Non-contact respiratory guidance system facilitating real-time feedback

Abstract

Systems and methods are provided for closed-loop, non-contact respiratory guidance that provide quantified alignment feedback data in real time. In one embodiment, the computer-implemented method may include receiving, by a computing system operably coupled to one or more processors, input data including respiratory data indicative of an entity's respiration. The computer-implemented method may include converting, by the computing system, the respiratory data to an entity respiratory signal such that the entity respiratory signal tracks the entity's respiration in real time. The computer-implemented method may include comparing, by the computing system, the entity respiratory signal to a suggested respiratory signal indicative of a suggested respiration. The computer-implemented method may include providing, by the computing system, alignment feedback data to the entity in real time based at least in part on the entity's respiration. The alignment feedback data may be indicative of an alignment between the entity respiratory signal and the suggested respiratory signal.

Description

[Technical field]

[0001] The present disclosure relates generally to guided breathing systems and, more particularly, to a closed-loop, non-contact guided breathing system that provides real-time feedback. [Background technology]

[0002] Guided breathing systems provide breathing exercises that may include suggested breathing patterns that an entity can attempt to mimic. For example, such systems may monitor the entity's breathing, output suggested breathing patterns, and / or provide feedback regarding the entity's breathing. The feedback may include biometric feedback that may be indicative of the entity's breathing rate, heart rate, and / or movement, for example. Summary of the Invention

[0003] Aspects and advantages of embodiments of the disclosure will be set forth in part in the description that follows, or may be learned from the description, or may be learned by practice of the embodiments.

[0004] According to an exemplary embodiment, a computing system may include one or more processors and one or more non-transitory computer-readable storage media storing instructions that, when executed by the one or more processors, cause the computing system to perform operations. The operations may include receiving input data including respiratory data indicative of an entity's respiration, converting the respiratory data to an entity respiratory signal such that the entity respiratory signal tracks the entity's respiration in real time, comparing the entity respiratory signal to a suggested respiratory signal indicative of a suggested respiration, and / or providing alignment feedback data to the entity in real time based at least in part on the entity's respiration. The alignment feedback data may indicate an alignment between the entity respiratory signal and the suggested respiratory signal.

[0005] According to another exemplary embodiment, a computer-implemented method may include receiving, by a computing system operatively coupled to one or more processors, input data including respiratory data indicative of respiration of an entity, converting, by the computing system, the respiratory data into an entity respiratory signal such that the entity respiratory signal tracks the entity's respiration in real time, comparing, by the computing system, the entity respiratory signal to a suggested respiratory signal indicative of a suggested respiration, and / or providing, by the computing system, alignment feedback data to the entity in real time based at least in part on the entity's respiration. The alignment feedback data may indicate an alignment of the entity respiratory signal and the suggested respiratory signal.

[0006] According to another exemplary embodiment, a computing system may include one or more processors and one or more non-transitory computer-readable storage media storing instructions that, when executed by the one or more processors, cause the computing system to perform operations. The operations may include receiving a continuous chirp radar signal including respiration data indicative of respiration of an entity. The continuous chirp radar signal may include a plurality of chirps. The operations may further include converting the continuous chirp radar signal into an entity respiration amplitude signal such that the entity respiration amplitude signal tracks the entity's respiration in real time. A signal amplitude of the entity respiration amplitude signal may be generated upon receipt of each of the plurality of chirps. The operations may further include comparing the entity respiration amplitude signal to a suggested respiration signal indicative of a suggested respiration. The operations may further include providing alignment feedback data to the entity in real time based at least in part on the entity's respiration. The alignment feedback data may indicate an alignment of the entity respiration amplitude signal and the suggested respiration signal.

[0007] These and other features, aspects, and advantages of various embodiments of the present disclosure will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the present disclosure and, together with the detailed description, serve to explain associated principles.

[0008] A detailed discussion of embodiments directed to those skilled in the art is set forth herein with reference to the accompanying drawings. [Brief description of the drawings]

