Vital sign monitoring system for detecting multiple vital signs of multiple persons in a scene and method therefor

Abstract

The present disclosure provides a vital sign monitoring system for detecting vital signs of multiple persons in a scene, comprising a radar transceiver, a signal processing unit, a biometric processing unit, and a vital sign estimation unit. The radar transceiver is configured to transmit radar signals to the scene and receive reflected signals corresponding to the multiple persons. The signal processing unit is configured to process the reflected signals to obtain phase signals corresponding to the multiple persons, and apply wavelet transform to the phase signals using adjustable wavelet parameters to generate a respiration rate signal and a heartbeat rate signal corresponding to each of the multiple persons. The biometric processing unit is configured to process the heartbeat rate signal of each of the multiple persons using a biometric model to determine a blood pressure signal of each of the multiple persons.

Description

Technical Field

[0001] This disclosure relates to a vital signs monitoring system, and more particularly to a vital signs monitoring system for detecting multiple vital signs in multiple people in a scenario. Background Technology

[0002] Current vital sign detection technologies are widely used in health monitoring, smart healthcare, home care, and disaster relief. Among these, non-contact vital sign measurement methods (such as respiratory rate, heart rate, and blood pressure) have attracted significant attention due to their low invasiveness and convenience. However, most existing non-contact vital sign monitoring systems rely on single-point radar or optical sensors (such as photographic imaging) to acquire physiological signals and are primarily designed for single-person scenarios. Therefore, in complex environments with multiple people present, such systems often prove ineffective.

[0003] In multi-person environments, weak physiological signals (such as chest displacement) generated by different individuals can interfere with each other. Furthermore, natural and involuntary body movements, such as random body movements (RBM), introduce additional signal variability, severely reducing detection accuracy. Environmental factors such as background noise, signal reflection, and obstruction further weaken the stability and reliability of detection. Existing technologies often struggle to distinguish between multiple vital signs, leading to inaccurate physiological parameter analysis results in multi-person scenarios.

[0004] Furthermore, signal strength and resolution tend to decrease with increasing distance, making it difficult to maintain accurate physiological data acquisition in wide or open spaces. For blood pressure (BP) estimation, traditional systems often rely on indirect inference from thoracic displacement phase signals. However, natural breathing or thoracic movements induced by the RBM (Reaching-Bridge Muscle Mass) introduce significant noise into the data, thereby reducing the accuracy and consistency of blood pressure estimation.

[0005] On the other hand, some systems employ camera-based image processing methods to analyze vital signs. While these methods may possess a degree of accuracy, their effectiveness is highly dependent on lighting conditions and camera angle, and can be negatively impacted by skin color differences and visual impairments. Furthermore, the use of optical imaging technologies raises privacy concerns, particularly in public places or home environments, thus limiting the widespread adoption of such solutions.

[0006] Therefore, there is an urgent need for an innovative technology that can overcome the above limitations. Summary of the Invention

[0007] This disclosure provides a vital signs monitoring system for detecting multiple vital signs in a scene. The vital signs monitoring system includes a radar transceiver, a signal processing unit, a biometric processing unit, and a vital signs estimation unit. The radar transceiver is configured to transmit radar signals to the scene and receive reflected signals corresponding to multiple individuals. The signal processing unit is configured to process the reflected signals to obtain phase signals corresponding to multiple individuals, and to apply wavelet transform to the phase signals using adjustable wavelet parameters to generate respiratory rate and heart rate signals for each individual. The biometric processing unit is configured to process the heart rate signal of each individual in the multiple individuals using a biometric model to determine the blood pressure signal of each individual. The vital signs estimation unit is configured to estimate the respiratory rate, heart rate, and blood pressure values ​​of each individual in the multiple individuals based on their respective respiratory rate, heart rate, and blood pressure signals.

[0008] In one embodiment, the signal processing unit includes a two-dimensional positioning unit, a DC offset correction unit, and a phase demodulation unit. The two-dimensional positioning unit is configured to apply a fast Fourier transform to the reflected signal to obtain range and azimuth information, and to apply a beamformer based on the range and azimuth information to determine the enhanced signal for each person in the multi-person group. The DC offset correction unit is configured to compensate for the phase offset of the enhanced signal. The phase demodulation unit is configured to extract the phase signal from the phase-offset-compensated enhanced signal.

[0009] In one embodiment, the signal processing unit further includes a signal preprocessing unit and a clutter suppression unit. The signal preprocessing unit is configured to normalize the reflected signal. The clutter suppression unit is configured to remove ambient clutter from the normalized reflected signal. The two-dimensional positioning unit receives the normalized reflected signal from the clutter suppression unit, and the normalized reflected signal has been cleared of ambient clutter.

[0010] In one embodiment, the signal processing unit is further configured to decompose the phase signal into a resonant component with cardiopulmonary signals based on wavelet transform, and the phase signal exhibits sparse characteristics in the wavelet domain.

[0011] In one embodiment, the signal processing unit is further configured to determine an optimal wavelet basis for each of the multiple individuals, based on the optimal value of adjustable wavelet parameters matched with the cardiopulmonary signal, and to reconstruct the respiratory waveform and heartbeat waveform from the cardiopulmonary signal based on the optimal wavelet basis.

[0012] In one embodiment, the adjustable wavelet parameters include a Q-factor, a redundancy factor, and a decomposition level.

[0013] In one embodiment, the Q factor evaluates the resonance quality of the phase signal, and the redundancy factor controls the frequency response overlap between adjacent wavelets, and the decomposition hierarchy determines the frequency span of adjacent wavelets.

[0014] In one embodiment, the signal processing unit is further configured to determine the optimal value of the Q factor based on the maximum reconstruction energy of the cardiopulmonary signal.

[0015] In one embodiment, the respiratory waveform and the heartbeat waveform are reconstructed based on the optimal value of the Q factor, and the center frequencies corresponding to the respiratory waveform and the heartbeat waveform fall within the frequency range of the respiratory waveform and the frequency range of the heartbeat waveform, respectively.

[0016] In one embodiment, the biometric processing unit uses a biometric model to determine the blood pressure signal based on the heartbeat waveform, and the vital signs estimation unit estimates the respiratory rate value based on the respiratory waveform, and estimates the heart rate value and blood pressure value based on the heartbeat waveform.

[0017] In one embodiment, the biometric model is a two-element Windkessel model.

