;

[0019] S2: obtaining the signal phase Δφ of the target tag according to the distance of the moving device obtained in S1 t = 2πf 2Δd t , the signal phase of the reference tag is Δφ r = 2πf 2Δd r ;

[0020] S3: according to the signal phase obtained in S2, using the ratio of the signal phases of the target tag and the reference tag of the movable device to quantify the difference in angle, that is, the compensation coefficient ω:

[0021]

[0022] S4: multiplying the compensation coefficient obtained in S3 with the signal data of the reference tag, specifically:

[0023]

[0024] S5: dividing the signal data of the target tag and the reference tag, and combining the equation of S4, the following can be obtained:

[0025]

[0026] In the formula, A t is the amplitude of the target tag, and A r is the amplitude of the reference tag;

[0027] S6: using the least square method to linearly fit (ω-1) ∫v d,r (t), and subtracting the fitted value from the phase to restore the waveform;

[0028] S7: selecting a fixed time window to segment the target tag signal, and taking the position of the sign change of the RSSI slope as the boundary of segmentation, normalizing the signal in each time window to obtain the human respiratory monitoring data.

[0029] Further, the target tag is multiple, which is used to realize the micro-movement caused by the respiration of multiple subjects.

[0030] Compared with the prior art, the beneficial effects of the present application are:

[0031] (1) The present application obtains signal data by setting target tags and reference tags, and the phase and RSSI of the signal reflected by the static reference tags provide distance and direction information of device movement, so that the signal of the target tags can be extracted by using the distance and direction information of device movement, the interference introduced by device movement is separated out, and human respiration monitoring can be realized in the motion state of the receiver. Compared with the traditional static state sensing technology, the present application significantly expands the sensing range, is suitable for more extensive practical application scenarios, such as nursing homes, hospitals and other environments that need to be monitored in a large range, and thus enhances the practicality of the technology. By applying wavelet domain denoising technology and phase unwrapping algorithm, the present application effectively eliminates the noise interference and waveform winding problem in the RFID signal, and significantly improves the accuracy of respiration monitoring.

[0032] (2) The present application can monitor the respiration conditions of multiple targets at the same time by increasing the number of target tags, and realizes the multi-target monitoring capability. At the same time, it shows good robustness in different environments, such as corridors, laboratories, open halls and different postures, such as standing, half-squatting, lying on the side and sitting still, accurately restores the respiration waveform in various complex environments, and has strong adaptability. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 The method flowchart of the present application.

[0034] Figure 2 The phase diagram of the original signal received by the receiver of the present application.

[0035] Figure 3 The flowchart of the present application for eliminating phase jump of signal data.

[0036] Figure 4 The result diagram of the target tag signal after eliminating phase jump of the present application.

[0037] Figure 5 The change diagram of the target tag RSSI at different times of the present application.

[0038] Figure 6 The signal phase diagram of the reference tag and target tag data after preprocessing of the present application.

[0039] Figure 7 The respiration waveform result diagram of the subject of the present application.

[0040] Figure 8 The impinj R220 RFID receiver of the embodiment of the present application.

[0041] Figure 9 The movable device diagram of the embodiment of the present application.

[0042] Figure 10Respiratory waveform result figures of embodiments of the present application with reference tags set in different positions.

[0043] Figure 11 Respiratory waveform result figures of embodiments of the present application under different obstacle quantities.

[0044] Figure 12 Working figures of embodiments of the present application under different experimental environments.

[0045] Figure 13 Average error result figures of embodiments of the present application under different experimental environments.

[0046] Figure 14 Respiratory waveform result figures of embodiments of the present application under different postures.

[0047] Figure 15 Respiratory waveform result figures of embodiments of the present application when testing multiple subjects at the same time.

[0048] Figure 16 Respiratory waveform result figures of embodiments of the present application under complete assembly, without using reference tags, without using segmentation method and without eliminating linear influence. DETAILED DESCRIPTION

[0049] The present application will be further described below in conjunction with specific embodiments, which are explanations of the present application rather than limitations.