[0009] [Figure 1] 1 illustrates a data flow diagram of an exemplary, non-limiting data flow process, in accordance with one or more exemplary embodiments of the present disclosure. [Diagram 2] 1 illustrates a data flow diagram of an exemplary, non-limiting data flow process, in accordance with one or more exemplary embodiments of the present disclosure. [Diagram 3] 1 illustrates a data flow diagram of an exemplary, non-limiting data flow process, in accordance with one or more exemplary embodiments of the present disclosure. [Figure 4] 1 illustrates a data flow diagram of an exemplary, non-limiting data flow process, in accordance with one or more exemplary embodiments of the present disclosure. [Figure 5A] 1 shows a diagram of an exemplary, non-limiting signal evaluation process, according to one or more exemplary embodiments of the present disclosure. [Figure 5B] 1 shows a diagram of an exemplary, non-limiting signal evaluation process, according to one or more exemplary embodiments of the present disclosure. [Figure 6] 1 shows a diagram of exemplary non-limiting alignment feedback data, in accordance with one or more exemplary embodiments of the present disclosure. [Figure 7] FIG. 1 illustrates a block diagram of an exemplary, non-limiting computing system in accordance with one or more exemplary embodiments of the present disclosure. [Figure 8] 1 illustrates a flow diagram of an exemplary, non-limiting computer-implemented method in accordance with one or more exemplary embodiments of the present disclosure. [Figure 9] 1 illustrates a flow diagram of an exemplary, non-limiting computer-implemented method in accordance with one or more exemplary embodiments of the present disclosure. [Figure 10A] 1 shows a diagram of exemplary non-limiting alignment feedback data, in accordance with one or more exemplary embodiments of the present disclosure. [Figure 10B] 1 shows a diagram of exemplary non-limiting alignment feedback data, in accordance with one or more exemplary embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Reference will now be made in detail to the embodiments of the present disclosure, one or more examples of which are illustrated in the drawings. Each example is provided as an explanation of the disclosure, and not as a limitation of the disclosure. Indeed, it will be apparent to those skilled in the art that various modifications and variations can be made in the present disclosure without departing from the scope or spirit of the disclosure. For example, features illustrated or described as part of one embodiment can be used with another embodiment to yield still a further embodiment. It is therefore intended that the present invention cover such modifications and variations as come within the scope of the appended claims and their equivalents.

[0011] As referred to herein, the term "entity" refers to a human being, a user, an end user, a consumer, and / or another type of entity that may implement one or more embodiments of the present disclosure described herein, shown in the accompanying drawings, and / or included in the appended claims. As referred to herein, the terms "or" and "and / or" are generally intended to be inclusive, i.e., that is, "A or B" or "A and / or B" are intended to mean "A or B or both," respectively. As referred to herein, terms such as "first," "second," "third," etc. may be used interchangeably to distinguish one component or entity from another and are not intended to imply the location, functionality, or importance of the individual components or entities. As used herein, the terms "about" and / or "approximately" when used in conjunction with a numerical value refer to within 10% of the indicated numerical value. As referred to herein, the terms “couple,” “couples,” “coupled,” and / or “coupling” refer to a chemical coupling (e.g., chemical coupling), a communicative coupling, an electrical coupling and / or an electromagnetic coupling (e.g., capacitive coupling, inductive coupling, direct coupling and / or connective coupling, etc.), a mechanical coupling, an operative coupling, an optical coupling, and / or a physical coupling.

[0012] Exemplary aspects of the present disclosure are directed to a closed-loop, non-contact respiratory guidance system that may provide real-time quantified alignment feedback to an entity (e.g., a human) attempting to mimic a suggested breathing (e.g., a suggested breathing pattern) recommended by the system. The quantified alignment feedback may indicate the degree to which the entity's breathing is aligned with the suggested breathing. As referred to herein, a "closed-loop" respiratory guidance system may describe a system that may, for example, receive first input data indicative of an entity's breathing at a first time (T1), provide a first suggested breathing to the entity based at least in part on the first input data, and receive second input data indicative of the entity's breathing at a second time (T2) after (e.g., subsequent to) the first time (T1) while the entity is attempting to mimic (e.g., simulate) the first suggested breathing, and / or provide a second suggested breathing to the entity based at least in part on the second input data. In some embodiments, such a closed-loop process may continue indefinitely while the entity is implementing the disclosed techniques according to one or more embodiments described herein.

[0013] According to one or more exemplary embodiments of the present disclosure, a computing system described herein may facilitate non-contact respiratory guidance with quantified alignment feedback in real time. For example, to facilitate such non-contact respiratory guidance with quantified alignment feedback in real time, a computing system according to exemplary embodiments described herein may perform operations that may include, but are not limited to, receiving input data that may include and / or consist of respiratory data indicative of an entity's breathing, converting the respiratory data to an entity respiratory signal such that the entity respiratory signal tracks the entity's breathing in real time, comparing the entity respiratory signal to a proposed respiratory signal indicative of a proposed breathing, and / or providing alignment feedback data to the entity in real time based at least in part on the entity's breathing. In some embodiments, the alignment feedback data may indicate an alignment of the entity respiratory signal and the proposed respiratory signal. For example, as described in further detail below, in exemplary embodiments described herein, the alignment feedback data may be generated based on comparing the entity respiratory signal to a proposed respiratory signal that may be indicative of a proposed breathing. For example, in some embodiments, the alignment feedback data may be associated with a difference between the entity respiratory signal and the proposed respiratory signal. In at least one embodiment, the alignment feedback data may be related to, for example, differences in amplitude, frequency, and / or phase between the entity respiratory signal and the proposed respiratory signal.