[0018] Embodiments of this disclosure further provide a method for detecting multiple vital signs in a scene involving multiple individuals. The method includes transmitting radar signals to the scene via a radar transceiver and receiving reflected signals corresponding to multiple individuals, and performing the following steps via a processor. The steps include processing the reflected signals to obtain phase signals corresponding to multiple individuals; applying wavelet transform to the phase signals using adjustable wavelet parameters to generate respiratory rate and heart rate signals for each individual; processing the heart rate signals for each individual using a biometric model to generate blood pressure signals for each individual; and estimating the respiratory rate, heart rate, and blood pressure values ​​for each individual based on their respective respiratory rate, heart rate, and blood pressure signals.

[0019] In one embodiment, the step of processing the reflected signal to obtain a phase signal corresponding to multiple people includes: applying a fast Fourier transform to the reflected signal to obtain range and azimuth information; applying a beamformer based on the range and azimuth information to determine an augmented signal for each of the multiple people; compensating for the phase shift of the augmented signal; and extracting the phase signal from the augmented signal compensated for the phase shift.

[0020] In one embodiment, the step of processing the reflected signal to obtain a phase signal corresponding to multiple individuals further includes: normalizing the reflected signal; and removing ambient clutter from the normalized reflected signal. The step of removing ambient clutter is performed before performing a fast Fourier transform to obtain range and azimuth information.

[0021] In one embodiment, the step of processing the reflected signal to obtain a phase signal corresponding to multiple people further includes: decomposing the phase signal into a resonant component with cardiopulmonary signals, and the phase signal being sparsely distributed in the wavelet domain based on wavelet transform.

[0022] In one embodiment, the step of applying wavelet transform to the phase signal using adjustable wavelet parameters to generate respiratory rate and heart rate signals for each person includes: determining an optimal wavelet basis for each person based on the optimal value of the adjustable wavelet parameters matched with the cardiopulmonary signal; and reconstructing the respiratory waveform and heart rate waveform from the cardiopulmonary signal based on the optimal wavelet basis.

[0023] In one embodiment, the step of applying wavelet transform to the phase signal using adjustable wavelet parameters to generate respiratory rate and heart rate signals for each person further includes: determining the optimal value of the Q factor based on the maximum reconstruction energy of the cardiopulmonary signal.

[0024] In one embodiment, the step of processing the heart rate signal of each individual among multiple people using a biometric model to generate a blood pressure signal for each individual includes: determining a blood pressure value based on a heart rate waveform using the biometric model. The step of estimating the respiratory rate, heart rate, and blood pressure values ​​for each individual among multiple people based on their respiratory rate, heart rate, and blood pressure signals includes: estimating the respiratory rate and heart rate values ​​based on the respiratory waveform and heart rate waveform, respectively.

[0025] Embodiments of this disclosure further provide a vital signs monitoring system for detecting multiple vital signs in a scene. The vital signs monitoring system includes a radar transceiver, a processor, and a storage unit. The radar transceiver is configured to transmit radar signals to the scene and receive reflected signals corresponding to multiple individuals. The storage unit is coupled to the processor and configured to store a computer program containing instructions. When the computer program is executed by the processor, the processor can: process the reflected signals to obtain phase signals corresponding to multiple individuals, and perform wavelet transformation on the phase signals using adjustable wavelet parameters to generate respiratory rate and heart rate signals for each individual; process the heart rate signals of each individual using a biometric model to determine the blood pressure signals of each individual; and estimate the respiratory rate, heart rate, and blood pressure values ​​of each individual based on their respective respiratory rate, heart rate, and blood pressure signals.

[0026] In one embodiment, based on wavelet transform, the phase signal is decomposed into a resonant component with cardiopulmonary signals, and the phase signal exhibits sparse characteristics in the wavelet domain. Adjustable wavelet parameters include a Q-factor, a redundancy factor, and a decomposition level. The Q-factor evaluates the resonant quality of the phase signal, the redundancy factor controls the frequency response overlap between adjacent wavelets, and the decomposition level determines the frequency span between adjacent wavelets.

[0027] The various embodiments disclosed herein provide a system capable of simultaneous multi-user detection in shared environments. This system combines several key technological advantages to address the challenges of non-contact vital sign monitoring in the real world. The system exhibits strong robustness to background clutter and random body motion, enables long-distance monitoring, and accurately estimates vital signs such as blood pressure, heart rate, and respiratory rate at the same distance. Therefore, the proposed system significantly expands the practical applicability, reliability, and scalability of non-contact vital sign detection technology. It is particularly suitable for fields such as intelligent medical systems, remote patient monitoring, environmentally assisted living, sleep health analysis, and group health and safety monitoring. These advancements mark a substantial step forward in achieving high-fidelity, multi-user physiological perception in unconstrained real-world environments. Attached Figure Description

[0028] This disclosure can be more fully understood by referring to the following detailed description and examples, as well as the accompanying drawings.

[0029] Figure 1 This is a schematic diagram of a vital signs monitoring system according to an embodiment of the present disclosure.

[0030] Figure 2 This is a flowchart of a method for detecting multiple vital signs in a scene according to an embodiment of the present disclosure.

[0031] Figure 3 For illustrative purposes, embodiments of the present disclosure are provided. Figure 1 The diagram shows a MIMO radar architecture based on FMCW for the radar transceiver shown.

[0032] Figure 4 According to embodiments of this disclosure Figure 1 The functional block diagram of the signal processing unit shown is shown below.

[0033] Figure 5A To present a demonstrative range profile spectrum before interference suppression.

[0034] Figure 5B To present an exemplary range profile spectrum after interference suppression.

[0035] Figure 6A This is a schematic diagram illustrating the application of circular fitting to phase data before and after DC offset correction, according to an embodiment of the present disclosure.

[0036] Figure 6B This is a schematic diagram illustrating the extracted phase information according to an embodiment of the present disclosure.

[0037] Figure 7 To present an exemplary reconstructed heartbeat waveform according to an embodiment of the present disclosure.

[0038] Figure 8 This is a system block diagram of a vital signs monitoring system according to an embodiment of the present disclosure.

