Methods, systems, and devices for breathing monitoring

Abstract

To provide methods, systems and devices for breathing detection and monitoring.SOLUTION: The present disclosure includes methods, systems and devices for breathing detection and monitoring, comprising: receiving, via a data interface, a first parameter; receiving, via a receiver, a plurality of signals; generating a channel impulse response from the plurality of signals; selecting a portion of the channel impulse response on the basis of the first parameter; generating a modified signal from the portion of the channel impulse response, where the modified signal is the portion of the channel impulse response with clutter removed; generating a plurality of regression lines from the modified signal; computing a plurality of times on the basis of the plurality of regression lines, where each of the regression lines goes through zero at one of the times; estimating a zero-crossing time on the basis of the plurality of times; and generating a breathing rate estimation value on the basis of the zero-crossing time.SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] Cross - reference to Applications This application claims the benefit and priority of U.S. Provisional Patent Application No. 63 / 604,658, titled "Methods, Systems, and Devices for Respiratory Monitoring," filed on November 30, 2023, which is hereby incorporated by reference in its entirety.

[0002] The present disclosure relates to radar devices, systems, and methods for respiratory detection and monitoring.

Background Art

[0003] Radar systems, including ultra - wideband - based radars, can be used to sense the environment by providing a means to obtain propagation channel measurements. The propagation channel is due to the reflection of transmitted signals in the environment. Channel measurements typically take the form of a set of periodic channel impulse response estimations (CIREs). The complex components (taps) of each CIRE correspond to the propagation delay of the reflected signal and thus to the distance of the reflecting target.

[0004] Small movements of reflecting targets, such as those arising from breathing, result in a change over time of the CIRE taps corresponding to the breathing target distance. Since breathing is typically a nearly periodic operation, these latter CIRE taps can change according to the same pattern. Thus, respiratory detection and monitoring algorithms classically consist of applying any of several frequency analysis methods (short - time Fourier transform, wavelets, MUSIC,...) to all CIRE taps to detect the presence of periodic variations in some of them. This periodic variation indicates the presence of a breathing person and provides an estimate of the respiratory rate.

[0005] However, frequency analysis methods are not robust to the movement of the subject, irregular breathing patterns, or rapid changes in the respiratory rate. Furthermore, they have high complexity due to processing requirements.

[0006] Accordingly, there is a need for improved systems and methods for respiratory monitoring and detection. SUMMARY OF THE INVENTION

[0007] In an exemplary aspect, the present disclosure is directed to a method for respiratory detection and monitoring. The method also includes receiving a first parameter via a data interface, receiving a plurality of signals via a receiver, generating a channel impulse response from the plurality of signals, selecting a portion of the channel impulse response based on the first parameter, generating a modified signal from the portion of the channel impulse response, where the modified signal is a portion of the channel impulse response with clutter removed, generating a plurality of regression lines from the modified signal, calculating a plurality of times based on the plurality of regression lines, where each of the plurality of regression lines passes through zero at one of the plurality of times, estimating a zero-crossing time based on the plurality of times, and generating a respiratory rate estimate based on the zero-crossing time.

[0008] In some aspects, the implementation may include one or more of the following features. The method may include sorting the zero-crossing times based on the direction of change of the modified signal at the zero-crossing times. The cost matrix may be based on the difference between the estimated zero-crossing times and the zero-crossing times stored in tracking. The method may include assigning the estimated zero-crossing times to tracking when gating constraints are met. The channel impulse response is a channel impulse response estimate. The respiratory rate estimate is generated at each of a plurality of time steps. The method may include detecting false zero-crossing detections based on one or more confidence criteria. The clutter may include background reflections in the environment.

[0009] In an exemplary aspect, the present disclosure is directed to a device. The device also includes a receiver, a non-transitory memory storing instructions, and one or more processors. The one or more processors are configured to cause the device to receive a first parameter via a data interface, receive a plurality of signals via the receiver, generate a channel impulse response from the plurality of signals, select a portion of the channel impulse response based on the first parameter, generate a modified signal from the portion of the channel impulse response, where the modified signal is a portion of the channel impulse response with clutter removed, generate a plurality of regression lines from the modified signal, calculate a plurality of times based on the plurality of regression lines, where each of the plurality of regression lines passes through zero at one of the plurality of times, estimate a zero-crossing time based on the plurality of times, and generate a respiration rate estimate based on the zero-crossing time.

[0010] In some aspects, the implementation may include one or more of the following features. The device is further configured such that the one or more processors execute instructions that may include sorting the zero-crossing times based on the direction of change of the modified signal at the zero-crossing times. The one or more processors are further configured to execute instructions that may include tracking the zero-crossing times estimated based on a cost matrix, where the cost matrix is based on the difference between the estimated zero-crossing times and the zero-crossing times stored in the tracking, and assigning the estimated zero-crossing times to the tracking when a gating constraint is satisfied. The channel impulse response is a channel impulse response estimate. The respiration rate estimate is generated at each of a plurality of time steps. The one or more processors are further configured to execute instructions that may include detecting false zero-crossing time detections based on one or more confidence criteria. Clutter may include background reflections in the environment.

[0011] In an exemplary aspect, the present disclosure is a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to receive a first parameter via a data interface, generate a channel impulse response from a plurality of signals, select a portion of the channel impulse response based on the first parameter, generate a modified signal from the portion of the channel impulse response, where the modified signal is a portion of the channel impulse response with clutter removed, generate a plurality of regression lines from the modified signal, calculate a plurality of times based on the plurality of regression lines, where each of the plurality of regression lines passes through zero at one of the plurality of times, estimate a zero-crossing time based on the plurality of times, or generate a respiration rate estimate based on the zero-crossing time.

