Human heartbeat detection method, device and equipment based on ultra-wideband radar

Abstract

The present invention relates to a human heartbeat detection method, device, and equipment based on ultra-wideband radar. By adopting a two-stage design of pre-detection and algorithm detection, first, in the pre-detection stage, the matching of the heartbeat detection algorithm is completed, and an SSA window adaptive switch is designed to accurately match the heartbeat detection algorithm of the current detection scenario based on the clothing situation of the measured area and support whether to enable adaptive SSA dynamic parameter adjustment. The detection process supports the SSA window adaptive adjustment strategy based on signal energy distribution, and uses the adaptively matched basic detection algorithm or high-order detection algorithm to achieve precise heartbeat detection processing in real-time detection scenarios. Without excessive increase in computational burden, the signal-to-noise ratio of the heartbeat signal is effectively improved through the adaptive matching algorithm and the SSA window adaptive adjustment strategy, while the reliability of human heartbeat detection is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of non-contact heartbeat detection, and relates to a method, device, and equipment for detecting human heartbeat based on ultra-wideband radar. Background Art

[0002] Non-contact human heartbeat detection based on ultra-wideband radar (UWB) utilizes the characteristics of strong penetration and anti-multipath interference of UWB radar. After collecting the echo signal of the human chest cavity, the static clutter is removed through the CA-CFAR (Cell-Averaging Constant False Alarm Rate) algorithm, and the target chest cavity micro-motion signal is extracted through clutter filtering processing. Then, the improved singular spectrum analysis (SSA) is used to construct and reconstruct the singular values through the trajectory matrix, and the main components of the signal are retained to suppress noise. Next, the variational mode decomposition (VMD) is used to separate the respiration and heartbeat signals through constraint conditions to avoid the mode mixing problem. Its general signal processing flow usually includes parts such as sampling, echo matrix construction, filtering, reconstruction, and frequency domain analysis.

[0003] However, the heartbeat signal is usually very weak and is easily interfered by environmental noise and respiration harmonics, which easily leads to errors in frequency estimation. This is because the chest cavity displacement caused by the heartbeat is only about millimeters, the signal amplitude is extremely low, and its noise sources are also relatively complex, often including device noise, environmental reflection clutter, and respiration harmonics (overlapping with the heartbeat frequency band) and other noise sources. In addition, traditional filtering and SSA may not be able to completely and effectively separate high-frequency noise and low-frequency physiological signals. With the urgent need for non-contact health monitoring (such as telemedicine and disaster rescue, etc.), how to more effectively improve the reliability of human heartbeat detection has become the core problem in the popularization and application of such technologies. The weak signal enhancement technology thus introduced can promote the wide application of UWB radar in fields such as medical treatment and security. Summary of the Invention

[0004] Aiming at the problems existing in the above traditional technologies, the present invention proposes a method for detecting human heartbeat based on ultra-wideband radar, a device for detecting human heartbeat based on ultra-wideband radar, and a device for detecting human heartbeat, which can more effectively improve the reliability of human heartbeat detection.

[0005] To achieve the above object, the embodiments of the present invention adopt the following technical solutions:

[0006] On the one hand, a method for detecting human heartbeat based on ultra-wideband radar is provided, including the steps of:

[0007] Pre-detect the measured area of the target to be measured by using an ultra-wideband radar, and lock the ultra-wideband radar at the calibrated distance point of the measured area according to the radar echo of the pre-detection; the ultra-wideband radar is the radar on the pan-tilt base, and the measured area is the human chest cavity;

[0008] Determine the clothing thickness range of the measured area by looking up a table in a preset energy thickness table according to the pre-detected radar echo energy loss; the energy thickness table includes the mapping of different echo energy losses and clothing thickness ranges.

[0009] Call a matching heartbeat detection algorithm from the detection algorithm library according to the clothing thickness range and determine whether to turn on the SSA window adaptive switch; the heartbeat detection algorithm includes a basic detection algorithm and a high-order detection algorithm.

[0010] Receive the detection echo of the ultra-wideband radar for the measured area, suppress the static clutter of the detection echo by using the background difference method, and extract the chest micro-motion signal of the measured area from the detection echo through the heartbeat detection algorithm.

[0011] Construct a trajectory matrix, reconstruct singular values, and perform variational mode decomposition on the chest micro-motion signal through the heartbeat detection algorithm; among them, if the SSA window adaptive switch is turned on, adjust the SSA sliding window according to the set SSA window adaptive adjustment strategy, otherwise keep the SSA sliding window unchanged.

[0012] Inverse-transform the frequency-domain heartbeat signal after variational mode decomposition into a time-domain heartbeat signal for output.

[0013] On the other hand, a human heartbeat detection device based on an ultra-wideband radar is also provided, including:

[0014] A pre-detection module, configured to pre-detect the measured area of the measured target by using an ultra-wideband radar, and lock the ultra-wideband radar at the calibrated distance point of the measured area according to the pre-detected radar echo; the ultra-wideband radar is the radar on the pan-tilt base, and the measured area is the human chest.

[0015] A loss look-up table module, configured to determine the clothing thickness range of the measured area by looking up a table in a preset energy thickness table according to the pre-detected radar echo energy loss; the energy thickness table includes the mapping of different echo energy losses and clothing thickness ranges.

[0016] An algorithm switch module, configured to call a matching heartbeat detection algorithm from the detection algorithm library according to the clothing thickness range and determine whether to turn on the SSA window adaptive switch; the heartbeat detection algorithm includes a basic detection algorithm and a high-order detection algorithm.

[0017] A micro-motion extraction module, configured to receive the detection echo of the ultra-wideband radar for the measured area, suppress the static clutter of the detection echo by using the background difference method, and extract the chest micro-motion signal of the measured area from the detection echo through the heartbeat detection algorithm.