[0039] List of reference numerals

[0040] 100: Vital Signs Monitoring System

[0041] 102: Radar transceiver

[0042] 104: Signal Processing Unit

[0043] 106: Biometric Processing Unit

[0044] 108: Vital Signs Estimation Unit

[0045] 110: Reflected signal

[0046] 112: Heart rate signal

[0047] 114: Blood Pressure Signal

[0048] 116: Respiratory rate signal

[0049] 118: Heart rate signal

[0050] 120: Blood pressure value

[0051] 122: Respiratory rate value

[0052] 124: Heart rate value

[0053] 200: Method

[0054] S202~S210: Steps

[0055] 300: Tx antenna

[0056] 302: Rx antenna

[0057] 304: Person p

[0058] 306: Chirp signal

[0059] 308: Chirp signal

[0060] 310: Equivalent Virtual Array

[0061] θ p azimuth

[0062] R p,0 Radial distance

[0063] dr: Antenna spacing

[0064] dt: Antenna spacing

[0065] λ: wavelength

[0066] B: Bandwidth

[0067] T s Slow time chirp interval

[0068] τ: Time delay

[0069] 400: Signal Preprocessing Unit

[0070] 402: Clutter Suppression Unit

[0071] 404: Two-dimensional positioning unit

[0072] 406: DC offset correction unit

[0073] 408: Phase demodulation unit

[0074] 410: Phase signal

[0075] 412: Cardiopulmonary signals

[0076] 414: Respiratory rate signal

[0077] 416: Heart rate signal

[0078] O31~O38: Operation

[0079] 500A: Range profile spectrum before clutter suppression

[0080] 500B: Range profile spectrum after clutter suppression

[0081] 502~504: Environmental Noise

[0082] 506~508: Human target signals

[0083] 600A: Circular Fitting

[0084] 600B: Phase Information

[0085] 602: Gray dot

[0086] 604: Black dot

[0087] 606: Triangle point

[0088] 608: Square dot

[0089] 610: Original Phase

[0090] 612: Phase after expansion processing

[0091] 700: Reconstructing Heartbeat Waveforms

[0092] τ fThe duration of the current heartbeat's contractions.

[0093] τ r : Duration of diastolic heartbeat

[0094] T: Complete heartbeat cycle time

[0095] 800: Vital Signs Monitoring System

[0096] 802: Radar transceiver

[0097] 804: Processor

[0098] 806: Storage unit

[0099] 808: Computer program Detailed Implementation

[0100] The following description is intended to illustrate the basic principles of this disclosure and should not be construed as limiting its scope. The scope of this disclosure should be determined with reference to the claims.

[0101] In the following embodiments, the same reference numerals represent the same or similar elements or components.

[0102] The ordinal numbers used in the claims, such as "first," "second," "third," etc., are for illustrative purposes only and do not imply any priority relationship between them.

[0103] The descriptions provided below for embodiments of the apparatus or system are equally applicable to embodiments of the method, and vice versa.

[0104] Figure 1 This is a schematic diagram of a vital signs monitoring system 100 according to an embodiment of the present disclosure. Figure 1 As shown, the vital signs monitoring system 100 includes a radar transceiver 102, a signal processing unit 104, a biometrics processing unit 106, and a vital signs estimation unit 108.

[0105] The radar transceiver 102 may include one or more radio frequency (RF) front-end modules, analog filters, frequency synthesizers, mixers, analog-to-digital converters (ADCs), and digital signal processing units, configured to transmit radar signals and receive reflected signals reflecting physiological activities.

[0106] The signal processing unit 104, biometric processing unit 106, and vital sign estimation unit 108 can be implemented using dedicated hardware logic, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or discrete hardware components. In some embodiments, two or more of the signal processing unit 104, biometric processing unit 106, and vital sign estimation unit 108 can be integrated into a single hardware module or incorporated into a system-on-chip (SoC) architecture to achieve performance improvements, size reduction, and power consumption reduction.

[0107] Figure 2 This is a flowchart of a method 200 for detecting multiple vital signs in a scene according to an embodiment of the present disclosure. Figure 2 As shown, method 200 may include steps S202 to S210. These steps are performed by... Figure 1 The components shown are executed. Therefore, for a deeper understanding of this embodiment, reference should also be made to... Figure 1 and Figure 2 .

[0108] In step S202, the radar transceiver 102 transmits radar signals to the scene and receives reflected signals corresponding to multiple people. The radar signals may include frequency-modulated continuous wave (FMCW) signals, pulse radar signals, or other forms of millimeter wave or microwave signals. The scene may include one or more people, such as multiple users in an indoor environment, individuals in a vehicle, passengers in a public transportation vehicle, patients recuperating in a clinical care area, or potential survivors buried under collapsed rubble after an earthquake, but this disclosure is not limited to these.

[0109] When a transmitted radar signal strikes the surface of a human body, it generates a reflected signal. The radar transceiver 102 is further configured to receive these reflected signals and convert them into digital signals for subsequent processing. The received reflected signals may originate from different individuals and may exhibit different time delays, Doppler shifts, or reflection intensities; these characteristics can be used for subsequent target detection, tracking, and classification.

[0110] In some embodiments, the radar transceiver 102 includes multiple antenna array elements to achieve beamforming and spatial resolution capabilities. Specifically, the radar transceiver 102 is implemented as a Multiple-Input Multiple-Output (MIMO) radar, including multiple transmit antennas and multiple receive antennas. MIMO radar refers to a radar system architecture that uses multiple sets of transmit (Tx) and receive (Rx) antennas to form a virtual array, significantly enlarging the aperture. By independently controlling the signals transmitted by each Tx antenna and coherently combining the signals received by the Rx antennas, MIMO radar can synthesize more virtual channels, thereby improving angular resolution, target detection capabilities, and spatial versatility.

[0111] In some embodiments, MIMO radar achieves spatial multiplexing and high-resolution imaging by utilizing a virtual antenna array generated by combining Tx and Rx elements. This configuration significantly enhances the radar's ability to detect, separate, and track multiple targets in complex environments, and enables the system to distinguish reflected signals from different individuals based on angular information such as azimuth and elevation angles. The system can work in conjunction with digital signal processing circuitry to perform range estimation, velocity estimation, and incident angle estimation, thereby generating occupancy maps or movement trajectory information related to the human body.

[0112] Reference Figure 3 , Figure 3 For the illustration of embodiments according to this disclosure Figure 1 The diagram illustrates an FMCW-based MIMO radar architecture for the radar transceiver 102 shown. In this embodiment, the FMCW-based MIMO radar architecture is configured to perform simultaneous multi-target tracking and spatial resolution. The FMCW-based MIMO radar architecture includes: a uniform linear array (ULA) consisting of N Tx antennas 300, with an antenna spacing of dt; and a ULA consisting of M Rx antennas 302, with an antenna spacing of dr. The Tx antennas 300 are numbered sequentially as n = 0, 1, ..., N. 1. The Rx antennas 302 are numbered sequentially as m = 0, 1,..., M 1, where N and M are positive integers.