[0050] The present application monitors human physiological characteristics in a moving state by deploying tags on a monitoring object. However, noise interference and waveform winding may occur when the RFID signal is received. In addition, the additional interference introduced by the movement of the device also destroys the waveform of the breath. In order to solve these problems, wavelet domain denoising technology is used to remove noise, and a phase unwinding algorithm is proposed to correctly unwind the waveform that has been wound. In terms of interference elimination, a static tag is deployed next to the monitoring object to ensure that the tag only reflects the signal changes caused by the movement of the device. The direction of the device movement trajectory is analyzed from the signal of the tag and is segmented. Combined with the phase information in the signal, the interference of the device movement on the target signal is successfully eliminated. The present application uses the precise binding of RFID tags to the sensing target to test the respiratory conditions of multiple targets when the device is moving.

[0051] As shown in Figure 1 A method for monitoring human respiration by moving RFID devices, which consists of the following three parts.

[0052] (1) Data preprocessing: After receiving the signal data containing two tags, the signal of the two tags cannot be directly used. The method of the application first aligns and filters the signal of the two tags. Then, the system performs phase unwrapping to solve the phase jump problem and denoising in the wavelet domain to reduce noise in the signal.

[0053] (2) Directional segmentation of motion trajectory: During the movement of the carrier, actions such as turning will affect the accuracy of waveform recovery. Therefore, RSSI data is used to detect the turning position, and the data is segmented at the point where the data jumps, and each segment is processed separately.

[0054] (3) Target respiratory waveform recovery: After data preprocessing, the data still contains interference caused by device movement. Therefore, the system eliminates the interference in the target tag by referring to the device movement information perceived by the reference tag, eliminates the linear effect in the signal to solve the problem introduced by the position difference, and restores the respiratory waveform.

[0055] In the data preprocessing part, the RFID receiver collects data from two tags, and the data is arranged according to the timestamp of the signal arrival. However, the time of arrival of data from the two tags is irregular. Therefore, the length of the signal from the two tags is not the same, which makes the complete signal of the two tags cannot be directly used for elimination process. In addition, it is observed that the original signal contains a large jump, as shown in Figure 2 , that is, a dramatic change of π times an integer occurs at a certain position, which cannot be eliminated by simple unwrap operation. Based on the above problems, in the data preprocessing stage, the application separates the composite signal, separates and filters the signal of the two tags, so that the length of the signal of the two tags is equal and uniformly distributed, and in the subsequent motion elimination process, the two signals can be corresponded. Then, the phase jump is eliminated. Specifically, the value of a phase in the signal should be less than An algorithm for recovering the phase is designed, and the signal processing is completed, as shown in Figure 3 , the processed result is shown in Figure 4 . The signal without jump can also better utilize the effective information in the signal.

[0056] The direction segmentation of the motion trajectory is used when the device is used to detect the respiration, and it is found that when the device moves along a fixed direction, the waveform recovery using the data of the two tags received has a good effect, but when the device turns, the waveform recovery effect is affected. In order to solve the above problem, the application proposes to segment the data based on the RSSI direction segmentation, and then perform waveform recovery. In the radio frequency identification system, RSSI is a commonly used parameter for measuring the signal strength between the tag and the receiver, and is often used in various activity recognition. RSSI can be expressed as:

[0057]

[0058] G in the above formula t , P T , T b , G r , d are the tag gain, the receiver transmission power, the transmission loss, the antenna gain, the distance between the tag and the receiver antenna, 1 mw represents 1 milliwatt, which is used as a reference power to convert the transmission power PT into a ratio relative to 1 milliwatt.

[0059] In the experiment, it is found that when the device drives towards and away from the experimental personnel, the value of RSSI has monotonicity. When the monotonicity changes, it can be used to judge whether the device turns at this moment, and the special positions are recorded, such as Figure 5 In the subsequent recovery process, the application will subdivide the signal into many time periods, and the special positions where the turning may occur will be set as the segmentation positions and normalized.

[0060] The target respiration waveform recovery is directly received, and the result is shown in Figure 6 The signal reflected by the reference tag only contains the changes introduced by the device motion, and the signal reflected by the target tag contains not only the device motion but also the respiration motion. The target tag is extracted using the device motion information contained in the reference tag to obtain the clean respiration signal of the target tag position. But the changes introduced to the two tags are different due to the different positions of the two tags. Therefore, in order to better recover the respiration waveform of the monitored person, the application proposes a two-stage strategy to eliminate the influence of the device motion on the sensing signal. First, the signals received by the two tags can be expressed as:

[0061]