[0014] To perform the above-mentioned operations and / or other operations described herein, a computing system according to exemplary embodiments of the present disclosure may include, may be communicatively coupled to, and / or associated with (e.g., communicatively, operatively, etc.) one or more processors and / or one or more non-transitory computer-readable storage media. In these or other exemplary embodiments, the one or more non-transitory computer-readable storage media may store instructions that, when executed by the one or more processors, may cause the computing system (e.g., via the one or more processors) to perform the above-mentioned operations and / or other operations described herein for facilitating non-contact respiratory guidance with real-time quantified alignment feedback.

[0015] In accordance with one or more example embodiments of the present disclosure, a computing system may receive input data that may include and / or consist of respiratory data that may be indicative of an entity's breathing (e.g., that may be indicative of the entity's current real-time breathing pattern). In at least one embodiment, such input data and / or respiratory data may include and / or consist of, for example, radar data indicative of the entity's breathing, high frequency radar data indicative of the entity's breathing, sonar data indicative of the entity's breathing, acoustic data indicative of the entity's breathing, video data indicative of the entity's breathing, time series data indicative of the entity's breathing, and / or other input data and / or respiratory data that may be indicative of the entity's breathing.

[0016] In some embodiments, the computing system may receive a particular type of respiratory data that may be indicative of an entity's breathing. The computing system may receive a particular type and / or a combination of different types of input data that may constitute an entity's breathing. For example, in these or other embodiments, the computing system may receive radar data indicative of an entity's breathing, sonar data indicative of an entity's breathing, and video data indicative of an entity's breathing.

[0017] In one embodiment, the computing system may receive input data in the form of a continuous chirp radar signal (e.g., a frequency modulated continuous wave (FMCW) radar signal), which may include and / or consist of, for example, respiration data that may be indicative of the entity's respiration. In this or another embodiment, the continuous chirp radar signal may include and / or consist of a plurality of chirps.

[0018] In one or more exemplary embodiments, the computing system may receive the above-mentioned input data and / or respiratory data from one or more non-contact sources and / or devices that may be included in, coupled (e.g., communicatively, operatively, etc.) and / or associated with the computing system. In at least one embodiment, such one or more non-contact sources and / or devices may capture, collect, and / or otherwise obtain such input data and / or respiratory data without physically engaging with (e.g., without contacting) the entity. For example, in one or more embodiments, the computing system may receive such input data and / or respiratory data from one or more non-contact sources and / or devices that may include, but are not limited to, radar devices (e.g., high frequency radar, real-time motion tracking radar, etc.), sonar devices, camera devices, audio devices (e.g., microphones, etc.), and / or other non-contact sources and / or devices that may capture, collect, and / or otherwise obtain such input data and / or respiratory data without physically engaging with an entity. In at least one embodiment, the computing system may receive the continuous chirp radar signal described above from a high frequency radar (e.g., FMCW radar) and / or a real-time motion tracking radar.

[0019] In at least one embodiment of the present disclosure, based at least in part on (e.g., in response to) receiving the input data and / or respiration data described above, the computing system may convert the respiration data into an entity respiration signal such that the entity's respiration signal may track the entity's respiration in real time. For example, in some embodiments, upon receiving the input data and / or respiration data (e.g., in real time, as soon as the input data and / or respiration data are received), the computing system may implement (e.g., execute, operate, etc.) a tracking algorithm, such as, for example, a phase tracking algorithm having a relatively low latency, to convert the respiration data into an entity respiration amplitude signal such that the entity respiration amplitude signal may track (e.g., mimic, simulate, replicate, etc.) the entity's respiration in real time (e.g., live, in parallel, and / or simultaneously with the entity's respiration) and / or be synchronized with the entity's respiration.

[0020] As referred to in this specification, the "relatively low latency" of the above-mentioned phase tracking algorithm may refer to a latency lower (e.g., less than) the latency of other phase tracking algorithms, a latency defined as low according to standards and / or protocols relevant to the technical field of signal processing, a latency considered low by those skilled in the art of signal processing, a latency that is imperceptible by an entity defined herein (e.g., imperceptible by a human), a latency that is low enough to enable an entity defined herein (e.g., a human) to perceive the conversion of the above-mentioned respiratory data into an entity respiratory signal as occurring in real time (e.g., live, instantaneous, etc.), and / or a latency that is shorter than a defined time (e.g., less than about 5 seconds, less than about 1 second, less than about 500 milliseconds (ms), less than about 100 ms, less than about 10 ms, etc.).