[0113] Assume there are P personnel within the radar's field of view, each numbered sequentially as p=1, ..., P. The p-th personnel, 304, is located at a radial distance R from the radar origin. p,0 At this location, the azimuth angle is θ p The chirped signal 306 transmitted by the nth Tx antenna is defined as follows: s n (t)=A t ej2π (f0t+B / 2Tt 2 + n,0 (t)+u n / λ sin(θ p Equation (1) Where A t Let f0 be the amplitude of the chirp signal 306, f0 be the starting frequency of the chirp signal 306, B be the bandwidth of the chirp signal 306, and T be the duration of the chirp signal 306. n,0 (t) represents the initial phase of the nth transmitter, u n Let θ be the position of the nth Tx antenna in the array, λ be the wavelength of the chirped signal 306, and θ be the position of the nth Tx antenna in the array. p Let 304 be the direction angle of the p-th person.

[0114] The chirped signal 306 integrates time-domain frequency modulation and spatial-domain direction coding to achieve joint distance-angle resolution.

[0115] The chirped signal 308, transmitted by the nth Tx antenna and reflected by all targets, is received by the mth Rx antenna. Its equation is expressed as: x m,n (t)= m,n (p) s(t τ m,n (p) (t))+ m,n (t) Equation (2) Where α m,n (p) Let τ be the reflection coefficient of the p-th person 304 on the (n,m)Tx-Rx antenna pair. m,n (p) (t) represents the time delay corresponding to the p-th person, 304, while m,n (t) represents clutter and noise components.

[0116] The chirp signal 308 can take into account multipath propagation and directional delay, accurately representing the mixed reflection phenomenon in multi-person scenarios. In other words, the chirp signal 308 corresponds to multiple targets.

[0117] Through this FMCW-based MIMO radar architecture, the system achieves high-resolution estimation of human position and motion information by using a MIMO configuration of multiple Tx and Rx antennas combined with frequency-modulated chirped waves.

[0118] Refer again Figure 1 When radar transceiver 102 receives the corresponding Figure 3 After the reflected signal 110 of the chirped signal 308, the signal processing unit 104 is configured to process the reflected signal 110. For example... Figure 2 As shown, in step S204, the signal processing unit 104 is configured to process the reflected signal 110 to obtain the phase signal corresponding to multiple people.

[0119] Reference Figure 4 , Figure 4 As shown in the embodiments of this disclosure Figure 1 The functional block diagram of the signal processing unit 104 is shown below. The signal processing unit 104 includes a signal preprocessing unit 400, a clutter suppression unit 402, a two-dimensional positioning unit 404, a DC offset correction unit 406, and a phase demodulation unit 408.

[0120] In operation O31, the signal preprocessing unit 400 is configured to perform de-ramp processing and in-phase / quadrature (I / Q) sampling to normalize the reflected signal 110. The normalized reflected signal for the k-th ADC sample of the l-th chirped signal can be expressed as: x m,n (k,l)= j2π {f b,p kT f +λ / 2(R p,0 +R p (lT s ))+(u n +u m ) / λsin(θ p )}+δ m,n (kT f ,lT s Equation (3) Where f b,p =2BR p (t) / cT is the difference frequency associated with the p-th person 304, R p (lT s ) represents the slow-time chest displacement caused by cardiopulmonary activity in person p, number 304. f For fast-time ADC sampling interval, T s The slow-time chirp interval is λ, where λ is the wavelength and δ is the lattice. m,n (kT f , lT s The ) represents the corresponding clutter and noise components.

[0121] According to the above equation (3), the phase shift of the p-th person 304 in the (n,m)-th Tx-Rx antenna pair can be expressed as: m,n (l)= m,n (R p,0 ,θ p )+4πR p (lT s Equation (4) in m,n (R p,0 ,θ p )=2π / λ{2R p,0 +(u n +u m sin(θ) p The )} represents the phase shift induced by the relative position of the p-th personnel 304 and the radar transceiver 102. Note that... m,n (R p,0 ,θ p The property is time-invariant because the target object (i.e., the p-th person 304) remains within the reference range (radial distance) R. p,0 And 4πR p (lT s The value of λ is primarily attributed to the cardiopulmonary activity of the human target and can be used to extract estimates of respiratory rate (RR) and heart rate (HR).

[0122] In other words, m,n (R p,0 ,θ p This corresponds to the static phase shift induced by the target's position and azimuth, while 4πR p (lT s ) / λ represents the dynamic phase change caused by thoracic cavity movement. Because m,n (R p,0 ,θ p It has time-invariant properties and can be filtered out using differential methods or spatial filtering techniques, thereby preserving the dynamic components that carry the characteristics of cardiopulmonary signals, i.e., 4πR. p (lT s ) / λ.

[0123] Next, the method proceeds to operation step O32. In operation step O32, clutter suppression unit 402 is configured to eliminate ambient clutter from the normalized reflection signal. To generate the range profile spectrum, x... m,n The fast time exponent k of (k,l) is subjected to a K-point Fast Fourier Transform (FFT), as shown below: X m,n (r,l)=FFT k {x m,n (k,l)}, r=0,…,K Equation (5) Where r represents the distance range index.

[0124] In one embodiment, there exists a scenario involving two human targets located at the same radial distance (e.g., approximately 1.59 meters). Figure 5A As shown, the signals of the two human targets were observed to superimpose with strong environmental noise in the same region. Figure 5A The example distance profile spectrum of 500 Å before noise suppression is presented; this phenomenon will severely affect positioning accuracy and cardiopulmonary signal extraction. (Refer to...) Figure 5A The range profile spectrum 500A before clutter suppression includes environmental noise 502 and environmental noise 504, as well as the human target signal 506. To eliminate environmental noise 502 and environmental noise 504 (which remain time-invariant over a slow time scale l), a sample moving average (SMA) filter is used, defined as follows: m,n (k,l)=x m,n (k,l)–1 / W m,n Equation (6) (k,q) Where W is the length of the slow time window.