[0012] In some aspects, the implementation may include one or more of the following features. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to further sort the zero-crossing time based on the direction of change of the modified signal at the zero-crossing time. The instructions, when executed by one or more processors, cause the one or more processors to further track an estimated zero-crossing time based on a cost matrix, where the cost matrix is based on the difference between the estimated zero-crossing time and the zero-crossing time stored in tracking, and to assign the estimated zero-crossing time to tracking when a gating constraint is satisfied. The channel impulse response is a channel impulse response estimate. The respiration rate estimate is generated at each of a plurality of time steps.

[0013] Those skilled in the art will understand the scope of the present disclosure and recognize its additional aspects after reading the following detailed description of the preferred embodiments in connection with the accompanying drawings.

Brief Description of the Drawings

[0014] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate some aspects of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0015]

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[0016] The embodiments described below represent the information necessary for those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. After reading the following description in light of the accompanying drawings, those skilled in the art will understand the concepts of the present disclosure and recognize the application of these concepts not specifically described herein. Of course, these concepts and their applications are included within the scope of the present disclosure and the accompanying claims.

[0017] In this specification, terms such as first, second, etc. can be used to describe various elements, but it should be understood that these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the present disclosure, the first element can be called the second element, and similarly, the second element can be called the first element. The term "and / or" used in this specification includes any and all combinations of one or more of the related listed items.

[0018] The terms used in this specification are only used for the purpose of describing a particular embodiment and are not intended to limit the present disclosure. As used in this specification, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used in this specification, "comprises", "comprising", "includes", and / or "including" identify the presence of the described features, integers, steps, operations, elements, and / or components, but it will be further understood that they do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0019] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Further, the terms used herein are to be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art, and are not to be interpreted in an idealized or overly formal sense unless expressly so defined herein. Additionally, like reference numerals throughout this specification and the drawings indicate like features.

[0020] Of course, the blocks in each figure or flowchart, and combinations of figures or flowcharts, may be implemented by computer program instructions. Since the computer program instructions may be equipped in a processor of a general-purpose computer, a special-use computer, or other programmable data processing device, the instructions executed via the processor of the computer or other programmable data processing device generate means for implementing the functions described in connection with the blocks of each figure or flowchart. Since the computer program instructions may be directed to a computer or other programmable data processing device and stored in a computer-usable or computer-readable memory capable of implementing the functions in a specified manner, the instructions stored in the computer-usable or computer-readable memory may produce a product that includes instructions for implementing the functions described in connection with the blocks of each figure or flowchart. Since the computer program instructions may be equipped in a computer or other programmable data processing device, the instructions that generate a process executed by the computer as a series of operational steps may be executed by the computer or other programmable data processing device, and the instructions that operate the computer or other programmable data processing device may provide steps for implementing the functions described in connection with the blocks of each figure or flowchart.

[0021] Each block may represent a module, segment, or part of code that includes one or more executable instructions for performing a specified logical function. Further, note that in some alternative execution examples, the functions referred to by the blocks may occur in a different order. For example, two blocks shown consecutively may be implemented substantially simultaneously or in the reverse order, depending on the corresponding functions.

[0022] Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. Further, although the respiratory monitoring system is described in relation to the embodiments, as an example, the embodiments may also be applied to other monitoring systems having similar technical backgrounds or features. Further, the embodiments may be modified within such a scope so as not to deviate significantly from the scope of the present disclosure determined by those skilled in the art, and such modifications may be applicable to other systems in addition to the radar.

[0023] Ultra-wideband (hereinafter, "UWB") may refer to a short-range high-speed wireless communication technology that uses a wide frequency band of several GHz or more, low spectral density, and short pulse widths (e.g., 1 nsec to 4 nsec) in the baseband state. UWB may mean the band itself to which UWB communication is applied. UWB may enable a safe and accurate range between devices. Therefore, UWB enables relative position estimation based on the distance between two devices or accurate position estimation of a device based on the distance.

[0024] Further, the following abbreviations may be used throughout. "CIR" for channel impulse response, "CIRE" for channel impulse response estimation, "RFRI" for radar frame repetition interval, "BPM" for breaths per minute, and "STFT" for short-time Fourier transform.

[0025] Embodiments of the present disclosure provide a system and method for respiratory tracking.

[0026] Embodiments of the present disclosure provide a system and method for respiratory detection.

[0027] Embodiments of the present disclosure provide a system and method for determining a respiratory rate.

[0028] Embodiments of the present disclosure provide a system and method for reducing or eliminating false detections and / or loss of a respiratory signal.

[0029] The disclosed system and method can facilitate several improvements. For example, the disclosed system and method for respiratory tracking includes false detections and loss of the respiratory signal and is more robust. For example, the disclosed system and method for respiratory tracking is more robust to irregularities in the respiratory cycle due to the movement of the subject and / or an abnormal respiratory pattern. For example, the disclosed system and method for respiratory tracking can quickly adapt the discrimination switch between true positive and true negative conditions. For example, the disclosed system and method reduces resource consumption compared to conventional frequency conversion-based techniques. In some embodiments described herein, the system and method can be well adapted to detect short apnea.

[0030] FIG. 1 illustrates a monitoring scenario 100 using radar pulses. In some embodiments, the monitoring scenario 100 includes a subject 110, a transmitter 115, and a receiver 120. The subject 110 can breathe at a rate that can change over time. While the subject 110 is breathing, the transmitter 115 can generate pulses at least a portion of which propagate towards the subject 110. The body of the subject can reflect at least a portion of the transmitted pulse 125. The respiration of the subject 100 can introduce a phase difference into the signal measured at the receiver 120. The receiver 120 can receive the pulse 130 scattered from the chest of the subject. For example, a personal portable device can transmit and receive pulses and perform respiratory monitoring through the systems and methods described herein to track those respiratory rates and may be placed next to a person's bed at night.