[0018] A signal processing module for constructing a trajectory matrix, reconstructing singular values, and performing variational mode decomposition on the thoracic micro-motion signal through a heartbeat detection algorithm. Among them, if the SSA window adaptive switch is turned on, the SSA sliding window is adjusted according to the set SSA window adaptive adjustment strategy; otherwise, the SSA sliding window remains unchanged.

[0019] A detection output module for inverse-transforming the frequency-domain heartbeat signal after variational mode decomposition into a time-domain heartbeat signal for output.

[0020] On the other hand, a human heartbeat detection device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned human heartbeat detection method based on ultra-wideband radar are implemented.

[0021] One of the above technical solutions has the following advantages and beneficial effects:

[0022] The above-mentioned human heartbeat detection method, device, and equipment based on ultra-wideband radar adopt a two-stage design of pre-detection and algorithm detection. First, in the pre-detection stage, the heartbeat detection algorithm is matched and an SSA window adaptive switch is designed. It can accurately match the heartbeat detection algorithm of the current detection scenario based on the clothing situation in the measured area and support whether to turn on adaptive SSA dynamic parameter adjustment. The detection process supports the SSA window adaptive adjustment strategy based on signal energy distribution, and uses the adaptively matched basic detection algorithm or high-order detection algorithm to achieve precise heartbeat detection processing in real-time detection scenarios. Without excessive increase in computational burden, through the adaptive matching algorithm and SSA window adaptive adjustment strategy, the signal-to-noise ratio of the heartbeat signal is effectively improved, and the reliability of human heartbeat detection is enhanced. Description of the Drawings

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0024] Figure 1 It is a flowchart of the human heartbeat detection method based on ultra-wideband radar in an embodiment;

[0025] Figure 2 It is a detection logic diagram of the human heartbeat detection method based on ultra-wideband radar in an embodiment;

[0026] Figure 3 It is a flowchart of the implementation of frequency-domain enhancement in an embodiment;

[0027] Figure 4 is a block diagram of a human heartbeat detection device based on ultra-wideband radar in an embodiment;

[0028] Figure 5 is a schematic diagram of the module structure of a human heartbeat detection device in an embodiment. Detailed implementation manners

[0029] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0030] It should be noted that referring to "embodiment" in this document means that a specific feature, structure or characteristic described in connection with the embodiment may be included in at least one embodiment of the present invention. The phrase is shown at various positions in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art can understand that the embodiments described herein can be combined with other embodiments. The term "and / or" used in the description of the present invention refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0031] The following will describe the implementation manners of the present invention in detail with reference to the accompanying drawings in the embodiments of the present invention.

[0032] To further solve the problem of low signal-to-noise ratio of the heartbeat signal, the following possible solutions have been found in actual research: Deep learning denoising, which identifies noise patterns by pre-training CNN / RNN models and adaptively suppresses interference. In an end-to-end optimized manner, it can effectively adapt to complex noise environments. However, this method requires a large amount of labeled training data and usually consumes a large amount of computing resources. Multi-sensor data fusion, which combines infrared thermal imaging or piezoelectric sensors to complement and enhance the heartbeat signal, can use multi-dimensional data to improve robustness. However, the system complexity may increase, and the problem of multi-source data synchronization needs to be solved. Dynamic parameter optimization reduces manual intervention by automatically adjusting the SSA window or the number of VMD modes according to the signal spectrum characteristics and improves the generalization ability of the algorithm. However, an efficient adaptive threshold mechanism needs to be specifically designed. Frequency domain enhancement technology focuses on the heartbeat frequency band (0.8 Hz to 2.5 Hz) by combining wavelet transform or synchrosqueezing transform (SST), which can effectively suppress interference in non-target frequency bands and retain signal details. However, the time-frequency resolution and computational efficiency need to be balanced. This application will propose a new method for detecting human heartbeat based on ultra-wideband radar based on the research solutions. The following is an explanation of the overall idea of this method.

[0033] In one embodiment, as Figure 1 shown, a method for detecting human heartbeat based on ultra-wideband radar is provided, which may include the following steps S10 to S20:

[0034] S10. Use the ultra-wideband radar to pre-detect the measured area of the target to be measured, and move the ultra-wideband radar to the calibrated distance point of the measured area according to the radar echo of the pre-detection and then lock it; the ultra-wideband radar is the radar on the pan-tilt base, and the measured area is the human chest.

[0035] It can be understood that the operating frequency band of the UWB radar can be from 3.1 GHz to 10.6 GHz, its bandwidth can reach 7.5 GHz, the range resolution is about 2 cm, and it has the characteristics of strong penetration (suitable for non-line-of-sight scenarios), anti-multipath interference and low power consumption. However, the amplitude of the heartbeat signal is extremely low (the chest displacement is about 0.1 mm to 0.5 mm), and it is easily submerged by environmental noise. The target to be measured is the human body for which human heartbeat detection is currently required. In this embodiment, the human chest area is used as the area to be measured for human heartbeat detection to ensure that the radar echo can contain the heartbeat information to the greatest extent in the initial stage. The calibrated distance point can be the best non-contact detection distance point from the human chest calibrated in advance according to the detection experiment of the ultra-wideband radar, which is used to further ensure the significance of the heartbeat information contained in the radar echo in the initial stage. In this embodiment, the ultra-wideband radar is installed on the pan-tilt base, and the ultra-wideband radar can be operated by a measurement operator holding it or placing it on a slide rail to detect, so as to lock the position of the calibrated distance point, remove the vibration noise introduced by factors such as hand vibration or ground vibration to the radar echo, and improve the purity of the received radar echo from the operation level.

[0036] S12. Determine the clothing thickness interval of the area to be measured by looking up a table in a preset energy thickness table according to the pre-detected radar echo energy loss; the energy thickness table includes the mapping between different echo energy losses and clothing thickness intervals.