[0062] In the above two formulas, f is the frequency of the RFID receiver, v d,r ,v d,trespectively. Since the system uses two tags at different locations, the signal propagation path length between each tag and the device is different. The present invention focuses on the change of path length, for example, for two time instants t1 and t2 (t1 < t2), the distance the device antenna moves is Δd, it can be found that when the time difference is small enough, for each tag, the change of path length has a mathematical relationship with Δd. To illustrate this, project the signal path at time t1 onto the path at time t2, at this time, it can be considered that the length of the projected line segment is approximately the same as the line segment before projection, then the length of the line segment between the foot and the device is the path change of the signal in this time. The signal path length changes of the target tag and the reference tag are Δd t = Δd • sin θ t , Δd r = Δd • sin θ r , where θ t and θ r are the angles between the respective perpendicular lines and the direction of movement of the device. Since the two signal phases can be calculated as Δφ t = 2πf • 2Δd t , Δφ r = 2πf • 2Δd r , it can be concluded that the different positions of the tags result in different angles for each of the two tags, so the phase changes caused by the movement of the device to the two tags are also different, so the phase information at the reference tag cannot be directly used. However, the angles in the above equations cannot be directly obtained from the RFID device. The present invention further theoretically deduces the difference in angles of the two tags, and can quantify the difference in angles by calculating the ratio of the phases caused by the movement of the device at the two locations, that is, the compensation coefficient ω, which is used to eliminate the phase change of the signal caused by the movement of the device.

[0063]

[0064] If the compensation coefficient can be obtained, the device movement information at the reference tag can be converted to the position of the target tag. Considering the second stage, according to the movement model, the conversion of this position is realized by using the compensation coefficient. Considering that if the compensation coefficient is multiplied by the phase information obtained in the reference tag, the following equation is obtained:

[0065]

[0066] By verifying experiment, it is found that the value of compensation coefficient does not change much when the relative position of device and tag is unchanged, so the ω in the above formula can be extracted from the integral as a coefficient. Now the mathematical relationship between the influence of device motion on two tags is obtained, but the ω in the above formula is unknown. On this basis, the signals of the two tags are directly divided by each other, and the following derivation is made:

[0067]

[0068] In the formula, A t is the amplitude of the target tag, A r is the amplitude of the reference tag;

[0069] According to the above derivation, the two tags are directly divided, and the phase is extracted. There is still a redundant part in the result, that is, (ω-1)∫v d,r (t). This part introduces a linear effect, and the least square method is used to linearly fit it, and the fitted value is subtracted from the phase to preliminarily restore the waveform.

[0070] At this time, the interference caused by the motion of the device has been alleviated, but the positions of the peaks and troughs of the waveform still cannot be kept relatively level. It is speculated that this phenomenon is caused by the additional motion of the body of the subject during breathing, and if it is not further processed, the performance of the system will be significantly affected. A time window of 0.5 seconds is selected to segment the signal. When selecting the segmentation position, the slope change of RSSI in the previous module is also considered, and the position with sharp RSSI change, i.e. the sign change of the slope, is taken as the boundary of segmentation. Then, the signal in each time window is normalized to highlight the peaks and troughs of the breathing waveform, and the processed result is as shown in Figure 7 At this time, the number of breaths in this time period can be obtained from the waveform.

[0071] Experimental results:

[0072] (1) Hardware and software.

[0073] Hardware: The device is as Figure 8 , which uses an impinj R220 RFID receiver connected to a circular polarized antenna. The antenna is placed on a movable trolley, and the trolley moves along different paths within the sensing range of the tags. The sensing device and the trolley are fixed together by tape and special supports, as shown in Figure 9The communication frequency of the receiver is 920 MHz, and the trolley is an STM332 laser radar intelligent trolley, which has a size of 269*194*140(mm), a load capacity of 3kg, and a maximum speed of 1.2m / s. The motion trajectory and speed of the trolley are completely controlled by the handle. The waveform true value of the target is collected by using the breathing sensor HKH-11C, the breathing condition is detected by placing the target label on the abdomen of the user, and reference labels at different positions are set.

[0074] Software: The software uses the software ItemTest matched by Impinj to configure and control the receiving device, and the system is realized based on MatlabR2022b.

[0075] (2) Label deployment

[0076] Two labels are used in the application, namely a target label and a reference label:

[0077] Target label: The target label is attached to the navel of the volunteer, and the micro-movement caused by breathing is monitored. Breathing causes abdominal movement, and the phase of the target label can reveal the details of this movement.