[0031] In some embodiments, the transmitter 115 may be housed in a device such as a mobile device, a medical device, and / or the like (e.g., similar to the radar-enabled device 800 depicted and described with respect to FIG. 8). The device may be UWB-enabled such that the transmitted pulse 125 is a UWB pulse. The device may be radar-enabled such that the transmitted pulse 125 is a radar pulse. In some embodiments, the pulse may be structured as a frame according to a standard, e.g., FiRa. Similarly, the second device may include a receiver 120. The second device may be UWB-enabled such that it is configured to receive and decode the transmitted UWB pulse 125. The second device may be radar-enabled such that a received pulse 130 including a radar pulse may be received. In some embodiments, both the transmitter 115 and the receiver 120 may be housed within the same device.

[0032] Figures 2A and 2B depict graphs of channel impulse responses. Figure 2A depicts a channel impulse response (CIR) 200. The channel impulse response 200 represents the response of the receiver to the received pulse, e.g., the response of the receiver 120 to the received pulse 130 of FIG. 1. The channel impulse response 200 may include a plurality of path responses 205(a)-(c). Each path response may correspond to a delay based on the path the signal took before reaching the receiver. In the example depicted in FIG. 2A, there are three path responses including the receiver's channel impulse response over time. In some embodiments, the channel impulse response is a digital estimate of the continuous channel impulse response (“CIRE”). References herein to the channel impulse response should be understood to include the CIRE, e.g., the digital channel impulse response, and vice versa as necessary. By way of example, the first path response 205a of the channel impulse response 200 may correspond to a pulse from the transmitter traversing the direct path between the transmitter and the receiver, and the second path response 205(b) of the channel impulse response 200 may correspond to a pulse from the transmitter traversing the path from the transmitter to the target, e.g., the target 110, and then to the receiver. The third path response 203(c) of the channel impulse response may correspond to a much longer path and may include descriptions of scattering from walls or other objects in the room. FIG. 2A is merely illustrative, and there may be more or fewer path responses including the channel impulse response, and the shape of the channel impulse response may vary based on several factors including the physical configuration of the receiver, the environment through which the pulse travels, and / or the types of objects in the environment that reflect the pulse.

[0033] FIG. 2B depicts a graphical representation 250 of a series of channel impulse responses or (CIREs) 260a-c resulting from a series of transmitted pulses by a transmitter having a specified radar frame repetition interval (RFRI) 255. In other words, there is one CIRE per frame and each frame consists of a series of pulses. In some embodiments, as described in FIG. 1, transmitter 115 transmits a sequence of pulses 125 at regular intervals equal to the RFRI, and the receiver generates one channel impulse response 260a-c for each transmission sequence of pulses 125. Sometimes, the RFRI 255 is referred to as the frequency. In some embodiments, the slow time axis 265 measures the time between transmitted pulses. In some embodiments, the fast time axis 270 corresponds to the delays or ranges of the various paths taken by the transmitted pulses, such as the length of the path from the transmitter to target 110 and from the target to the receiver. Each path results in a path response, e.g., one of 205a-c, in the channel impulse response 260a-c. In some embodiments, the fast time axis 270 corresponds to the delay measured from the time each pulse was transmitted by the transmitter. Discoverably, the signal of the path response may vary periodically as a slow function of time, which represents the periodicity of the respiration of the object in the room. The channel impulse responses depicted in FIGS. 2A-2B are merely illustrative. In some embodiments, the signals processed by the respiration monitoring system are digital signals.

[0034] The signal measured by the receiver may be mathematically represented as r(τ,kT as the k-th tap of the CIR s )(measured at time τ (sampling rate T s )) and the sampling rate may be, in some embodiments, from 500 MHz to 10 GHz. In some embodiments, r(τ,kT s ) is a complex number. Let K be the number of complex taps of the CIRE. In some embodiments, the following 2K real CIRE signals are defined to process the real and imaginary parts of each CIRE tap independently.

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[0035] Figure 3 depicts a graph 300 of an exemplary periodic signal 305. The periodic signal 305 is shown as p(τ) in Equation 3 described herein. Markers 310a - f and 315a - f identify the positions where the periodic signal crosses zero. The vertical axis 320 corresponds to signal intensity in arbitrary units. The horizontal axis 325 corresponds to low - speed time, similar to 265 in FIG. 2B, for example. In some embodiments, the respiratory rate can be calculated by tracking the zero of the periodic signal. For the reasons described herein, zero crossings (ZCs) can be distinguished based on whether the periodic signal is increasing or decreasing at the ZC, i.e., the positive or negative slope of the graph. The direction of the ZC path is referred to as the ZC path, i.e., upward (increasing) or downward (decreasing). The upward arrow marked when crossing zero is where the periodic function is increasing at the ZC, and the downward arrow marked when below zero is where the periodic function is decreasing at the ZC. Thus, FIG. 3 depicts an example of p(τ) (over low - speed time τ). Under certain conditions, the delay between two consecutive ZC times proceeding in the same way is proportional to the periodic signal period Λ. In some embodiments, the systems and methods described herein estimate the ZC time and direction of the ZC of the function p(τ), and from this, determine a respiratory rate estimate by tracking the time interval between consecutive ZC events.