[0037] It can be understood that in actual detection applications, the types of clothing materials and the number of clothes worn by different people during human heartbeat detection are often different. The detection signal of the low-frequency (such as 3.1 GHz to 10.6 GHz) ultra-wideband radar has strong penetration and can penetrate most thin clothes such as cotton and chemical fiber, but its penetration ability is weakened or even may fail in scenarios such as thick down jackets, multiple layers of clothes and metal-coated clothes (such as windbreakers). For example, experiments show that the signal attenuation of a cotton T-shirt to the low-frequency ultra-wideband radar is about 1 dB to 3 dB, while that of a thick sweater can reach 5 dB to 10 dB.

[0038] However, in traditional technologies, it is often considered that when detecting a human heartbeat, the person is not wearing clothes, wearing thin clothes, or using complex signal enhancement and filtering algorithms to weaken the influence of clothes on radar echoes. This results in limited application of the technology or complex and redundant calculations. In this embodiment, a full study is made on the differences in the types and quantities of clothing materials worn by different people when detecting a human heartbeat in actual detection applications. According to the situation of the clothes worn by a person when detecting a human heartbeat (such as below single-layer clothing and above double-layer clothing), two levels of clothing thickness intervals are pre-divided for the clothing thickness covered by the measured area. And through sampling experiments, the magnitude of the radar echo energy loss of the same ultra-wideband radar in the above two different clothing thickness intervals is calibrated, and the corresponding relationship between the above different clothing thickness intervals and the radar echo energy loss is made into a preset energy-thickness table to establish a mapping relationship between different clothing thickness intervals and radar echo energy loss.

[0039] S14. Call a matching heartbeat detection algorithm from the detection algorithm library according to the clothing thickness interval and determine whether to turn on the SSA window adaptive switch; the heartbeat detection algorithm includes a basic detection algorithm and a high-order detection algorithm.

[0040] It can be understood that the detection algorithm library can include a basic detection algorithm unit and a high-order detection algorithm unit integrated by software. These algorithm units will automatically perform corresponding algorithm processing when called by the system. Among them, the basic detection algorithm can include the existing improved singular spectrum analysis (SSA, which constructs a trajectory matrix and reconstructs singular values to retain the main components of the signal to suppress noise) and variational mode decomposition (VMD, which separates the respiration and heartbeat signals through constraint conditions to avoid the problem of mode mixing) in this field. The high-order detection algorithm is an improved version of the basic detection algorithm, in which a new frequency-domain enhancement process is designed to comprehensively improve the heartbeat detection ability of the detection echo in a low signal-to-noise ratio scenario. For example, when the clothing thickness interval is an interval that causes relatively large radar echo energy loss, the high-order detection algorithm will be matched for subsequent processing. Conversely, the basic detection algorithm will be matched for subsequent processing. By calling the adapted detection algorithm according to the clothing thickness interval of the current measured area in the way of algorithm library matching, it can meet the accurate detection requirements while flexibly saving computing resources and computing time, achieving the effect of algorithm adaptability.

[0041] Among them, the SSA window adaptive switch is an algorithm switch used to determine whether to enable the set SSA window adaptive adjustment strategy in the current detection, enabling the detection to adjust and improve the algorithm parameters of the singular spectrum analysis according to the real-time signal characteristics, thereby reducing manual intervention and improving the detection effect. The SSA window adaptive switch can be automatically turned on or off according to the matched heartbeat detection algorithm, or can be manually turned on or off, regardless of which heartbeat detection algorithm is currently matched, to ensure the improvement of the detection effect.

[0042] S16. After receiving the detection echo of the ultra-wideband radar from the area to be measured and suppressing the static clutter of the detection echo using the background difference method, the thoracic micro-motion signal of the area to be measured is extracted from the detection echo through the heartbeat detection algorithm.

[0043] It can be understood that after matching the heartbeat detection algorithm required to be called currently, the formal heartbeat detection of the area to be measured is entered. The ultra-wideband radar locks at the calibrated distance point of the area to be measured and emits a radar detection signal to the area to be measured. After receiving the corresponding detection echo, the background difference method is used to suppress the static clutter of the detection echo. Specifically, the static background template can be generated by averaging the detection echo for a long time (such as 5 s) first, and the real-time echo signal is subtracted from the background template to eliminate the reflection of fixed objects in the area to be measured and its surrounding positions. The detection echo after static clutter suppression can be expressed as follows :

[0044] ;

[0045] Among them, is the initial received detection echo before static clutter suppression, t represents time, T is the sampling interval, N is the average number of frames. Then, the thoracic micro-motion signal of the area to be measured is extracted from the detection echo through the heartbeat detection algorithm. The specific extraction process of this thoracic micro-motion signal can be the same as the extraction process of the thoracic micro-motion signal in the traditional technology.

[0046] S18. Construct the trajectory matrix, reconstruct the singular value, and perform variational mode decomposition on the thoracic micro-motion signal through the heartbeat detection algorithm; among them, if the SSA window adaptive switch is turned on, the SSA sliding window is adjusted according to the set SSA window adaptive adjustment strategy, otherwise the SSA sliding window remains unchanged.

[0047] It can be understood that the construction of the trajectory matrix and the reconstruction of singular values are existing processes for improving singular spectrum analysis (SSA). The variational mode decomposition is VMD. The specific processing flow can be understood by analogy with the existing SSA and VMD processing flows in the art to complete the separation of respiratory and heartbeat signals. At this time, if the SSA window adaptive switch is automatically or manually turned on, the improved singular spectrum analysis process will adjust the SSA sliding window according to the SSA window adaptive adjustment strategy set in this application to accurately and dynamically adapt to the SSA analysis and processing under different noise levels, thereby significantly improving the signal-to-noise ratio of the heartbeat signal.

[0048] S20, inverse-transform the frequency-domain heartbeat signal after variational mode decomposition into a time-domain heartbeat signal and output it.