[0021] In one or more embodiments in which the computing system receives the input data and / or respiration data in the form of a continuous chirp radar signal as described above (e.g., in the form of an FMCW radar signal that may be provided by a high frequency radar, an FMCW radar, a real-time motion tracking radar, etc.), the computing system may convert the continuous chirp radar signal into an entity respiration amplitude signal such that the entity respiration amplitude signal may track the entity's respiration in real-time. In these one or more embodiments, the signal amplitude of the entity respiration amplitude signal may be generated (e.g., by the computing system) upon receipt of each of a plurality of chirps. For example, in these or other embodiments, upon receiving the continuous chirp radar signal (e.g., in real-time, as soon as the continuous chirp radar signal is received), the computing system may implement (e.g., execute, operate, etc.) the phase tracking algorithm described above having a relatively low latency to convert the continuous chirp radar signal into an entity respiration amplitude signal such that the entity respiration amplitude signal may track (e.g., mimic, simulate, replicate, etc.) the entity's respiration in real-time (e.g., live, in parallel, and / or simultaneously with the entity's respiration) and / or be synchronized with the entity's respiration.

[0022] To facilitate the above-described conversion of the continuous chirp radar signal into such an entity respiration amplitude signal according to one or more embodiments, upon receiving a plurality of chirps of the continuous chirp radar signal (e.g., in real time, as soon as the plurality of chirps of the continuous chirp radar signal are received), the computing system may implement (e.g., execute, operate, etc.) the above-described phase tracking algorithm having a relatively low latency to generate a signal amplitude of the entity respiration amplitude signal. For example, in one embodiment, upon receiving a new chirp of the plurality of chirps (e.g., in real time, as soon as a chirp not previously received is received), the computing system may implement a phase tracking algorithm having a relatively low latency to generate a new local amplitude (e.g., a previously ungenerated local amplitude) of the entity respiration amplitude signal. In this embodiment, the new local amplitude may correspond to the new chirp. In this manner, a computing system according to an exemplary embodiment may thereby convert each chirp of the multiple chirps, and thus the continuous chirp radar signal, into an entity respiration amplitude signal that may track (e.g., mimic, simulate, replicate, etc.) the entity's respiration in real time (e.g., live, in parallel, and / or simultaneously with the entity's respiration) and / or be synchronized with the entity's respiration.

[0023] To further facilitate the above-described conversion of the continuous chirp radar signal into such an entity respiration amplitude signal in accordance with at least one embodiment of the present disclosure, upon receiving the continuous chirp radar signal (e.g., in real time, as soon as the continuous chirp radar signal is received), the computing system may implement (e.g., execute, operate, etc.) the above-described phase tracking algorithm having relatively low latency to perform one or more of the following operations:

[0024] In one embodiment, the computing system may implement a phase tracking algorithm having a relatively low latency to remove noise data from the continuous chirp radar signal (e.g., using an exponential filter and / or an exponential smoothing process). In this embodiment, the noise data may include and / or consist of data that may be indicative of at least one movement corresponding to one or more second entities (e.g., movement of an object and / or another person).

[0025] In one embodiment, the computing system may implement a phase tracking algorithm having a relatively low latency to map a range that may be associated with the continuous chirp radar signal (e.g., using an exponential filter and / or an exponential smoothing process). In this embodiment, the range may include and / or constitute at least a portion of the entity's respiratory data. In some embodiments, the range may include and / or constitute a spatial range that may be associated with and / or proximate to the entity. For example, in some embodiments, the range may be defined as a distance spanning between the entity and a radar device that captures, collects, and / or otherwise obtains the entity's respiratory data. For example, in at least one embodiment, the range may be defined as a distance spanning, for example, from the entity's chest to the radar device. In one embodiment, the range may be defined as a distance spanning between the entity's chest and the radar device of approximately one meter (m). In at least one embodiment, the range may include one or more range bins that may include and / or constitute at least a portion of the entity's respiratory data. For example, in one embodiment, the one or more range bins may be defined as a distance interval along the range described above. For example, in this or another embodiment, one or more range bins may each be defined as a distance of approximately 3 centimeters (cm) across a 1 m range between the entity's chest and the radar.

[0026] In one embodiment, the computing system may implement a phase tracking algorithm having a relatively low latency to normalize the ranges into a range probability map that may include and / or comprise a plurality of range bins (e.g., one or more range bins as described above). In this embodiment, each of the plurality of range bins may include and / or comprise at least a portion of the respiratory data. As referred to herein, a "range probability map" may comprise a vector (e.g., range_map / sum(range_map)) that divides each of the range bin values ​​by the sum of all range bin values.

[0027] In one embodiment, the computing system may implement a phase tracking algorithm having a relatively low latency to apply one or more inertia functions to the range probability map and / or the range bins to determine a center of mass that may correspond to the range probability map and / or the range bins. In an embodiment, the computing system may implement the phase tracking algorithm to apply inertia as an element-wise low pass filter. In this embodiment or another embodiment, the range probability map is updated over time and should not be changed frequently. In this embodiment or another embodiment, the effect of inertia may eliminate sudden motion artifacts. In this embodiment or another embodiment, the center of mass may constitute an expected value of the probability map after application of inertia. For example, the center of mass may describe and / or constitute a range that has the highest probability of accurately describing an entity's chest region. In some embodiments, the center of mass may constitute a range bin that has the highest probability (e.g., compared to all other range bins) of accurately describing an entity's chest region and / or chest motion during the entity's breathing. In at least one embodiment, the computing system may implement a phase tracking algorithm to "lock" a phase value at the center of mass (e.g., to a phase value at a particular range bin that has the highest probability of accurately describing an entity's chest region and / or chest motion during the entity's breathing).