[0036] Figure 4 depicts a graph 400 of an example of a noisy periodic signal 405. The noisy periodic signal 405 is the clutter - free signal c shown in Equation 3 described herein. lIt can correspond to (τ). As depicted in FIG. 4, the upper markers 410a - d and the lower markers 415a - e identify the positions where the periodic signal crosses zero. The vertical axis 420 corresponds to the signal intensity in any unit. The horizontal axis 425 corresponds to, for example, the slow time, similar to 265 in FIG. 2B. In some embodiments, the respiratory rate can be calculated by tracking the zero of the periodic signal. As shown, the noise causes multiple zero crossings (ZC) near the ZC of the linear regression line 430. In some embodiments, the ZC of a periodic signal without noise is called the true ZC. FIG. 4 shows a regression line 430 that can be used to approximate the true ZC. For the reasons described herein, the ZC may be distinguished based on whether the periodic signal is increasing or decreasing at the ZC, i.e., the positive or negative slope of the graph. The upper markers 410a - d mark that the slope of the graph is positive at the ZC, and the lower markers 415a - e mark that the slope of the graph is negative at the ZC.

[0037] Over a short period [τ s , τ e (around the ZC event of p(τ)), p(τ) can be approximated by a straight line. Thus, linear regression can be applied to the input signal to estimate the ZC time. The time at which the straight line resulting from the linear regression crosses the zero level is the estimated ZC time of p(τ), and the sign of the slope of the straight line gives the ZC way.

[0038] Figure 5 illustrates a block diagram of a respiratory monitoring system 500 according to some embodiments of the present disclosure. The respiratory monitoring system 500 may include a tap selection block 505, a clutter removal block 510, a regression block 515, a zero crossing event detection block 520, a zero crossing direction detection block 525, an upward tracking block 530, a downward tracking block 535, and a BPM calculation block 540. In some embodiments, the input to the respiratory monitoring system is a digitized CIR estimate and one or more user-provided parameters as described herein. In some embodiments, the respiratory monitoring system receives the CIR estimate as K complex taps at regular intervals (RFRI) Δ. For illustrative purposes, at time (m) = Δm, the k-th complex tap of r(m,k) is referred to. Where k is the fast time index and m is the slow time index. For simplicity, the period Δ is omitted and assumed to be equal to 1, which means a change in units. In some embodiments, the final output of the respiratory monitoring system is a respiratory rate estimate 545. For example, when monitoring a human subject, the respiratory rate estimate can vary by about 15 breaths per minute.

[0039] In some embodiments, the tap selection block 505 selects taps from the K complex taps of the CIR estimate. The tap selection block 505 arranges the tap signals into a vector represented as (4).

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[0040] In some embodiments, the clutter removal block 510 removes a certain background clutter (e.g., C in Equation 2) due to unwanted reverberations in a static environment. The clutter removal is applied independently to each component of the vector of K complex taps in Equation 4, and the components can be referred to as l ). The clutter may first be estimated according to the following equation. [Number] Here, μ(m) is the adaptive algorithm step size or learning rate that determines the convergence rate. The larger the value of μ, the faster the convergence, but the larger the clutter estimation noise. In some embodiments, μ may be constant. In some embodiments, μ may vary with the slow time m. In some embodiments, [Number] where μ ( [Number] (μ 0 is a user-defined parameter) ensures that the convergence rate remains high enough. The output of the clutter removal block 510 is the impulse response from only the target reverberation (e.g., only the pulse reflected by the target 110 in FIG. 1). [Number] This is an example of a method for estimating clutter, and other estimation techniques, such as stochastic gradient descent, are contemplated.

[0041] In some embodiments, the regression block 515 enables the calculation of a regression within a sliding window of the taps selected by the tap selection block 505. In some embodiments, the regression block 515 receives the output of the clutter removal block that includes the clutter-free signal.

[0042] The set {ζ(i)} can be the sequence of all ZC times of p(τ). The u-th

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[0043] In some embodiments, the N+M+1 measurements of the clutter-free signal are

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[0044] In some embodiments, all clutter-free signals c l (τ) have a non-zero oscillation amplitude

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[0045] The above considerations may be readjusted to accommodate the use of multiple tap signals.

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[0046] When the measurement is performed periodically, [Number] , the linear regression matrix Θ(m) can be mathematically expressed as follows. [Number] Therefore, the linear regression result can be obtained by always applying the pseudo-inversion of the matrix (0) and then shifting the result as a function of m. Since the pseudo-inversion matrix coefficients depend only on the fixed parameters M and N, they can be calculated in advance.

[0047] The set of quality indicators for multi-tap signal regression can be mathematically expressed as follows. [Number]

[0048] In some embodiments, the above regression may be continuously applied as a sliding window process, and the ZC time estimate [Number] is calculated for each input vector c l (m). [Number] is [Number] close to one of those, the linear assumption is valid, and [Number] otherwise, [Number] is not valid and must be rejected. In some embodiments, the rejection criterion is based on the following observations.

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[0049] Generally, multiple ZC time estimation values

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[0050]

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[0052] 3. Calculate the slope estimate value, ZC time estimate value, and weight. For slope estimation, it is necessary to consider only the input with the maximum amplitude. This is,

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[0053] Referring to the zero-crossing event detection block 520 and the zero-crossing direction detection block 525, these blocks may be based on the principle of multi-target tracking. Multi-target tracking improves the robustness of the systems and methods described herein against irregularities in the breathing sequence, for example, due to the movement of the subject.