[0049] It can be understood that the inverse transformation can use the inverse FFT to convert the frequency-domain signal back to the time-domain signal for output. The above-mentioned human heartbeat detection method based on ultra-wideband radar adopts a two-stage design of pre-detection and algorithm detection. First, in the pre-detection stage, the heartbeat detection algorithm is matched and the SSA window adaptive switch is designed to accurately match the heartbeat detection algorithm of the current detection scenario based on the clothing situation in the measured area and support whether to turn on the adaptive SSA dynamic parameter adjustment, so that the detection process supports the SSA window adaptive adjustment strategy based on the signal energy distribution designed, and uses the adaptively matched basic detection algorithm or high-order detection algorithm to achieve accurate heartbeat detection processing in the real-time detection scenario. Without excessive increase in the computational burden, through the adaptive matching algorithm and the SSA window adaptive adjustment strategy, the signal-to-noise ratio of the heartbeat signal is effectively improved while the reliability of human heartbeat detection is improved.

[0050] In one embodiment, as Figure 2 shown, the process of determining whether to turn on the SSA window adaptive switch includes:

[0051] If the high-order detection algorithm is matched from the detection algorithm library for the clothing thickness interval, the SSA window adaptive switch is turned on.

[0052] It can be understood that in this embodiment, the system can automatically determine whether to turn on the SSA window adaptive switch by confirming whether the currently matched heartbeat detection algorithm is a high-order detection algorithm. That is, when the clothing thickness interval determined by the current look-up table matches a high-order detection algorithm in the detection algorithm library, the system automatically turns on the SSA window adaptive switch, so that during the processing of the high-order detection algorithm, the SSA window adaptive adjustment is enabled for the improved singular spectrum analysis process, thereby ensuring a significant improvement in the signal-to-noise ratio of the heartbeat signal in the detected echo in this scenario. Conversely, when the system confirms that the currently matched heartbeat detection algorithm is not a high-order detection algorithm but a basic detection algorithm, the SSA window adaptive switch is automatically turned off, so as to directly complete the heartbeat detection in the current scenario through the basic detection algorithm, saving the system's computing resources while ensuring that the basic reliability requirements for heartbeat signal detection are met.

[0053] In one embodiment, further, as Figure 2 shown, the set SSA window adaptive adjustment strategy includes:

[0054] When the short-time energy of the thoracic micro-motion signal in the SSA sliding window is lower than the set first energy threshold and the main frequency ratio is lower than 50%, the SSA sliding window is reduced by one gear at every set time interval within the set first window size range; the first window size range is from 20 ms to 15 ms;

[0055] When the short-time energy of the thoracic micro-motion signal in the SSA sliding window is higher than the second energy threshold and the main frequency ratio is greater than 70%, the SSA sliding window is enlarged by one gear at every set time interval within the set second window size range; the second window size range is from 30 ms to 40 ms; the first energy threshold is less than the second energy threshold.

[0056] It can be understood that in order to realize the real-time adjustment of the SSA algorithm parameters according to the signal characteristics and greatly reduce the intervention of manual parameter adjustment, a new dynamic parameter optimization design is given in this specification. In this embodiment, first, the design of the dynamic adaptive selection of the SSA sliding window is carried out, and the signal energy of the thoracic micro-motion signal in the SSA sliding window is calculated to obtain the short-time energy to discriminate its noise level, and the short-time energy The calculation method can be as follows:

[0057] ;

[0058] Among them, is the sampling value of the thoracic micro-motion signal at the discrete time point k in the SSA sliding window, k is the discrete time index, indicating the k th sampling point, k starting from to t , indicating that all sampling points within the current SSA sliding window are covered. L is the SSA sliding window size. For the thoracic micro-motion signal, the energy ratio of the heartbeat frequency band (0.8 Hz to 2.5 Hz, i.e., the main frequency) can be analyzed through FFT (Fourier transform), which can be used to effectively identify respiratory harmonic interference.

[0059] Specifically, the dedicated SSA window adaptive adjustment strategy can be designed as follows: (1) High-noise scenario (i.e., STE < the first energy threshold and the main frequency ratio < 50%): Reduce the SSA sliding window at a set time interval within the set first window size range (the set first window size range is 20 ms to 15 ms) to reduce the integration time of noise energy in the complex noise scenario, effectively avoiding the noise from accumulating and dominating the trajectory matrix in the long window, deeply conforming to the fast-changing characteristics of the weak heartbeat signal. Utilize the fact that the energy is more evenly distributed and the signal energy is concentrated in the short window in real time to better retain its time-domain details and improve the signal-to-noise ratio of the heartbeat signal.

[0060] (2) Low-noise scenario (i.e., STE > the second energy threshold and the main frequency ratio > 70%): Expand the SSA sliding window at a set time interval within the set second window size range (the second window size range is 30 ms to 40 ms). By adaptively amplifying the SSA sliding window, more signal energy is distributed in the long window in the low-noise scenario, retaining more signal details to improve the signal-to-noise ratio of the heartbeat signal. Update the sliding window size at an interval of every 1 second or less than 1 second.

[0061] Among them, the set time interval can be 1 second or less than 1 second, and the SSA sliding window adjustment can be automatically triggered by the system based on the sliding statistic (such as the moving average of STE); each step can be 1 ms or 5 ms, and specifically can be selected according to the speed of improving the signal-to-noise ratio of the heartbeat signal in the echo during the experimental determination, or set by the tester based on the empirical value during the system power-on initialization. The first energy threshold can be set as follows: By pre-collecting signal samples (such as high-noise and low-noise) at different noise levels and annotating the noise level of each signal sample (such as the signal-to-noise ratio SNR), calculate the short-time energy (STE) and the main frequency ratio (the percentage of the main frequency energy in the total energy) of all signal samples, draw the STE histograms of high-noise and low-noise samples, determine the critical value for distinguishing the two, and after analyzing the distribution law of the main frequency ratio in the noise environment, select the STE value that makes the classification (as high-noise) accuracy of the signal samples the highest as the set first energy threshold. The second energy threshold is set similarly, only selecting the STE value that makes the classification (as low-noise) accuracy of the signal samples the highest as the set second energy threshold.