[0028] In one embodiment, the computing system may implement a phase tracking algorithm having a relatively low latency to extract phase data that may correspond to the center of mass. In this embodiment, the phase data may represent a wrapped phase signal that may correspond to the center of mass. Note that in some embodiments, the original range map may be calculated from the continuous chirp radar signal by taking the absolute component of the Fast Fourier Transform (FFT). In some embodiments, the phase component of the FFT may be set aside (e.g., set aside, saved, stored, etc.). In some embodiments, the dimensions of the range map may be the same as the dimensions of the phase map, since it is derived from the same FFT as the phase map. In some embodiments, to extract the phase data that may correspond to the center of mass, the computing system may implement a phase tracking algorithm having a relatively low latency to extract (e.g., retrieve) specific phase values ​​in the corresponding highest probability user chest range bins identified in the previous step described above.

[0029] In one embodiment, the computing system may implement a phase tracking algorithm with relatively low latency to perform a signal phase unwrapping process on the phase data and / or the wrapped phase signal to obtain a continuous phase signal (e.g., an unwrapped phase signal) that may correspond to the center of mass. In some embodiments, when calculating the phase for each chirp, the phase value is between negative π (-π or -¶) and positive π (+π or +¶). In some embodiments, the phase value varies within this range (e.g., between -π (-¶) and +π (+¶)) over time based on the entity's breathing frequency. However, in some embodiments, there may be a "wrapping" effect when, for example, the phase value is π-0.1 (¶-0.1), and as the entity breathes deeply and the phase value increases, the phase value should bounce back to -π (-¶) since it cannot represent values ​​greater than +π (+¶). Thus, in some embodiments, the "smooth" nature of an entity's breathing can be exploited (e.g., leveraged) and whenever there is this sudden bounce from -π(-¶) to π(¶) or from π(¶) to -π(-¶), it can be interpreted as wrapping behavior and the two ends can be "stitched together" to reconstruct a smooth breathing signal. For example, in at least one embodiment, a phase tracking algorithm having a relatively low latency can implement a phase unwrapping algorithm, such as, for example, Itoh's phase unwrapping algorithm, to perform the above-mentioned signal phase unwrapping process on a one-dimensional (1-D) phase time series that may be obtained from the previous step described above.

[0030] In one embodiment, the computing system may implement a phase tracking algorithm having a relatively low latency to apply a filter to the continuous phase signal to obtain an entity respiration amplitude signal, in this embodiment, the filter may be operable to remove data that may be indicative of defined entity movements that may be associated with the entity's respiration (e.g., subtle movements that the entity makes while inhaling and / or exhaling).

[0031] According to at least one embodiment described herein, based at least in part on (e.g., in response to) converting the above-mentioned respiration data and / or the continuous chirp radar signal into the above-mentioned entity respiration signal and / or entity respiration amplitude signal, the computing system may compare the entity respiration signal and / or entity respiration amplitude signal to a suggested respiration signal that may be indicative of a suggested respiration (e.g., a suggested respiration pattern that may be defined and / or recommended by the computing system). For example, in one exemplary embodiment, the computing system may compare the entity respiration signal and / or entity respiration amplitude signal to such suggested respiration signal to determine the degree to which the entity respiration signal and / or entity respiration amplitude signal is aligned with the suggested respiration signal. In one exemplary embodiment, the computing system may compare the entity respiration signal and / or entity respiration amplitude signal to a suggested respiration signal that may be generated by the computing system. In this or another exemplary embodiment, such suggested respiration signal may be indicative of a suggested respiration (e.g., a suggested respiration pattern) that may be defined and / or recommended by the computing system. In this or another exemplary embodiment, the computing system may define and / or recommend such suggested breathing and / or generate such suggested breathing signals based at least in part on, for example, one or more attributes and / or biometrics that may correspond to the entity (e.g., the entity's age, weight, height, real-time heart rate and / or average heart rate, real-time blood pressure and / or average blood pressure, etc.).