[0078] Reference label: In the collection of data, noise generated by the movement of the device and other environmental factors in the target area will inevitably be collected. In order to eliminate the influence of this part, a reference label is set. In the experiment, information reflected back by the two labels is collected at the same time, and then through further processing, the noise influence is eliminated and the breathing waveform is recovered.

[0079] In the recovery of the breathing waveform, the position selection of the reference label is crucial and directly determines the recovery effect of the waveform. In order to select the best position of the reference label, labels are attached to different objects as reference labels. Two experiments are included, the first is to attach the label to the shoulder of the subject, considering that when doing abdominal breathing, the shoulder part will not produce obvious vibration, which meets the deployment requirements of the reference label. The other case is to place a static object next to the subject and attach the label to it. Different experiments are done for these two cases, and the experimental results are shown in Figure 10 , wherein Figure 10 (a) is the result graph when the reference label is located on the shoulder, Figure 10 (b) is the result graph when the reference label is located on the static object. As can be seen from the graph, when the label is attached to the static object next to the subject, the waveform recovery effect is the best, and it is found that as the distance between the static object and the subject decreases and does not block the test label, the recovery effect is continuously improved.

[0080] (3) Environment-related experiments

[0081] The impact of different obstacles: In specific application environments, obstacles will inevitably appear within the monitoring range. To verify the robustness of the system in the presence of obstacles, stationary obstacles were placed within the monitoring range while keeping other factors constant. The experimental results are as follows: Figure 11 As shown, where Figure 11 (a) shows the breathing waveform with one obstacle, and (11)b shows the breathing waveform with two obstacles. The experimental results show that the accuracy of waveform recovery will decrease slightly when the number of obstacles increases.

[0082] Different experimental environments: Experiments were conducted in three different environments: a corridor, a laboratory, and an open hall, corresponding to scenarios 1, 2, and 3, respectively. Figure 12 (a) Figure 12 (b) and Figure 12 (c) In these three environments, the device is required to maintain a constant speed along a straight line to examine the robustness of the system. Specific experimental results are as follows: Figure 13 As shown, the present invention can achieve results with small errors in different environments, further illustrating the robustness of the present invention.

[0083] (4) The influence of different test postures

[0084] In real life, the movements of monitored individuals are diverse. To verify the robustness of the system, this invention requires test subjects to perform the experiment in four different postures: standing, semi-squatting, lying on their side, and sitting still. The experimental results are as follows: Figure 14 As shown, where Figure 14 (a) Standing in the corresponding position. Figure 14 (b) Corresponds to a half squat. Figure 14 (c) Corresponding to lateral decubitus position, Figure 14 (d) The results of the recovery of the breathing waveform under the four different sitting postures are shown. The present invention can accurately recover the specific breathing waveform under different postures, which fully proves the robustness of the present invention.

[0085] (5) Multi-user monitoring

[0086] In real-world scenarios, respiratory monitoring should be able to monitor multiple individuals simultaneously. Therefore, this invention explores the system's performance when handling multiple subjects. Specifically, this invention monitors two subjects, ensuring that their reflected signals are simultaneously captured by an RFID device. Keeping other factors constant, both subjects breathe simultaneously, with one subject required to have a slightly faster breathing rate. Figure 15 (a) The respiratory waveform results at a slower respiratory rate. Figure 15(b) the corresponding respiratory rate is faster, when two subjects appear at the same time, the present application can still maintain good waveform recovery ability. But with the increasing number of subjects, it will be found that the recovery ability gradually decreases, mainly because the limited monitoring range of the device.

[0087] (6) ablation experiment

[0088] The effectiveness of double labels: to prove the effectiveness of the double label method, a group of experiments is designed, only using the target label in the double label for testing. Through verification, it is found that the performance difference between single label and double label in the recovery of respiratory waveform is significant. Only relying on single label cannot effectively reconstruct accurate respiratory waveform, in which Figure 16 (a) and Figure 16 (b) correspond to double label and single label respectively, the results clearly show the advantage and effectiveness of the double label method in recovering the respiratory waveform.