[0054] In some embodiments, the zero-crossing event detection block 220 identifies the ZC estimated value [Number] (corresponding to the true ZC ζ(u)). In some embodiments, this is achieved by adding consecutive ZC estimated values [Number] to a list, provided they are close to the previous ones. When this proximity condition is no longer verified, the list is closed and emptied, and the elements stored in the list are used to calculate the ZC detector output. The ZC detector output is indexed by j. The zero-crossing event detection block 220 includes the original trust criteria that make the system and method robust against false respiration detection and incorrect estimation. The zero-crossing event detection block may output the ZC time Z(j), the ZC slope vector A(j), and the quality indicator / weight W(j). In some embodiments, the weight W(j) is [Number] It is determined from the quality index calculated from

[0055] Figure 6 shows a block diagram for detecting a zero-crossing event. Figure 6 illustrates several steps in a sequential order, although additional steps may be added and the steps may be reordered as shown. For the following description, L is a list and j is the index of the output of the zero-crossing detection block 520. In some embodiments, the system refers to a latency-aware computing device as described in Figure 8.

[0056] In step 605, the system is initialized, L is empty, and j = 1.

[0057] In step 610, the system waits for the next input. In some embodiments, the regression block 515 provides one input per slow-time index m. Each input contains four pieces of information

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[0058] In step 615, the system checks the input validity condition. The input is

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[0059] In step 620, the system checks whether the list L is not empty.

[0060] In step 625, the system checks the list entry condition. The input (i.e., the zero-crossing) is such that the list L is

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[0061] In step 630, the system saves the input to a list, that is, adds the current zero crossing time index m to the list L.

[0062] In step 635, the system checks the termination condition. In some embodiments, the list is closed if no new elements have been added during the last T e time.

[0063] In step 640, the output can be generated from the elements stored in the list L. In some embodiments, the ZC time estimate Z(j) is calculated,

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[0064] In step 645, the system checks the output validity condition. In some embodiments, the output is considered valid if (a) the list contains more than N l elements and W(j)>T o triggers the tracking block, where N l and T o are user-defined parameters.

[0065] In step 650, if the output validity condition is met, the system triggers the tracking block.

[0066] In step 655, the system empties list L and increments the output index j = j + 1.

[0067] Referring to the upper event tracking block 530 and the lower event tracking block 535, these blocks implement multi-target tracking and separately track zero crossings with positive and negative slopes, upper events, and lower events. In the following discussion, since each block 530, 535 has similar processing steps, the subscript / superscript references to upper (u) or lower (d) are suppressed.

[0068] A periodic signal crosses the zero line twice per period, once upward and once downward. At a given ZC time, some inputs may be rising while others are falling. However, the reverse also occurs at the next ZC time. For simplicity, regardless of the true value of the input signal, one of these ZCs is called "upward" and the other is called "downward" per period, but they may proceed in the opposite way.

[0069] The delay between the upward ZC event and the downward ZC event may be different from the delay between downward and upward. For example, breathing may not be symmetric in the time between exhalation and inhalation. Therefore, the upward and downward ZC estimates are processed independently to obtain two period estimates that are combined at the end of the process for the final estimate.

[0070] When a new event occurs, it is first necessary to identify its "upward" or "downward" direction. This is achieved by comparing the

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[0071] In fact, (u) is not at regular intervals and can sometimes disappear, for example, when the object is moving. To respond quickly to all these changes, the processes described herein use multi-target tracking that is robust to the irregularities of the true sequence.

[0072] FIG. 7 shows a block diagram for event tracking according to some aspects of the present disclosure. In some embodiments, the event tracking block 700 includes an assignment block 705, a maintenance block 710, a filtering and prediction block 715, a gating block 720, and a selective output block 725. In some embodiments, the event tracking block 700 may consist of tracking the respiratory period from ZC event estimation.

[0073] Each tracking t (u) is characterized by a set of parameters. [Table 1]

[0074] In some embodiments, the assignment block 705 associates observations with a tracking. The purpose of this block may be to check whether a new observation Z(j) can be assigned to the currently running tracking. If there is no currently running tracking, the block is bypassed. Assigning block 705 may include several steps described below.

[0075] First, block 705 may fill the cost matrix M. In some embodiments, due to some ZC events not being detected, the gap between the current observation and the last measurement may be equal to two or more periods. Thus, in the cost matrix, the distance from the current observation to the "future" ZC time is also considered. u n is the index of the nth currently running tracking, and then [Equation] where p indexes the number of periods.

[0076] Second, block 705 [Number] finds the minimum value and index of

[0077] Third, block 705 checks if the selected value is a gating constraint, i.e., [Number] if so, then next checks that Z(j) and A(j) are assigned to track u n * Otherwise, the observation is not assigned to any of the surviving tracks. [Number] In some embodiments, the track maintenance block 710 modifies the track and / or creates a new track. The tracking maintenance block 710 may include several steps. First, block 710 may create a new track for Z(j) that is not assigned.

[0078] Second, in some embodiments, the tentative track is checked to see if the track has been assigned at least M4T2C times during the last N4T2C events. M4T2C and N4T2C are user-defined parameters. By default, M4T2C = 2 and N4T2C = 3. Third, if the track has not been assigned in T4C2 seconds, the confirmed track is deleted. If the track has not been assigned in T4T2 seconds, the tentative track is deleted. [Table 2] In some embodiments, the filtering and prediction block 715 may be a first-order loop filter and may be applied to the tracking of period estimation. Block 715 may calculate the following.

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[0079] In some embodiments, the gating block 720 may generate a gating value used by the assignment block 705. The gating value is

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[0080] In some embodiments, the selective output block 725 may output, at each time step, the period estimate P x (along with its reliability estimate C x and path vector estimate D x , x = {u, d} being used) depending on which one of the upper or lower blocks is considered. At a given time, the tracking block may be maintaining several ongoing tracks, some of which are in a tentative state and some of which are confirmed. u* is the index of the latest surviving confirmed track, if it exists. Next,

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[0081] In some embodiments, the zero-crossing direction detection block 525 may identify which upper or lower tracking block has to process the last event. The upper and lower zero-crossing events are tracked independently to make the algorithm robust against asymmetric breathing patterns. A multi-dimensional direction detection solution is proposed. The crossing direction is based on the result of the scalar product of (j) with the reference vectors D u and Dd is given by comparing with.