[0062] Optionally, the pseudo-code corresponding to the above SSA window adaptive adjustment strategy can be as follows:

[0063]

[0064] By further identifying high-noise signals through the first energy threshold and initiating the corresponding SSA window adaptive adjustment, and further identifying low-noise signals through the second energy threshold, it is possible to accurately and effectively reduce the interference of different noise levels on subsequent signal analysis and processing (such as SSA reconstruction), while avoiding the use of a fixed large window in a noise-dominated scenario, preventing signal distortion caused by the accumulation of noise energy, significantly improving the signal-to-noise ratio of the heartbeat signal and enhancing the robustness of the algorithm.

[0065] In one embodiment, the above-mentioned human heartbeat detection method based on ultra-wideband radar further includes the steps of:

[0066] When the short-time energy of the thoracic cavity micro-motion signal in a continuous set number of SSA sliding windows is higher than the first energy threshold and the main frequency ratio is lower than 50%, update the first energy threshold to the average signal energy of the thoracic cavity micro-motion signal in the continuous set number of SSA sliding windows.

[0067] Specifically, considering that when the ultra-wideband radar continuously detects the same measured area or detects the measured areas of different measured targets, the signal energy may drift as a whole due to external influences (such as the appearance of new interference signals) or internal influences of the radar. In this embodiment, an adaptive dynamic adjustment mechanism for the first energy threshold is further designed and introduced to adaptively update the first energy threshold according to the real-time signal energy characteristics to automatically match the current detection environment changes, avoiding frequent manual recalibration of the first energy threshold. The set number can be but is not limited to 5 or 8, so as to avoid misjudgment due to too short judgment time while taking into account the energy threshold update efficiency and accuracy. It can be understood that when the main frequency ratio remains unchanged but the short-time energy of the thoracic cavity micro-motion signal in a continuous set number of SSA sliding windows is significantly lower than the first energy threshold (such as less than half of the first energy threshold), the same adaptive dynamic adjustment mechanism for the first energy threshold can also be adopted to achieve a similar detection environment change adaptation effect.

[0068] In one embodiment, the above-mentioned human heartbeat detection method based on ultra-wideband radar further includes the steps of:

[0069] When the short-time energy of the thoracic cavity micro-motion signal in a continuous set number of SSA sliding windows is lower than the second energy threshold and the main frequency ratio is greater than 70%, update the second energy threshold to the average signal energy of the thoracic cavity micro-motion signal in the continuous set number of SSA sliding windows.

[0070] Specifically, considering that when the ultra-wideband radar continuously detects the same measured area or detects the measured areas of different measured targets, the signal energy may have an overall drift due to external influences or internal influences of the radar (such as power supply decline). In this embodiment, an adaptive dynamic adjustment mechanism for the second energy threshold is further designed and introduced to adaptively update the second energy threshold according to the real-time signal energy characteristics to automatically match the current detection environment changes, and avoid frequent manual recalibration of the second energy threshold. The set number can be, but is not limited to, 5 or 8, so as to avoid misjudgment caused by too short judgment time while taking into account the energy threshold update efficiency and accuracy. It can be understood that when the main frequency ratio remains unchanged but the short-time energy of the thoracic micro-motion signal in a continuous set number of SSA sliding windows is significantly higher than the second energy threshold (such as 1 time higher than the second energy threshold), the same adaptive dynamic adjustment mechanism for the second energy threshold can also be adopted to achieve a similar detection environment change adaptation effect.

[0071] In one embodiment, the set SSA window adaptive adjustment strategy includes:

[0072] Match the short-time energy of the thoracic micro-motion signal in the current SSA sliding window with each standard sample with noise level annotation in the signal energy sample library to determine the noise level of the thoracic micro-motion signal in the current SSA sliding window; the noise level includes high noise or low noise;

[0073] When the noise level of the thoracic micro-motion signal in the SSA sliding window is high noise, reduce the SSA sliding window by one gear at each set time interval within the set first window size range; the first window size range is from 20 ms to 15 ms;

[0074] When the noise level of the thoracic micro-motion signal in the SSA sliding window is low noise, expand the SSA sliding window by one gear at each set time interval within the set second window size range; the second window size range is from 30 ms to 40 ms.

[0075] It can be understood that in this embodiment, another SSA window adaptive adjustment strategy is also designed. By collecting and annotating thoracic micro-motion signal samples in advance to construct a signal energy sample library, a mapping relationship between standard samples of different signal energies and noise levels is established. Among them, the main frequency proportion of the standard samples is also <50% and >70% (the discrimination boundaries of these two main frequency proportions are the optimal boundaries considering discrimination accuracy and computational complexity through experimental statistics). Therefore, when the short-time energy of the thoracic micro-motion signal in the current SSA sliding window matches the standard sample of the corresponding signal energy, the system will automatically compare whether the main frequency proportion of the thoracic micro-motion signal in the current SSA sliding window meets the main frequency proportion constraint of the standard sample. If it meets, the noise level of the standard sample is used to calibrate the noise level of the thoracic micro-motion signal in the current SSA sliding window. Otherwise, the thoracic micro-motion signal in the current SSA sliding window is discarded, and the thoracic micro-motion signal in the next SSA sliding window is directly judged and processed.

[0076] Through the aforementioned another SSA window adaptive adjustment strategy, it is also possible to accurately and effectively reduce the interference of different noise levels on subsequent signal analysis and processing, and at the same time avoid using a fixed large window in a noise-dominated scenario to prevent signal distortion caused by noise energy accumulation, greatly improving the signal-to-noise ratio of the heartbeat signal and enhancing the robustness of the algorithm.