[0032] To facilitate the above-described comparison of the entity respiratory signal and / or the entity respiratory amplitude signal to the proposed respiratory signal and / or to determine the degree to which the entity respiratory signal and / or the entity respiratory amplitude signal is aligned with the proposed respiratory signal, a computing system according to an exemplary embodiment may implement (e.g., execute, operate, etc.) an alignment algorithm. For example, in at least one embodiment, the computing system may implement an alignment algorithm that may apply a spectral similarity process to compare the entity respiratory signal and / or the entity respiratory amplitude signal to the proposed respiratory signal and / or to determine the degree to which the entity respiratory signal and / or the entity respiratory amplitude signal is aligned with the proposed respiratory signal. In this embodiment, the degree to which the entity respiratory signal and / or the entity respiratory amplitude signal is aligned with the proposed respiratory signal may be expressed as an alignment score, which may range, for example, from a value of about zero (0) to a value of about one (1). In this embodiment, a value of zero (0) may indicate a relatively poor alignment (e.g., no alignment) and / or a value of one (1) may indicate a relatively good alignment (e.g., perfect alignment). In this manner, a computing system according to the exemplary embodiments described herein may determine an alignment score that may indicate the degree of alignment between the entity respiration signal and / or the entity respiration amplitude signal and the proposed respiration signal.

[0033] As referred to herein, in some embodiments, "alignment" of an entity respiratory signal (e.g., the entity respiratory amplitude signal) with a proposed respiratory signal may occur when the entity respiratory signal (e.g., the entity respiratory amplitude signal) is visually and / or mathematically in phase or approximately in phase with the proposed respiratory signal. As used herein, the "degree" of alignment of an entity respiratory signal (e.g., the entity respiratory amplitude signal) with a proposed respiratory signal may describe, for example, how close (or not) the entity respiratory signal (e.g., the entity respiratory amplitude signal) is to being in phase or approximately in phase with the proposed respiratory signal, visually and / or mathematically.

[0034] To determine the above-mentioned alignment scores according to at least one embodiment of the present disclosure, a computing system may implement (e.g., execute, operate, etc.) the above-mentioned alignment algorithm to perform one or more of the following operations: For example, in one embodiment, the computing system may implement the alignment algorithm to calculate a spectral vector of the respiratory data for phase invariance by calculating a first spectral vector that may correspond to the entity's respiratory data and / or the entity's respiratory signal (e.g., the entity's respiratory amplitude signal) and calculating a second spectral vector that may correspond to suggested respiratory data (e.g., data indicative of suggested breathing that may be defined and / or recommended by the computing system as described above) and / or the suggested respiratory signal, apply a normalization function, such as an L2 normalization function, to the first and second spectral vectors to calculate a first and second L2 normalized spectral vector, respectively, calculate alignment scores of the first and second L2 normalized spectral vectors using a dot product operation, and / or apply a softmax function to the alignment scores to improve the dynamic range associated with the alignment scores.

[0035] In one or more embodiments, each of the above-mentioned spectral vectors may constitute an absolute FFT (e.g., absolute FFT values ​​corresponding to a phase signal, an amplitude signal, etc.) of a signal. For example, in one embodiment, the above-mentioned first spectral vector may constitute an absolute FFT (e.g., absolute FFT values) of the above-mentioned continuous phase signal (e.g., an unwrapped phase signal that may correspond to a center of mass) that may correspond to a center of mass that may be obtained using the above-mentioned phase unwrapping algorithm (e.g., using the Itoh phase unwrapping algorithm). In at least one embodiment, the above-mentioned L2 normalization function may include dividing each element of the spectral vector by the L2 norm (=energy) of the entire vector (e.g., to obtain an L2 normalized vector). In one embodiment, the above-mentioned dot product operation may include taking a dot product of two such normalized vectors (e.g., two L2 normalized vectors), which may include taking an element-wise product between the two vectors and summing them all together. In some embodiments, such a dot product is maximized to 1 if the two normalized vectors are exactly the same. For example, the unnormalized vector can be expressed as dot(a / ||a||,a / ||a||)=dot(a,a) / ||a||^2=||a||^2 / ||a||^2=1, and the dot product is minimized when the two normalized vectors look different. Thus, in some embodiments, the dot product is a measure of similarity between an entity's breathing (e.g., the entity's breathing pattern) and a proposed breathing (e.g., the proposed breathing pattern). In some exemplary embodiments of the present disclosure, the dot product of the entity's breathing and the proposed breathing and / or such a measure of similarity is described as the degree to which the entity's breathing is aligned with the proposed breathing.

[0036] In one or more embodiments described herein, the computing system may provide alignment feedback data to the entity in real time based at least in part on the entity's respiration. In one or more of these embodiments, the alignment feedback data may indicate an alignment of the entity respiration signal and / or the entity respiration amplitude signal with the proposed respiration signal. For example, in one or more of these embodiments, based at least in part on (e.g., in response to) receiving the above-mentioned input data (e.g., a continuous chirp radar signal), which may include and / or consist of respiration data that may be indicative of the entity's respiration, the computing system may (e.g., in real time as soon as such input data is received) implement the above-mentioned phase tracking algorithm having a relatively low latency to convert the respiration data into an entity respiration signal (e.g., the entity respiration amplitude signal), implement the above-mentioned alignment algorithm to compare the entity respiration signal (e.g., the entity respiration amplitude signal) with the proposed respiration signal and / or determine an alignment score corresponding to such signal, and / or provide the entity with the alignment score and / or other alignment feedback data in real time in response to the entity's respiration (e.g., live, in parallel, and / or simultaneously with the entity's respiration). In one or more of these embodiments, the computing system may perform such operations in real-time while the entity is performing a guided breathing exercise, which may be defined and / or communicated to the entity by the computing system based on, for example, the suggested breathing described above and / or a suggested breathing signal that may be defined and / or generated by the computing system as described above. For example, in one or more of these embodiments, the computing system may perform such operations in real-time while the entity is attempting to align the entity's breathing with the suggested breathing and / or align the entity breathing signal (e.g., the entity breathing amplitude signal) with the suggested breathing signal.