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[0082] In some embodiments, the BPM calculation block 540 may calculate a respiratory rate estimate 545. The outputs from the upward tracking block and the downward tracking block are, as described above, P u , P d , C u , and C d and may be supplied to the BPM calculation block 540 shown as. The following table shows the estimated respiratory rates for various reliability estimates, C u and C d .

Table 4

[0083] As shown in Table 1, there is no output respiratory rate estimate only when both reliability estimates are zero. By tracking zero crossings upward and downward, the methods and systems described herein can estimate the respiratory rate even in the case of one type of zero crossing, i.e., when there is no confidence upward or downward.

[0084] FIG. 8 is a simplified diagram of a radar-compatible device 800. One or more radar-compatible devices 800 may be present in the scenario depicted in FIG. 1 and described with respect to FIG. 1 according to the embodiments described herein. In some embodiments, the radar-compatible device 800 may execute the respiration monitoring system and method depicted in FIGS. 2-7 and described with respect to FIGS. 2-7. As shown in FIG. 8, the radar-compatible device 800 includes a processor 810 coupled to a memory 820. The operation of the radar-compatible device 800 is controlled by the processor 810. Also, although the radar-compatible device 800 is shown with only one processor 810, it is understood that the processor 810 may represent one or more central processing units, multi-core processors, microprocessors, microcontrollers, digital signal processors, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), graphics processing units (GPUs), and / or the like within the radar-compatible device 800. The radar-compatible device 800 may be implemented as a stand-alone subsystem, as a board added to a computing device, and / or as a virtual machine, in whole or in part.

[0085] The memory 820 may be used to store software executed by one or more data structures used during the operation of the radar-compatible device 800 and / or the radar-compatible device 800. The memory 820 may include one or more types of machine-readable media. Some common forms of machine-readable media include floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium having patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, and / or any other medium adapted to be read by a processor or computer.

[0086] Processor 810 and / or memory 820 can be arranged in any suitable physical configuration. In some embodiments, processor 810 and / or memory 820 may be implemented on the same board, within the same package (e.g., system-in-package), on the same chip (e.g., system-on-chip), and / or the like. In some embodiments, processor 810 and / or memory 820 may include distributed, virtualized, and / or containerized computing resources. Consistent with such embodiments, processor 810 and / or memory 820 may be located in one or more data centers and / or cloud computing facilities.

[0087] In some examples, memory 820 may include a non-transitory and tangible machine-readable medium, the non-transitory and tangible machine-readable medium including executable code that, when executed by one or more processors (e.g., processor 810), may cause the one or more processors to perform the methods described in more detail herein. For example, as shown, memory 820 may include instructions for respiratory monitoring module 830 that can be used to implement and / or emulate a system and model and / or implement any of the methods described herein. Respiratory monitoring module 830 may receive input signal 840 via antenna 815 and / or generate output signal 850. Examples of input signals may include, for example, as depicted in FIG. 1, earlier transmitted pulses reflected from an object within the environment of the device, which generate an estimated channel impulse response as shown in FIGS. 2A and 2B and described with respect to FIGS. 2A and 2B. The input signal may be structured in frames, and the frames may take forms described, for example, in any number of different standards, namely IEEE, FiRa, and / or the like. Examples of output signals may include transmitted frames at a particular rate, e.g., an RFRI rate, by radar-compatible device 800, as depicted in and described with respect to FIG. 2B.

[0088] Antenna 815 may comprise a transceiver, a separate transmitter and receiver, or any other means for transmitting in a radar or other signal transmission modality applicable to the systems and methods described herein. For example, radar-compatible device 800 may receive input signal 840 from an earlier transmitted pulse, e.g., a pulse transmitted by antenna 815.

[0089] Data interface 817 may include a communication interface, a user interface (such as a voice input interface, a graphical user interface, and / or the like). For example, radar-compatible device 800 may receive input 845 (such as a set of parameters for respiration monitoring) from a network database via the communication interface. Alternatively, radar-compatible device 800 may receive input 845, such as user-specified parameters for respiration monitoring, from a user via the user interface. Radar-compatible device 800 may generate output 855. For example, output 855 may be a numerical value for the respiration rate, or a sequence of numerical values over time representing a change in the respiration rate, as shown, for example, in FIGS. 10, 12, and 14.

[0090] In some embodiments, respiration monitoring module 330 is configured to control the content and timing of output signal 855. Respiration monitoring module 830 may further include a signal preparation sub-module 831 (e.g., instructions implementing tap selection block 505 and / or clutter removal block 510 in FIG. 5), a regression sub-module 832 (e.g., implementing regression block 515 in FIG. 5), a detection sub-module 833 (e.g., for implementing zero-crossing event detection block 520 and / or zero-crossing direction detection block as described in FIGS. 5-7), and / or a tracking sub-module 834 (e.g., for implementing blocks 530 and / or 535 as depicted in FIG. 5).

[0091] Some embodiments of UWB devices, such as UWB device 300, may include a non-transitory and tangible machine-readable medium that includes executable code, which, when executed by one or more processors (e.g., processor 310), may cause the one or more processors to perform a process of a method. Some common forms of machine-readable media that may include the process of the method are, for example, floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tapes, any other physical media having patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, and / or any other media adapted to be read by a processor or computer.