[0125] Figure 5B Presents a demonstrative range profile spectrum of 500B after clutter suppression. For example... Figure 5B As shown, the range profile spectrum 500B after clutter suppression contains only the human target signal 508. Figure 5B The display shows that after clutter suppression, environmental clutter 502 and environmental clutter 504 have been effectively suppressed, thus facilitating the subsequent identification of target distance intervals. In other words, operation step O32 can effectively remove static background components while preserving dynamic changes related to cardiopulmonary signals.

[0126] Next, the method proceeds to operation O33. In operation O33, the two-dimensional positioning unit 404 is configured to acquire range and azimuth information from the normalized reflected signal after ambient clutter has been eliminated, and to determine the enhancement signal based on this range and azimuth information. It should be noted that... Figure 3 The MIMO radar architecture based on FMCW shown can be constructed using Time-Division Multiplexing (TDM) technology to have M... v=MN is the equivalent virtual array 310 of elements. This equivalent virtual array 310 achieves higher angular resolution without the need for additional physical antennas. Therefore, the signal in the target range interval on all virtual elements can be summarized as M v The equation for a ×1 vector is as follows: x(l)=[ 0,0 (l), 0,1 (l),…, M-1,N-1 (l)] T Equation (7) in m,n (l) represents the distance profile signal corresponding to the target distance interval of the (n,m)th channel pair for the l-th chirp signal, while ( ) T This represents the transpose operator.

[0127] Referring to equation (7), an FFT is then performed across the virtual array dimension to obtain the orientation information of individual individuals. This provides the distance and orientation information for each human target, enabling the system to determine the orientation of each target. Furthermore, this distance and orientation information can be used to separate and subsequently extract accurate and reliable individual RR and HR estimates.

[0128] To improve spatial resolution and suppress off-axis interference, a Capon beamformer is then used to determine the enhancement signal, as shown in the following equation: y p (l)=( x 1 a( p ) / a H ( p ) x 1 a H ( p )) H x(l) Equation (8) in p Defined as the estimated direction corresponding to the p-th person, 304. x = (l)x H (l) represents the covariance matrix of the virtual array output, while a( p ) represents a pointer p The array steering vector of the direction. Equation (8) realizes two-dimensional positioning of distance and azimuth, enabling subsequent algorithms to separate the signals of each human target and reliably extract individual RR and HR estimates, even when human targets overlap or are very close together.

[0129] Next, the method proceeds to operation step O34. In operation step O34, the DC offset correction unit 406 is configured to compensate for the phase offset of the enhanced signal. To extract the accurate cardiopulmonary signal 412 from an individual target, the residual distortion of the beamforming signal caused by static clutter coupling and spectral leakage must be addressed. Specifically, the enhanced signal y output by the Carpenter beamformer... p (l) Since DC offset often presents intermodulation interference and center point drift in the complex plane, such distortion may distort the phase information related to RR and HR estimation.

[0130] To alleviate this problem, this disclosure employs a DC offset correction method constructed in the form of a circular fitting problem. Figure 6A This illustration shows a schematic diagram of applying a circular fitting 600A to phase data before and after DC offset correction, according to an embodiment of the present disclosure. Figure 6A As shown, the circular fitting 600A includes gray point 602, black point 604, triangular point 606, and square point 608. Gray point 602 indicates the phase trajectory before correction, black point 604 indicates the phase trajectory after correction, triangular point 606 indicates the center point position after phase displacement, and square point 608 indicates the original center point position of the phase.

[0131] The circular fitting problem employs the least squares method to estimate the optimal center point μ of the circular trajectory. p The corrected signal is obtained from the enhanced signal y. p (l) is obtained by subtracting the estimated center point, and its equation is: p (l)=y p (l)-μ p Equation (9) Therefore, as Figure 6A As shown, the center of the corrected circular trajectory is aligned with the origin. Specifically, gray point 602, after correction, is aligned with the center as black point 604, and triangular point 606, after correction, is aligned with the center as square point 608. This indicates that the phase shift caused by DC offset has been effectively compensated, thereby moving the corrected center to the origin.

[0132] Next, the method proceeds to operation step O35. In operation step O35, the phase demodulation unit 408 is configured to derive the phase signal 410 from the phase-shifted enhanced signal. Since the phase shift of the enhanced signal has been corrected, the phase signal 410 of the p-th person 304 can be extracted according to the following equation: p (l)=arctan(R{ p (l)} / I{ p (l)}) Equation (10) Where the symbol R{ } and I{ } represent the real and imaginary parts of a complex quantity, respectively.

[0133] Reference Figure 6B , Figure 6B The schematic diagram shown below, according to an embodiment of this disclosure, illustrates the extracted phase information 600B. The original phase 610 corresponds to the phase signal 410 extracted immediately after the enhanced signal completes phase offset compensation. However, the original phase 610 itself is not smooth and continuous. To address this issue, a phase unrolling operation is applied to the original phase 610 to eliminate 2π discontinuities caused by angular coiling. This process produces a clean and extended phase sequence suitable for subsequent spectral analysis to estimate RR and HR.

[0134] like Figure 6B As shown, the expanded phase 612 exhibits a smooth time-domain waveform that corresponds to cardiopulmonary activity, thus providing a reliable basis for estimating RR and HR frequencies.

[0135] Reference Figure 2 After obtaining the phase signal 410 corresponding to multiple people, the signal processing unit 104 is further configured to apply wavelet transformation to the phase signal 410 using adjustable wavelet parameters in step S206 to generate the RR signal and HR signal for each person.

[0136] Reference Figure 4In operation O36, the signal processing unit 104 is further configured to decompose the phase signal 410 into resonant components containing the cardiopulmonary signal 412 based on wavelet transform, and the phase signal is sparsely distributed in the wavelet domain. Specifically, since the cardiopulmonary signal 412 has non-steady-state characteristics and exhibits both oscillatory and transient behavior, traditional filtering techniques struggle to accurately separate these resonant components from the phase signal 410, which is affected by residual clutter and RBMs. To address this issue, this disclosure provides a Resonance-Based Sparse Separation (RBSS) algorithm based on Tunable Q-Factor Wavelet Transform (TQWT) for reliably reconstructing the cardiopulmonary signal 412 and accurately estimating RR and HR.

[0137] Unlike acoustic system recovery, sparse signal recovery techniques can be used to process the phase signal 410, which is sparse in the wavelet domain based on wavelet transform, thereby separating the cardiopulmonary signal 412 from the phase signal 410, which is affected by residual clutter and RBMs.