[0037] In additional and / or alternative embodiments of the present disclosure, the computing system may provide alignment feedback data to the entity that may include and / or consist of an alignment visualization that may include the entity respiratory signal (e.g., the entity respiratory amplitude signal) and / or the proposed respiratory signal. For example, in this additional and / or alternative embodiment, the computing system may provide alignment visualization to the entity that may include and / or consist of a visualization of the entity respiratory signal (e.g., the entity respiratory amplitude signal) overlaid and / or proximate (e.g., superimposed and / or adjacent to) the proposed respiratory signal. In one embodiment, such alignment visualization may include and / or consist of an image (e.g., a still image) of the entity respiratory signal (e.g., the entity respiratory amplitude signal) overlaid and / or proximate (e.g., superimposed and / or adjacent to) the proposed respiratory signal. In another embodiment, such alignment visualization may include and / or consist of a video (e.g., live video, real-time video) of the entity respiratory signal (e.g., the entity respiratory amplitude signal) overlaid and / or proximate to (e.g., superimposed on and / or adjacent to) the proposed respiratory signal.

[0038] In some embodiments, the computing system may provide one or more other types of alignment feedback data to the entity that may be indicative of alignment between the entity respiratory signal and the proposed respiratory signal. For example, in these or other embodiments, the computing system may provide such alignment feedback data to the entity in the form of, for example, audio data (e.g., a digital voice, a buzzer, an audible alarm, etc.), textual data, numerical data, and / or alphanumeric data (e.g., letters, numbers, words, written messages, push notifications, etc.), graphical data (e.g., symbols, signs, icons, emojis, etc.), haptic data (e.g., vibration of a device associated with the entity, such as a smartphone, a wearable computing device, etc.), visual data (e.g., a light having an intensity that correlates with and / or varies depending on the degree of alignment), and / or another form of data. In these or other embodiments, the computing system may provide such one or more other types of alignment feedback data to the entity in real time in response to the entity's breathing (e.g., live, in parallel, and / or simultaneously with the entity's breathing).

[0039] In at least one embodiment, the computing system may provide the alignment feedback data described above to the entity over a network, such as, for example, a local area network (LAN), a wireless and / or wired network, a wide area network (WAN), a personal area network (PAN), a wireless personal area network (WPAN), and / or another network. In this or another embodiment, the computing system may provide the alignment feedback data to the entity via, for example, a monitor and / or screen that may be coupled to, included in, and / or otherwise associated with the computing system, a monitor and / or screen that may be coupled to, included in, and / or otherwise associated with a computing device (e.g., a wearable computing device, computer, smartphone, tablet, etc.) that may be associated with the entity, and / or another device.

[0040] Exemplary aspects of the present disclosure provide several technical effects, advantages, and / or improvements in computing technology. For example, by facilitating non-contact respiratory guidance with quantified alignment feedback in real time according to exemplary embodiments of the present disclosure, a computing system may thereby eliminate one or more contact-based components (e.g., devices, hardware, software, etc.) and / or processes (e.g., workflows) used to otherwise acquire, capture, and / or collect entity respiratory data. In another example, by implementing the above-described phase tracking algorithm having a relatively low latency (e.g., compared to other phase tracking algorithms) to convert respiratory data to an entity respiratory signal (e.g., an entity respiratory amplitude signal) as described above, a computing system according to exemplary embodiments may thereby improve the processing speed, performance, and / or efficiency of one or more processors that may perform such conversion. In this example, by improving the processing speed, performance, and / or efficiency of such one or more processors, a computing system may thereby reduce computational costs associated with such one or more processors. In another example, by comparing an entity respiration signal (e.g., an entity respiration amplitude signal) to a proposed respiration signal in real time and / or providing alignment feedback to the entity in real time according to an exemplary embodiment of the present disclosure, the computing system may thereby eliminate the use of one or more memory devices for storing the above-mentioned input data and / or respiration data that may be indicative of the entity's respiration. In this example, by eliminating the use of one or more memory devices for storing such data, the computing system according to an exemplary embodiment may thereby increase the available storage capacity of such one or more memory devices and / or reduce operational costs associated with such one or more memory devices.