[0077] In one embodiment, as Figure 3 shown, the basic detection algorithm includes improved singular spectrum analysis and variational mode decomposition, and the high-order detection algorithm includes improved singular spectrum analysis, variational mode decomposition and frequency domain enhancement; among them, the process of frequency domain enhancement includes:

[0078] S101, perform time-frequency decomposition on the signal after singular value reconstruction using Morlet wavelet to obtain a high-resolution time-frequency diagram;

[0079] S103, compress the energy along the direction of the instantaneous frequency derivative of the high-resolution time-frequency diagram to obtain a high-resolution time-frequency diagram after frequency reassignment;

[0080] S105, retain the signal in the frequency band of 0.8Hz to 2.5Hz on the high-resolution time-frequency diagram after frequency reassignment and set the rest of the signals to zero to complete the extraction of the heartbeat frequency band;

[0081] S107, adaptively filter out the invading respiratory harmonic frequency from the signal extracted from the heartbeat frequency band using the designed IIR notch filter; the center frequency of the IIR notch filter is the dynamically detected respiratory harmonic frequency;

[0082] S109, output the time-domain heartbeat signal after filtering out the invading respiratory harmonic frequency to replace the time-domain heartbeat signal obtained after variational mode decomposition.

[0083] It can be understood that when the signal after singular value reconstruction is subjected to wavelet transform, the Morlet wavelet is used to decompose the signal in time and frequency to obtain a high-resolution time-frequency diagram, and then the frequency is redistributed by compressing the energy along the instantaneous frequency derivative direction to enhance the sparsity of the heartbeat frequency, retaining the 0.8Hz to 2.5Hz frequency band, and setting the rest to zero to complete the frequency band extraction. Compared with the traditional FFT, it has a higher resolution in the time-frequency domain and can more effectively separate the overlapping respiratory harmonics and heartbeat signals.

[0084] Among them, the adaptive comb filter is designed based on the principle that the fundamental frequency of the respiratory signal (such as 0.1Hz to 0.5Hz) and its harmonics (such as 1Hz, 1.5Hz) may still invade the heartbeat frequency band, and the respiratory frequency can be detected dynamically. , set the notch filter to suppress the heartbeat frequency band and The harmonics can be specifically realized by using an IIR notch filter, whose center frequency is set to the detected respiratory harmonic frequency and whose bandwidth is ±0.1Hz to further ensure its filtering performance. Finally, since the time domain heartbeat signal obtained by frequency domain enhancement in the high-order detection algorithm is more accurate and reliable than the time domain heartbeat signal obtained after variational mode decomposition, the time domain heartbeat signal after filtering out the intrusive respiratory harmonic frequency can be output instead of the time domain heartbeat signal obtained after variational mode decomposition.

[0085] In some embodiments, the verification example may be as follows: The quantitative indicators used in the performance verification include signal-to-noise ratio and heart rate error (RMSE). Among them, the signal-to-noise ratio :

[0086] ;

[0087] in, For at time point t The heartbeat signal amplitude at n ( t ) is at the time point t The heart rate error (RMSE) is calculated by comparing the UWB detection result with the R peak (ECG) measurement value and calculating the root mean square error (BPM). The test scenarios are as follows: static scenario, the target is in a sitting or lying position; dynamic scenario, the target is slightly shaking.

[0088] Data collection: 10 subjects (5 sitting still, 5 lightly exercising) synchronously collected UWB radar signals and ECG reference data in low noise (laboratory) and high noise (Wi-Fi interference environment) scenarios.

[0089] Traditional UWB heartbeat detection method (the previous generation technology related to this application as the baseline): Fixed window SSA ( L = 30) + band-pass filtering (0.8 Hz to 2.5 Hz). The above-mentioned human heartbeat detection method based on ultra-wideband radar in this application (new method) adopts dynamic SSA window + frequency domain enhancement + adaptive comb filter. In the evaluation index, the signal-to-noise ratio (SNR) calculates the ratio of the signal energy to the noise energy in the heartbeat frequency band (0.8 Hz to 2.5 Hz), and the heart rate error (RMSE) is specifically the root mean square error (BPM) between the UWB detected heart rate and the ECG heart rate.

[0090] Experimental data (as shown in Table 1) and result analysis:

[0091] Table 1

[0092]

[0093] It can be found from Table 1 that through experiments, it is shown that when the SSA window adaptive switch is turned on, in the low-noise and high-noise scenarios, the above-mentioned human heartbeat detection method based on ultra-wideband radar has an SNR increase of 35% and 125% respectively compared to the previous generation technology. The frequency domain enhancement technology effectively suppresses respiratory harmonics and environmental noise, and the dynamic window optimization reduces motion interference. The RMSE also decreases by 66% compared to the previous generation technology in the high-noise scenario. Although the above-mentioned human heartbeat detection method based on ultra-wideband radar has an increased delay of 5 ms / frame to 10 ms / frame due to dynamic calculation, it is still within an acceptable range (<50 ms). It can be seen that the dynamic parameter optimization + frequency domain enhancement technology significantly improves the heartbeat detection accuracy in complex environments, especially suitable for motion or high-noise scenarios, providing a direct and reliable practical basis for the implementation of the aforementioned technology in medical monitoring and smart homes.