[0138] More specifically, the RBSS algorithm utilizes the sparsity of the phase signal 410 in the wavelet domain to effectively separate the respiratory and heartbeat components in the phase signal 410, which is affected by residual clutter and RBMs. This algorithm can be formulated as the following convex optimization problem: p = p TQW 1 (W) + j w j 1. Equation (11) in Let TQW represent the phase signal vector of the p-th person (304). 1 (·) represents the inverse TQWT, and the matrix W = [w1, ..., w J+1 w in ] j Let λ be the wavelet coefficient vector of the j-th sub-band, J be the decomposition level of TQWT, and λ be the frequency response vector. j Let be the normalization parameter of the j-th sub-band.

[0139] TQWT is determined by three key parameters: the Q factor, the redundancy factor r, and the decomposition level J. The Q factor evaluates the resonance quality of the phase signal 410. The redundancy factor r controls the degree of frequency response overlap between adjacent wavelets. The decomposition level J determines the frequency span between adjacent wavelets. Specifically, a higher Q value corresponds to a narrower bandwidth and stronger resonance characteristics, suitable for modeling oscillatory physiological components such as respiratory and heart rates. A higher redundancy factor r value produces greater overlap, thereby improving spectral continuity and resolution during signal reconstruction. The choice of the decomposition level J directly affects the number of sub-bands available for signal decomposition and must be selected to effectively cover the target respiratory and heart rate bands.

[0140] Furthermore, the decomposition level J specifies the total number of sub-bands, and its maximum value is given by the following equation: J max = log(N y / 4(Q+1)) / log((Q+1)r / (Q+1)2r) Equation (12) Where N y The length of phase signal 410, · This is represented as the floor operator. It is particularly important to note that for the phase signal 410, precise selection of the Q-factor value is crucial for achieving the desired decomposition under resonance conditions.

[0141] In one embodiment, since the choice of the Q-factor value significantly affects the decomposition fidelity, the signal processing unit 104 is further configured to determine the optimal value of the Q-factor based on the maximum reconstruction energy of the cardiopulmonary signal 412. The estimated value of the optimal Q-factor is given by the following equation: TQW 1 ( p,vs ) Equation (13) in p,vs These are represented as wavelet coefficients, corresponding to center frequencies falling within the normal RR and HR frequency range.

[0142] In operations O37 and O38, to efficiently solve the convex optimization problem of reference equations (12) and (13), the Split Augmented Lagrangian Shrinkage Algorithm (SALSA) can be used to solve equation (11). This method can achieve high-fidelity reconstruction of the original cardiopulmonary waveform. Based on this, the optimal wavelet basis that best matches the cardiopulmonary signal 412 can be obtained for each subject, thereby achieving reliable reconstruction of the respiratory waveform and the heartbeat waveform. Therefore, the RR signal 414 and the HR signal 416 can be estimated from the dominant peak frequencies in the respiratory segment and the cardiac segment spectrum.

[0143] Therefore, by jointly adjusting these three parameters (i.e., the Q factor, the redundancy factor r, and the decomposition level J), the TQWT framework can adapt to and match the spectral and resonance characteristics of the phase signal 410. This design can select the optimal wavelet basis for sparse reconstruction, accurately estimate the frequency of the cardiopulmonary signal 412, and then generate the RR signal 414 and the HR signal 416.

[0144] Reference Figure 2 In step S208, the biometric processing unit 106 is configured to process the HR signals 112 of each of the multiple objects using a biometric model to determine the BP signal 114 of that object.

[0145] like Figure 7 As shown, Figure 7 An exemplary reconstructed heartbeat waveform 700 according to an embodiment of the present disclosure is presented. In this embodiment, the biometric model is a two-element Windkessel model. The reconstructed heartbeat waveform 700, reconstructed from the HR signal 112, shows that the BP of the p-th person 304 can be estimated using the two-element Windkessel model and the cardiac systolic and diastolic cycles.

[0146] More specifically, the diastolic blood pressure (DBP) and systolic blood pressure (SBP) of the p-th person (304) can be estimated using the following equations: DBP est =DBP0 β (τ f τ f0 ) Equation (14) Where DBP0 represents the diastolic blood pressure reference value obtained through multiple measurements, β represents the attenuation coefficient determined through model fitting, and τ f τ represents the duration of cardiac contraction during the current heartbeat. f0 This is expressed as the average contraction time of the previous heartbeat; PP est =PP0 Equation (15) Where PP represents pulse pressure, PP0 represents the pulse pressure reference value obtained through multiple measurements, T represents the complete heartbeat cycle time (including systole and diastole), and T0 represents the average heartbeat cycle value.

[0147] SBP can be calculated using the following equation: SBP est =DBP est +PP est Equation (16) This technology can achieve dynamic blood pressure estimation using only the phase-analyzed heartbeat waveform extracted from radar measurements, without the need for additional physiological sensors. It is particularly suitable for applications such as remote health monitoring, long-term care systems, and sleep analysis.

[0148] Reference Figure 2 In step S210, the vital signs estimation unit 108 is configured to estimate the RR value 122, HR value 124, and BP value 120 for each subject based on their RR signal 116, HR signal 118, and BP signal 114, respectively. The vital signs estimation unit 108 estimates the RR value 122 based on the respiratory waveform of the RR signal 116, and estimates the HR value 124 and BP value 120 based on the heartbeat waveform derived from the HR signal 118.

[0149] The vital signs estimation unit 108 is operatively coupled to the signal processing unit 104 and the biometrics processing unit 106 to receive the RR signal 116, HR signal 118 and BP signal 114 of each subject, respectively.

[0150] Specifically, the RR value 122 is derived from the respiratory waveform embedded in the RR signal 116. The estimation of the RR value 122 by the vital signs estimation unit 108 may involve frequency domain analysis, peak detection, periodicity analysis, and / or wavelet decomposition to identify the number of respiratory cycles per unit time. The HR value 124 and BP value 120 are derived from the heartbeat waveform embedded in the HR signal 118. The vital signs estimation unit 108 estimates the HR value 124 by analyzing the time intervals between consecutive R waves, S waves, or other ventricular systolic parameters. The vital signs estimation unit 108 extracts physiological timing parameters (such as cardiac contraction time τ). fThe total cardiac cycle duration (T) is used, and a computational model (such as the binary Wendkesell model or other pulse dynamics model) is applied to estimate the BP value 120, which includes both systolic and diastolic blood pressure components.