[0092] In some embodiments, the radar-compatible device 800 may be configured for UWB.

[0093] FIG. 9 illustrates an exemplary method for respiration rate estimation and tracking by a radar-compatible device, according to some aspects of the present disclosure. Method 900 is merely an example and is not intended to limit the present disclosure beyond what is expressly recited in the claims. Additional operations may be provided before, during, and after method 900, and some of the operations described may be exchanged, eliminated, or moved for additional embodiments of FIGS. 1-8. For ease of illustration, FIG. 9 is described in relation to FIGS. 1-8. In some embodiments, method 900 may be implemented by UWB device 800 as shown in FIG. 8, or by any of the radar-compatible devices described herein.

[0094] In step 902, a radar-compatible device (e.g., 800 of FIG. 8) receives a first parameter (e.g., the number of taps described herein) via a data interface (e.g., 817 of FIG. 8).

[0095] In step 904, the radar-compatible device (e.g., 800 in FIG. 8) receives a plurality of signals (e.g., 814 in FIG. 8 and as depicted and described in relation to FIG. 1) via a receiver (e.g., antenna 815 in FIG. 8).

[0096] In step 906, the radar-compatible device (e.g., 800 in FIG. 8) generates a channel impulse response (e.g., as described in FIGS. 2A and 2B) from the plurality of signals. In some embodiments, the channel impulse response may be a channel impulse response estimate that may be a digital signal.

[0097] In step 908, the radar-compatible device (e.g., 800 in FIG. 8) selects a portion of the channel impulse response (e.g., as represented by Equation 4) based on a first parameter (e.g., 505 in FIG. 5 as implemented by the signal preparation submodule 831 in FIG. 8).

[0098] In step 910, the radar-compatible device (e.g., 800 in FIG. 8) generates a modified signal (e.g., as represented by Equation 3) from a portion of the channel impulse response (e.g., using the signal preparation submodule 831 in FIG. 8), and the modified signal is a portion of the channel impulse response with clutter removed (e.g., 510 in FIG. 5). In some embodiments, the clutter may include background reverberation in the environment (e.g., as depicted and described in relation to FIG. 1).

[0099] In step 912, the radar-compatible device (e.g., 800 in FIG. 8) generates a plurality of regression lines from the modified signal (e.g., 515 in FIG. 5 implemented by the regression submodule 832).

[0100] In step 914, the radar-compatible device (e.g., 800 in FIG. 8) calculates a plurality of times based on the plurality of regression lines (e.g., 520 in FIG. 5 implemented by the detection submodule 833 in FIG. 8), and each of the plurality of regression lines passes through zero at one of the plurality of times.

[0101] In step 916, the radar-enabled device (e.g., 800 of FIG. 8) estimates a zero-crossing time based on a plurality of times (e.g., 520 of FIG. 5 implemented by detection submodule 833 of FIG. 8). In some embodiments, the radar-enabled device sorts the zero-crossing times based on the direction of change of the corrected signal at the zero-crossing time (determined by 525 of FIG. 5). In some embodiments, the radar-enabled device tracks the estimated zero-crossing times based on a cost matrix, where the cost matrix is based on the difference between the estimated zero-crossing times and the zero-crossing times stored in tracking. In some embodiments, the radar-enabled device assigns the estimated zero-crossing times to tracks if the gating constraints are met. In some embodiments, the radar-enabled device detects false zero-crossing times based on one or more confidence criteria.

[0102] In step 918, the radar-enabled device (e.g., 800 of FIG. 8) generates a respiration rate estimate (e.g., 540 of FIG. 5) based on the zero-crossing time. In some embodiments, the respiration rate estimate may be generated at each of a plurality of time steps. The respiration rate estimate may be output by the radar-enabled device, displayed on a user interface, or otherwise made available.

[0103] In some embodiments, a further step of method 900 may include a radar-enabled device that transmits a pulse at a given frequency (e.g., RFRI 255 depicted in and described with respect to FIG. 2B) (e.g., 850 in FIG. 8).

[0104] Referring to FIGS. 10 - 15, plots of respiratory rate are depicted using the systems and methods described herein. Further, heat maps are depicted for the prediction of respiratory rate using a frequency - based method. The frequency - based method used was the short - time Fourier transform. In FIGS. 10 - 15, the horizontal axis is a low - speed time axis in seconds. In the plots of the frequency - based technique, the vertical axis represents frequency bins. In the plots according to the systems and methods described herein, the vertical axis is breaths per minute.

[0105] FIG. 10 illustrates a plot of respiratory rate over time for a stationary subject according to some aspects of the present disclosure. The estimated respiratory rate is shown by individual data points in FIG. 10. While data was being acquired and the respiratory rate was being estimated, no specific instructions were given to the subject being measured, and the subject breathed normally. FIG. 10 shows the variation of respiratory rate over time and the estimated values without interruption of the respiratory rate throughout the experimental period.

[0106] FIG. 11 shows the results of respiration of the same subject described with respect to FIG. 10, but using a frequency - based technique. As shown in FIG. 11, the frequency - based method sometimes has higher intensity at the correct frequency bins (in the range of 15 - 20), but over a long period of time, the frequency - based method cannot accurately monitor the respiratory rate.

[0107] FIG. 12 illustrates a plot of respiratory rate over time with subject movement according to some aspects of the present disclosure. For the experiments plotted in FIGS. 12 and 13, the subject was asked to start moving their arm at a specific point in time. The experiment was designed to determine whether the systems and methods described herein are robust to subject movement. As seen in FIG. 12, the individual points accurately track the right respiration. The respiratory rate at approximately 175 seconds corresponding to when the subject starts to move. In particular, the respiratory rate is not lost due to the subject's movement.