[0094] It should be understood that although the above steps Figure 1 and Figure 3 are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the above steps Figure 1 and Figure 3 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential either, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0095] In one embodiment, as Figure 4As shown in the figure, a human heartbeat detection device 100 based on ultra-wideband radar is provided, which includes a pre-detection module 11, a loss look-up table module 13, an algorithm switch module 15, a micro-motion extraction module 17, a signal processing module 19, and a detection output module 21. Among them, the pre-detection module 11 is used to pre-detect the measured area of the target to be measured by using ultra-wideband radar, and lock the ultra-wideband radar at the calibrated distance point of the measured area according to the radar echo of the pre-detection; the ultra-wideband radar is the radar on the pan-tilt base, and the measured area is the human chest. The loss look-up table module 13 is used to look up the clothing thickness interval of the measured area from a preset energy thickness table according to the energy loss of the radar echo of the pre-detection; the energy thickness table includes the mapping between different echo energy losses and clothing thickness intervals. The algorithm switch module 15 is used to call a matching heartbeat detection algorithm from the detection algorithm library according to the clothing thickness interval and determine whether to turn on the SSA window adaptive switch; the heartbeat detection algorithms include a basic detection algorithm and a high-order detection algorithm. The micro-motion extraction module 17 is used to receive the detection echo of the ultra-wideband radar from the measured area, suppress the static clutter of the detection echo by using the background difference method, and then extract the chest micro-motion signal of the measured area from the detection echo through the heartbeat detection algorithm. The signal processing module 19 is used to construct a trajectory matrix, perform singular value reconstruction and variational mode decomposition on the chest micro-motion signal through the heartbeat detection algorithm; among them, if the SSA window adaptive switch is turned on, the SSA sliding window is adjusted according to the set SSA window adaptive adjustment strategy, otherwise the SSA sliding window remains unchanged. The detection output module 21 is used to inverse-transform the frequency-domain heartbeat signal after variational mode decomposition into a time-domain heartbeat signal and output it.

[0096] The above-mentioned human heartbeat detection device 100 based on ultra-wideband radar adopts a two-stage design of pre-detection and algorithm detection. First, in the pre-detection stage, the matching of the heartbeat detection algorithm is completed and the SSA window adaptive switch is designed, so as to accurately match the heartbeat detection algorithm of the current detection scenario based on the clothing situation of the measured area and support whether to turn on the adaptive SSA dynamic parameter adjustment. The detection process supports the SSA window adaptive adjustment strategy based on the signal energy distribution, and uses the adaptively matched basic detection algorithm or high-order detection algorithm to achieve accurate heartbeat detection processing in the real-time detection scenario. Without excessive increase in the calculation burden, the signal-to-noise ratio of the heartbeat signal is effectively improved by the adaptive matching algorithm and the SSA window adaptive adjustment strategy, and the reliability of human heartbeat detection is improved.

[0097] In one embodiment, when the algorithm switch module 15 matches a high-order detection algorithm from the detection algorithm library in the clothing thickness interval, the SSA window adaptive switch is turned on.

[0098] For the specific limitations of a human heartbeat detection device 100 based on ultra-wideband radar, reference can be made to the corresponding limitations of the various embodiments of the human heartbeat detection method based on ultra-wideband radar in the foregoing text, which will not be elaborated herein.

[0099] Figure 5 It is a schematic diagram of a human heartbeat detection device 4 provided in an embodiment of the present application. As Figure 5 shown, the human heartbeat detection device 4 of this embodiment includes: a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the computer program 403, the steps in the above-mentioned various embodiments of the human heartbeat detection method based on ultra-wideband radar can be implemented. Alternatively, when the processor 401 executes the computer program 403, the functions of the various modules / units in the embodiment of the human heartbeat detection device 100 based on ultra-wideband radar are implemented.

[0100] The human heartbeat detection device 4 can be a computer device such as a desktop computer, a notebook, a palm computer, and a cloud server. The human heartbeat detection device 4 can include but is not limited to the processor 401 and the memory 402. Those skilled in the art can understand that Figure 5 merely examples of the human heartbeat detection device 4, which do not constitute a limitation on the human heartbeat detection device 4, and may include more or fewer components than those shown, or different components.

[0101] The processor 401 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0102] The memory 402 may be an internal storage unit of the human body heart rate detection device 4. For example, it can be the hard disk or memory of the human body heart rate detection device 4. The memory 402 may also be an external storage device of the human body heart rate detection device 4. For example, it can be a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the human body heart rate detection device 4. The memory 402 may also include both the internal storage unit and the external storage device of the human body heart rate detection device 4. The memory 402 is used to store computer programs and other programs and data required by the computer device.

[0103] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit and module is used as an example. In practical applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0104] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above method embodiments of the present application, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above method embodiments. The computer program may include computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of practice. For example, in some regions, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0105] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided by the present invention can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), memory bus dynamic random access memory (Rambus DRAM, abbreviated as RDRAM), and interface dynamic random access memory (DRDRAM), etc.

[0106] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0107] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the protection scope of the invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, which all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A human heartbeat detection method based on ultra-wideband radar, characterized in that: Includes steps: The ultra-wideband radar is used to pre-detect the measured area of ​​the measured target, and the ultra-wideband radar is moved to the calibrated distance point of the measured area according to the pre-detected radar echo indication and then locked; the ultra-wideband radar is a radar of the pan-tilt base, and the measured area is the human chest cavity; Determine the clothing thickness range of the measured area by looking up a preset energy thickness table according to the pre-detected radar echo energy loss; The energy thickness table includes a mapping of different echo energy losses and clothing thickness intervals; According to the clothing thickness interval, a matching heartbeat detection algorithm is called from the detection algorithm library and it is determined whether to turn on the SSA window adaptive switch; the heartbeat detection algorithm includes a basic detection algorithm and a high-order detection algorithm; After receiving the detection echo of the measured area to the ultra-wideband radar and performing static clutter suppression on the detection echo by using a background difference method, a chest micro-motion signal of the measured area is extracted from the detection echo by using the heartbeat detection algorithm; The heartbeat detection algorithm is used to construct a trajectory matrix, reconstruct singular values, and perform variational mode decomposition on the chest micro-motion signal; wherein, if the SSA window adaptive switch is turned on, the SSA sliding window is adjusted according to the set SSA window adaptive adjustment strategy, otherwise the SSA sliding window is maintained unchanged; The frequency domain heartbeat signal after variational mode decomposition is inversely transformed into a time domain heartbeat signal output.