[0151] By employing the above-mentioned technology, the vital signs estimation unit 108 can accurately, non-contactly, and instantly assess the physiological indicators of multiple individuals without relying on traditional contact sensors (such as electrocardiogram electrodes or blood pressure cuffs), thus demonstrating broad application value in smart healthcare, remote monitoring, sleep analysis, and related fields.

[0152] Reference Figure 8 , Figure 8 This is a system block diagram of a vital signs monitoring system 800 according to an embodiment of the present disclosure. The vital signs monitoring system 800 includes a radar transceiver 802, a processor 804, and a storage unit 806. The radar transceiver 802 is configured to transmit radar signals to the scene and receive reflected signals corresponding to a group of people. The storage unit 806 is coupled to the processor 804 and configured to store a computer program 808.

[0153] Computer program 808 contains instructions that cause processor 804 to perform the following operations: process reflected signal 110 to obtain phase signal 410 corresponding to multiple subjects, and perform wavelet transformation on phase signal 410 using adjustable wavelet parameters to generate RR signal 116 and HR signal 118 for each subject in the multiple subjects; process HR signal 118 of each subject using biometric model 106 to determine BP signal 114 of that subject; and estimate RR value 122, HR value 124 and BP value 120 for each subject based on their RR signal 116, HR signal 118 and BP signal 114.

[0154] The vital signs monitoring system 800 can be implemented using any computing device with processing capabilities, such as a personal computer (e.g., a desktop or laptop computer), a server computer, a mobile device (e.g., a tablet or smartphone), or an embedded system, but this disclosure is not limited thereto.

[0155] Processor 804 may include any one or more general-purpose or special-purpose processors, or any combination thereof, for executing instructions. In a typical embodiment, processor 804 may include a central processing unit (CPU) and a graphics processing unit (GPU), where the GPU is generally more efficient than the CPU in handling machine learning-related tasks. Therefore, task allocation can be based on the characteristics of the CPU and GPU. For example, tasks such as acquiring signal data or communicating with other devices (such as display devices) may be assigned to the CPU, while signal processing and / or model training-related tasks may be delegated to the GPU.

[0156] Storage cell 806 may be any device containing non-volatile memory, such as read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or non-volatile random access memory (NVRAM). Examples of such devices include, but are not limited to, hard disk drives (HDDs), solid-state drives (SSDs), or optical discs, but this disclosure is not limited thereto.

[0157] like Figure 8 As shown, storage unit 806 stores computer program 808, which can be software or firmware, containing a sequence or set of instructions executable by a computer system. Computer program 808 can be written in any or more programming languages, such as Java, C, C#, C++, Python, etc., but this disclosure is not limited thereto. When processor 804 loads computer program 808 from storage unit 806, the instructions will drive processor 804 to execute the methods described in this disclosure to detect multiple vital signs in a scene.

[0158] The various embodiments disclosed herein provide a system capable of simultaneous detection of multiple users in a shared environment. This system integrates several key technological advantages to address the practical challenges of non-contact vital sign monitoring. This system exhibits strong resistance to background clutter and random body movements, enabling long-distance monitoring and accurate estimation of vital signs such as blood pressure, heart rate, and respiratory rate at the same distance. Accordingly, the system disclosed significantly expands the practicality, reliability, and scalability of non-contact vital sign detection technology. It is particularly suitable for fields such as intelligent medical systems, remote patient monitoring, environmentally assisted living, sleep health analysis, and group health and safety monitoring. These technological breakthroughs mark a significant advancement in achieving high-accuracy multi-user physiological sensing in unrestricted real-world environments.

[0159] The preceding paragraphs describe the subject from multiple perspectives. Clearly, the teachings of this specification can be implemented in various ways. Any particular structure or function disclosed in the embodiments is merely representative. Based on the teachings of this specification, those skilled in the art should understand that any disclosed aspect can be implemented independently, or two or more aspects can be combined.

[0160] While this disclosure is illustrative by way of example and preferred embodiments, it should be understood that this disclosure is not limited to the disclosed embodiments. Rather, it is intended to cover various modifications and similar configurations (which will be apparent to those skilled in the art). Therefore, the appended claims should be given the broadest interpretation to cover all such modifications and similar configurations.

Claims

1. A vital sign monitoring system for detecting multiple vital signs in multiple people in a scene, comprising: A radar transceiver is configured to transmit radar signals to the scene and receive reflected signals corresponding to the multiple people. A signal processing unit is configured to process the reflected signal to obtain a phase signal corresponding to the multiple people, and to apply wavelet transformation to the phase signal using adjustable wavelet parameters to generate a respiratory rate signal and a heart rate signal for each of the multiple people. A biometric processing unit is configured to process the heart rate signal of each of the multiple individuals using a biometric model to determine a blood pressure signal of each of the multiple individuals. as well as A vital signs estimation unit is configured to estimate a respiratory rate value, a heart rate value, and a blood pressure value for each individual among the multiple individuals, based on the respiratory rate signal, the heart rate signal, and the blood pressure signal of each individual among the multiple individuals.

2. The vital signs monitoring system as described in claim 1, wherein the signal processing unit comprises: A two-dimensional positioning unit is configured to apply a fast Fourier transform to the reflected signal to obtain range and azimuth information, and to apply a beamformer based on the range and azimuth information to determine the enhanced signal for each of the multiple people. A DC offset correction unit is configured to compensate for a phase offset of the enhanced signal; as well as A phase demodulation unit is configured to extract the phase signal from the enhanced signal compensated for by the phase offset.

3. The vital signs monitoring system as described in claim 2, wherein the signal processing unit further comprises: A signal preprocessing unit is configured to normalize the reflected signal; as well as A clutter suppression unit is configured to eliminate environmental clutter from the normalized reflected signal; The two-dimensional positioning unit receives the normalized reflected signal from the clutter suppression unit, and the normalized reflected signal has been eliminated by environmental clutter.

4. The vital signs monitoring system as claimed in claim 1, wherein the signal processing unit is further configured to decompose the phase signal into a resonant component with cardiopulmonary signals based on the wavelet transform, and the phase signal exhibits sparse characteristics in a wavelet domain.

5. The vital signs monitoring system of claim 4, wherein the signal processing unit is further configured to, for each of the multiple individuals, determine an optimal wavelet basis based on an optimal value of the adjustable wavelet parameter matching the cardiopulmonary signal, and reconstruct a respiratory waveform and a heartbeat waveform from the cardiopulmonary signal based on the optimal wavelet basis.