[0108] FIG. 13 illustrates a plot of respiratory rate using a frequency-based technique with movement of the subject. FIG. 13 shows that the frequency-based method tends to have poor performance with respect to movement of the subject. When the subject begins to move around the 175 second mark, the frequency-based method loses the intensity of the correct frequency bin.

[0109] FIG. 14 illustrates a plot of respiratory rate in an empty room according to some aspects of the present disclosure. As shown in FIG. 14, as expected for an empty room, the respiratory rate was not detected.

[0110] FIG. 15 illustrates a plot of respiratory rate in an empty room using a frequency-based technique. In contrast to FIG. 14, the frequency-based method detects spurious frequencies that do not reflect the respiration of any subject.

[0111] Some of the terms used herein may match or approximate those of a particular standard such as FiRa, but those skilled in the art will recognize their relevance and applicability to other protocols based on the concept of the round range (e.g., as defined by the CCC - Car Connectivity Consortium).

[0112] Those skilled in the art will recognize improvements and changes to the preferred embodiments of the present disclosure. All such improvements and modifications are considered to be within the concepts disclosed herein and the scope of the following claims.

Claims

1. 1. A method for respiration detection and monitoring, comprising: Receiving a first parameter via a data interface; receiving a plurality of signals via a receiver; generating a channel impulse response from the plurality of signals; selecting a portion of the channel impulse response based on the first parameter; generating a modified signal from the portion of the channel impulse response, the modified signal being the portion of the channel impulse response with clutter removed; generating a plurality of regression lines from the corrected signal; calculating a plurality of times based on the plurality of regression lines, each of the plurality of regression lines passing through zero at one of the plurality of times; estimating a zero crossing time based on the plurality of times; generating a respiration rate estimate based on the zero crossing time.

2. The method of claim 1 , further comprising sorting the zero-crossing times based on a direction of change of the modified signal at the zero-crossing times.

3. tracking estimated zero-crossing times based on a cost matrix, the cost matrix being based on differences between the estimated zero-crossing times and zero-crossing times stored in the tracking; The method of claim 2 , further comprising: if a gating constraint is satisfied, assigning the estimated zero-crossing time to the track.

4. The method of claim 1 , wherein the channel impulse response is a channel impulse response estimate.

5. The method of claim 1 , wherein the respiration rate estimate is generated at each of a plurality of time steps.

6. The method of claim 1 , further comprising detecting false zero-crossing time detections based on one or more confidence measures.

7. The method of claim 1 , wherein the clutter comprises background reverberations in an environment.

8. A device, comprising: A receiver; a non-transitory memory for storing instructions; and one or more processors, the one or more processors causing the device to: Receiving a first parameter via a data interface; receiving a plurality of signals via a receiver; generating a channel impulse response from the plurality of signals; selecting a portion of the channel impulse response based on the first parameter; generating a modified signal from the portion of the channel impulse response, the modified signal being the portion of the channel impulse response with clutter removed; generating a plurality of regression lines from the corrected signal; calculating a plurality of times based on the plurality of regression lines, each of the plurality of regression lines passing through zero at one of the plurality of times; estimating a zero crossing time based on the plurality of times; generating a respiration rate estimate based on the zero-crossing time.

9. One or more processors The device of claim 8 , further configured to execute instructions comprising: sorting the zero-crossing times based on a direction of change of the modified signal at the zero-crossing times.

10. One or more processors tracking estimated zero-crossing times based on a cost matrix, the cost matrix being based on differences between the estimated zero-crossing times and zero-crossing times stored in the tracking; The device of claim 9 , further configured to execute instructions comprising: if a gating constraint is satisfied, assigning the estimated zero-crossing time to the track.

11. The device of claim 8 , wherein the channel impulse response is a channel impulse response estimate.

12. The device of claim 8 , wherein the respiration rate estimate is generated at each of a plurality of time steps.

13. One or more processors The device of claim 8 , further configured to execute instructions including detecting false zero-crossing time detections based on one or more confidence criteria.

14. The device of claim 8 , wherein the clutter comprises background reverberations in the environment.

15. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: Receiving a first parameter via a data interface; generating a channel impulse response from a plurality of signals; selecting a portion of the channel impulse response based on the first parameter; generating a modified signal from the portion of the channel impulse response, the modified signal being the portion of the channel impulse response with clutter removed; generating a plurality of regression lines from the corrected signal; calculating a plurality of times based on the plurality of regression lines, each of the plurality of regression lines passing through zero at one of the plurality of times; estimating a zero crossing time based on the plurality of times; generating a respiration rate estimate based on the zero-crossing time.

16. The instructions, when executed by the one or more processors, further cause the one or more processors to:

16. The non-transitory computer-readable medium of claim 15, further comprising sorting the zero-crossing times based on a direction of change of the modified signal at the zero-crossing times.

17. The instructions, when executed by the one or more processors, further cause the one or more processors to: tracking estimated zero-crossing times based on a cost matrix, the cost matrix being based on differences between the estimated zero-crossing times and zero-crossing times stored in the tracking; and assigning the estimated zero-crossing time to the track if a gating constraint is satisfied.

18. 16. The non-transitory machine-readable medium of claim 15, wherein the channel impulse response is a channel impulse response estimate.

19. 16. The non-transitory machine-readable medium of claim 15, wherein the respiration rate estimate is generated at each of a plurality of time steps.

20. The instructions, when executed by the one or more processors, further cause the one or more processors to:

20. The non-transitory computer-readable medium of claim 15, further comprising detecting false zero-crossing time detections based on one or more confidence criteria.

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