[0089] The effectiveness of motion trajectory segmentation: due to the inevitable turning problem of mobile devices in actual application, the present application proposes to use RSSI for direction segmentation, and recover the respiratory waveform by segment. To verify the necessity of RSSI segmentation for respiratory waveform recovery, the present application directly recovers all respiratory waveforms without using RSSI segmentation, such as Figure 16 (c), and compares the results with the recovery results after using RSSI segmentation. The experiment shows that when no direction segmentation is performed, the instability of the amplitude of the respiratory waveform will increase. The results further verify the effectiveness and necessity of RSSI segmentation in the recovery of respiratory waveform.

[0090] The effectiveness of waveform recovery algorithm: the present application proposes to eliminate the linear trend of the signal to eliminate the phase change of the signal introduced by the device motion. To verify the effectiveness of this method, it is compared with the direct division waveform recovery method. The specific experimental results are shown in Figure 16 (d), the results show that eliminating the linear trend of the waveform can significantly improve the accuracy of the recovery of respiratory waveform.

Claims

1. A method for monitoring human respiration using a mobile RFID device, characterized in that, Includes the following steps: Signal data collection: A target tag and a reference tag are set, and signal data of the target tag and the reference tag are collected by a moving receiver; the target tag is used to monitor the micro-movements caused by the subject's breathing, and the reference tag is a static tag. The distance and direction information of the receiver movement are obtained based on the phase and RSSI signal data provided by the reference tag. Signal data preprocessing: Separate and filter the signal data of the target tag and the reference tag, and segment the reference tag signal data based on RSSI direction segmentation; The separated and filtered reference tag and target tag signal data are phase expanded, and the signal data is denoised by wavelet domain denoising to obtain preprocessed signal data. Target respiratory waveform recovery: Using preprocessed signal data, based on the distance and direction information of receiver movement obtained from the reference tag, the distance and direction information of receiver movement in the target tag are eliminated, and the waveform is normalized to recover the human respiratory monitoring data.

2. The method for monitoring human respiration using a mobile RFID device according to claim 1, characterized in that, The receiver is connected to a circularly polarized antenna, and both the receiver and the circularly polarized antenna are mounted on a mobile device to enable the receiver to move. The mobile device moves along different paths within the perception range of the target tag and the reference tag. The target tag is positioned above the subject's abdomen, and the reference tag is positioned on a static object around the subject.

3. A method for monitoring human respiration using a mobile RFID device according to claim 2, characterized in that, The signal data of the target tag and the reference tag can be represented as follows: In the formula, The imaginary unit, For reference tag signal data, For target tag signal data, For the receiver frequency, The speed of the path change introduced by the motion of the mobile device relative to the reference label. The speed of path change introduced by the movement of the mobile device relative to the target label.

4. The method for monitoring human respiration using a mobile RFID device according to claim 1, characterized in that, The RSSI is represented as: In the formula, Represents the reference label gain. Represents the receiver's transmit power. Represents receiver transmission loss. represents the antenna gain, d represents the distance between the reference tag and the receiver, and 1mW represents 1 milliwatt, which is used as a reference power to convert the transmit power PT to a ratio relative to 1 milliwatt.

5. A method for monitoring human respiration using a mobile RFID device according to claim 3, characterized in that, The specific recovery of the target respiratory waveform is as follows: S1: For any time... and The distance that the mobile device can move is d， The change in the signal path length of the target label is as follows: The signal path length of the reference tag changes as follows , in and These are the angles between their respective perpendicular lines and the direction of equipment movement; S2: Based on the distance the mobile device has moved as obtained in S1, the signal phase in the target tag is obtained as follows: The signal phase in the reference label is ; S3: Based on the signal phase obtained in S2, the angle difference is quantified by the ratio of the signal phases of the mobile device on the target tag and the reference tag, i.e., the compensation coefficient. : = S4: Multiply the compensation coefficient obtained in S3 by the signal data of the reference tag, specifically: S5: Dividing the signal data of the target tag and the reference tag, and combining this with equation S4, we get: In the formula, A t A represents the amplitude of the target label. r The amplitude of the reference label; S6: Use the least squares method to... Perform a linear fit and subtract the fitted value from the phase to recover the waveform; S7: Select a fixed time window to segment the target label signal, and use the position where the RSSI slope changes sign as the segmentation boundary. Normalize the signal within each time window to obtain human respiratory monitoring data.

6. A method for monitoring human respiration using a mobile RFID device according to claim 1, characterized in that, The target labels are multiple and are used to realize the micro-movements caused by the breathing of multiple subjects.

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