6. The vital signs monitoring system as described in claim 5, wherein the adjustable wavelet parameters include a Q factor, a redundancy factor, and a decomposition level.

7. The vital signs monitoring system of claim 6, wherein the Q factor evaluates the resonance quality of the phase signal, the redundancy factor controls a frequency response overlap between adjacent wavelets, and the decomposition level determines a frequency span of the adjacent wavelets.

8. The vital signs monitoring system of claim 6, wherein the signal processing unit is further configured to determine the optimal value of the Q factor based on the maximum reconstruction energy of the cardiopulmonary signal.

9. The vital signs monitoring system of claim 8, wherein the respiratory waveform and the heartbeat waveform are reconstructed based on the optimal value of the Q factor, and the center frequencies corresponding to the respiratory waveform and the heartbeat waveform fall within a respiratory waveform frequency range and a heartbeat waveform frequency range, respectively.

10. The vital signs monitoring system of claim 9, wherein the biometric processing unit determines the blood pressure signal based on the heartbeat waveform using the biometric model, and in, The vital signs estimation unit estimates the respiratory rate based on the respiratory waveform, and estimates the heart rate and blood pressure based on the heartbeat waveform.

11. The vital signs monitoring system as described in claim 1, wherein the biometric model is a binary Wendkessel model.

12. A method for detecting multiple vital signs in multiple people in a scene, comprising: A radar transceiver transmits radar signals to the scene and receives reflected signals corresponding to the multiple people. as well as The following steps are performed using a processor: The reflected signal is processed to obtain the phase signal corresponding to the multiple individuals; Wavelet transformation is applied to the phase signal using adjustable wavelet parameters to generate a respiratory rate signal and a heart rate signal for each of the multiple individuals. A biometric model is used to process the heart rate signal of each person in the group to generate a blood pressure signal for each person in the group. as well as Based on the respiratory rate signal, heart rate signal, and blood pressure signal of each individual in the group, estimate the respiratory rate value, heart rate value, and blood pressure value of each individual in the group.

13. The method of claim 12, wherein the step of processing the reflected signal to obtain the phase signal corresponding to the multiple persons comprises: Apply a Fast Fourier Transform to the reflected signal to obtain range and azimuth information; A beamformer is applied based on the distance and azimuth information to determine the enhanced signal for each of the multiple people. Compensate for a phase shift in the enhanced signal; as well as The phase signal is extracted from the enhanced signal that has been compensated for by the phase shift.

14. The method of claim 13, wherein the step of processing the reflected signal to obtain the phase signal corresponding to the multiple persons further comprises: Normalize the reflected signal; as well as Eliminate environmental clutter from the normalized reflected signal; The step of eliminating environmental clutter is performed before the fast Fourier transform is executed to obtain the distance and azimuth information.

15. The method of claim 12, wherein the step of processing the reflected signal to obtain the phase signal corresponding to the multiple persons further comprises: The phase signal is decomposed into a resonant component with cardiopulmonary signals, and the phase signal is sparsely distributed in a wavelet domain based on the wavelet transform.

16. The method of claim 15, wherein the step of applying the wavelet transform to the phase signal using the adjustable wavelet parameters to generate the respiratory rate signal and the heart rate signal for each of the multiple individuals comprises: Based on an optimal value of the adjustable wavelet parameter that matches the cardiopulmonary signal, an optimal wavelet basis is determined for each of the multiple individuals. as well as Based on the optimal wavelet basis, a respiratory waveform and a heartbeat waveform are reconstructed from the cardiopulmonary signal.

17. The method of claim 16, wherein the adjustable wavelet parameters include a Q-factor, a redundancy factor, and a decomposition level.

18. The method of claim 17, wherein the Q factor evaluates the resonance quality of the phase signal, the redundancy factor controls a frequency response overlap between adjacent wavelets, and the decomposition level determines a frequency span of the adjacent wavelets.

19. The method of claim 17, wherein the step of applying the wavelet transform to the phase signal using the adjustable wavelet parameters to generate the respiratory rate signal and the heart rate signal for each of the multiple individuals further comprises: The optimal value of the Q factor is determined based on the maximum reconstruction energy of the cardiopulmonary signal.

20. The method of claim 19, wherein the respiratory waveform and the heartbeat waveform are reconstructed based on the optimal value of the Q factor, and the center frequencies corresponding to the respiratory waveform and the heartbeat waveform fall within a respiratory waveform frequency range and a heartbeat waveform frequency range, respectively.

21. The method of claim 20, wherein: The steps of processing the heart rate signal of each individual in the group using the biometric model to generate the blood pressure signal of each individual in the group include: Based on the heartbeat waveform, the blood pressure value was determined using this biometric model; The steps for estimating the respiratory rate, heart rate, and blood pressure values ​​for each individual in the group, based on the respiratory rate, heart rate, and blood pressure signals of each individual, include: The respiratory rate and heart rate are estimated based on the respiratory waveform and the heart rate waveform, respectively.

22. A vital sign monitoring system for detecting multiple vital signs in multiple people in a scene, comprising: A radar transceiver is configured to transmit radar signals to the scene and receive reflected signals corresponding to the multiple people. One processor; as well as A storage unit coupled to the processor and configured to store a computer program including instructions, which, when executed by the processor, enables the processor to: The reflected signal is processed to obtain the phase signal corresponding to the multiple people, and a wavelet transform is applied to the phase signal using adjustable wavelet parameters to generate a respiratory rate signal and a heart rate signal for each of the multiple people. A biometric model is used to process the heart rate signal of each person in the group to determine the blood pressure signal of each person in the group. as well as Based on the respiratory rate signal, heart rate signal, and blood pressure signal of each individual, estimate the respiratory rate value, heart rate value, and blood pressure value of each individual in the group.

23. The vital signs monitoring system as described in claim 22, wherein: Based on this wavelet transform, the phase signal is decomposed into a resonant component with cardiopulmonary signals, and the phase signal exhibits sparse characteristics in a wavelet domain. The adjustable wavelet parameters include a Q factor, a redundancy factor, and a decomposition level. as well as The Q factor evaluates the resonance quality of the phase signal, the redundancy factor controls the overlap of a frequency response between adjacent wavelets, and the decomposition level determines a frequency span of the adjacent wavelets.

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