2. The human heartbeat detection method based on ultra-wideband radar according to claim 1 is characterized in that: The process of determining whether to enable the SSA window adaptation switch includes: If the clothing thickness interval matches a high-order detection algorithm from the detection algorithm library, the SSA window adaptive switch is turned on.

3. The human heartbeat detection method based on ultra-wideband radar according to claim 2 is characterized in that: The set SSA window adaptive adjustment strategies include: When the short-term energy of the chest micro-motion signal in the SSA sliding window is lower than the set first energy threshold and the main frequency ratio is lower than 50%, the SSA sliding window is reduced by one level at a set time interval within the set first window size range; the first window size range is 20ms to 15ms; When the short-time energy of the chest micro-motion signal in the SSA sliding window is higher than the second energy threshold and the main frequency accounts for more than 70%, the SSA sliding window is expanded by one level at every set time interval within the set second window size range; the second window size range is 30ms to 40ms; the first energy threshold is less than the second energy threshold.

4. The human heartbeat detection method based on ultra-wideband radar according to claim 2 is characterized in that: The set SSA window adaptive adjustment strategies include: Matching the short-term energy of the chest micro-motion signal in the current SSA sliding window with each standard sample with a noise level label in the signal energy sample library to determine the noise level of the chest micro-motion signal in the current SSA sliding window; the noise level includes high noise or low noise; When the noise level of the chest micro-motion signal in the SSA sliding window is high noise, the SSA sliding window is reduced by one level at a set time interval within a set first window size range; the first window size range is 20ms to 15ms; When the noise level of the chest micro-motion signal in the SSA sliding window is low noise, the SSA sliding window is expanded by one level at every set time interval within a set second window size range; the second window size range is 30ms to 40ms.

5. The human heartbeat detection method based on ultra-wideband radar according to claim 3 is characterized in that: The method further comprises the steps of: When the short-time energy of the chest micro-motion signal within a set number of consecutive SSA sliding windows is higher than the first energy threshold and the main frequency accounts for less than 50%, the first energy threshold is updated to the average signal energy of the chest micro-motion signal within a set number of consecutive SSA sliding windows.

6. The human heartbeat detection method based on ultra-wideband radar according to claim 3 is characterized in that: The method further comprises the steps of: When the short-time energy of the chest micro-motion signal within a set number of consecutive SSA sliding windows is lower than the second energy threshold and the main frequency accounts for more than 70%, the second energy threshold is updated to the average signal energy of the chest micro-motion signal within a set number of consecutive SSA sliding windows.

7. The human heartbeat detection method based on ultra-wideband radar according to any one of claims 1 to 6, characterized in that: The basic detection algorithm includes improved singular spectrum analysis and variational mode decomposition, and the high-order detection algorithm includes improved singular spectrum analysis, variational mode decomposition and frequency domain enhancement; wherein the frequency domain enhancement process includes: The Morlet wavelet is used to perform time-frequency decomposition on the signal after singular value reconstruction to obtain a high-resolution time-frequency diagram; Compressing the energy of the high-resolution time-frequency graph along the direction of the instantaneous frequency derivative to obtain a high-resolution time-frequency graph after frequency redistribution; The signals in the frequency band of 0.8 Hz to 2.5 Hz on the high-resolution time-frequency diagram after frequency redistribution are retained and the remaining signals are set to zero to complete the extraction of the heartbeat frequency band; The designed IIR notch filter is used to adaptively filter out the intrusive respiratory harmonic frequency from the signal extracted from the heartbeat frequency band; the center frequency of the IIR notch filter is the dynamically detected respiratory harmonic frequency; The time-domain heartbeat signal obtained after the variational mode decomposition is replaced by the time-domain heartbeat signal after the intrusive respiratory harmonic frequency is filtered out for output.

8. A human heartbeat detection device based on ultra-wideband radar, characterized in that: include: A pre-detection module is used to perform pre-detection on a detected area of ​​a detected target using an ultra-wideband radar, and according to the pre-detected radar echo indication, the ultra-wideband radar is moved to a calibrated distance point of the detected area and then locked; the ultra-wideband radar is a radar of a pan-tilt base, and the detected area is a human chest cavity; A loss table lookup module, used to look up a preset energy thickness table to determine the clothing thickness interval of the measured area according to the pre-detected radar echo energy loss; the energy thickness table includes a mapping between different echo energy losses and clothing thickness intervals; An algorithm switch module, used to call a matching heartbeat detection algorithm from a detection algorithm library according to the clothing thickness interval and determine whether to turn on the SSA window adaptive switch; The heartbeat detection algorithm includes a basic detection algorithm and a high-order detection algorithm; A micro-motion extraction module is used to receive the detection echo of the measured area to the ultra-wideband radar and use the background difference method to suppress static clutter on the detection echo, and then extract the chest micro-motion signal of the measured area from the detection echo by using the heartbeat detection algorithm; A signal processing module, used for performing trajectory matrix construction, singular value reconstruction and variational mode decomposition on the chest micro-motion signal through the heartbeat detection algorithm; wherein, if the SSA window adaptive switch is turned on, the SSA sliding window is adjusted according to the set SSA window adaptive adjustment strategy, otherwise the SSA sliding window is maintained unchanged; The detection output module is used to inversely transform the frequency domain heartbeat signal after variational mode decomposition into a time domain heartbeat signal output.

9. The human heartbeat detection device based on ultra-wideband radar according to claim 8, characterized in that: The algorithm switch module turns on the SSA window adaptive switch when a high-order detection algorithm is matched in the clothing thickness interval from the detection algorithm library.

10. A human heartbeat detection device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the human heartbeat detection method based on ultra-wideband radar as described in any one of claims 1 to 7 are implemented.

Images