[0041] 1 illustrates a data flow diagram of an exemplary, non-limiting data flow process 100 according to one or more exemplary embodiments of the present disclosure. A computing system described herein may implement the data flow process 100 for facilitating a closed-loop, non-contact respiratory guidance process that provides quantified alignment feedback data in real time according to exemplary embodiments of the present disclosure. For example, a user computing device 710 and / or a server component system 740, described below and shown in FIG. 7, may implement the data flow process 100 for facilitating a closed-loop, non-contact respiratory guidance process that provides quantified alignment feedback data in real time according to exemplary embodiments of the present disclosure.

[0042] 1, the dataflow process 100 may include inputting input data 102 to a tracking algorithm 104. In this embodiment, the dataflow process 100 may further include inputting an output of the tracking algorithm 104 to an alignment algorithm 106, which may output alignment feedback data 108. In at least one embodiment, a user computing device 710 and / or a server component system 740, described below with reference to FIG. 7, may implement the tracking algorithm 104 and / or the alignment algorithm 106 to provide alignment feedback data 108 in real time based at least in part on receiving the input data 102.

[0043] The input data 102 may include and / or consist of respiratory data (not shown) that may be indicative of the entity's breathing (e.g., may be indicative of the entity's current real-time respiratory pattern). For example, the input data 102 may include and / or consist of radar data indicative of the entity's breathing, high frequency radar data indicative of the entity's breathing, sonar data indicative of the entity's breathing, acoustic data indicative of the entity's breathing, video data indicative of the entity's breathing, time series data indicative of the entity's breathing, and / or other data that may be indicative of the entity's breathing.

[0044] In some embodiments, input data 102 may include and / or comprise a combination of different types of input data, each of which may include and / or comprise a particular type of respiratory data that may be indicative of an entity's breathing. For example, input data 102 may include and / or comprise a combination of radar data indicative of an entity's breathing, sonar data indicative of an entity's breathing, and video data indicative of an entity's breathing, for example.

[0045] In at least one embodiment, the input data 102 may include and / or comprise, for example, respiration data that may be indicative of an entity's respiration, and / or may comprise a continuous chirp radar signal, such as a frequency modulated continuous wave (FMCW) radar signal. In this or another embodiment, the continuous chirp radar signal and / or the FMCW radar signal may include and / or comprise a plurality of chirps.

[0046] In at least one embodiment, the input data 102 and / or the respiratory data described above may be obtained from one or more non-contact sources and / or devices that may capture, collect, and / or otherwise obtain the input data 102 and / or respiratory data without physically engaging with the entity (e.g., without contacting the entity). For example, the input data 102 and / or respiratory data may be obtained from a radar device (e.g., a high frequency radar, a real-time motion tracking radar, etc.), a sonar device, a camera device, an audio device (e.g., a microphone, etc.), and / or another non-contact source and / or device that may capture, collect, and / or otherwise obtain the input data 102 and / or respiratory data without physically engaging with the entity. In at least one embodiment in which the input data 102 includes and / or consists of a continuous chirp radar signal (e.g., an FMCW radar signal) described above, such a continuous chirp radar signal may be obtained from a high frequency radar (e.g., an FMCW radar) and / or a real-time motion tracking radar.

[0047] The tracking algorithm 104 may include and / or be configured to have a phase tracking algorithm having a relatively low latency (e.g., compared to other phase tracking algorithms). In one embodiment, the tracking algorithm 104 may convert the respiratory data of the input data 102 to an entity respiratory signal such that the entity respiratory signal may track (e.g., mimic, simulate, replicate, etc.) and / or synchronize with the entity's breathing in real time (e.g., live, in parallel, and / or simultaneously with the entity's breathing). In another embodiment, the tracking algorithm 104 may convert the respiratory data of the input data 102 to an entity respiratory amplitude signal such that the entity respiratory amplitude signal may track (e.g., mimic, simulate, replicate, etc.) and / or synchronize with the entity's breathing in real time (e.g., live, in parallel, and / or simultaneously with the entity's breathing). Details describing how the tracking algorithm 104 may convert respiratory data of the input data 102 into an entity respiratory signal such that the entity respiratory signal may track (e.g., mimic, simulate, replicate, etc.) and / or be synchronized with the entity's breathing in real time (e.g., live, in parallel, and / or simultaneously with the entity's breathing) are provided below with reference to the exemplary embodiment shown in FIG. 2.

[0048] In an embodiment in which the input data 102 includes and / or consists of a continuous chirp radar signal (e.g., an FMCW radar signal) as described above, the tracking algorithm 104 may convert the continuous chirp radar signal into an entity respiration amplitude signal such that the entity respiration amplitude signal may track (e.g., mimic, simulate, replicate, etc.) and / or synchronize with the entity's respiration in real time (e.g., live, in parallel, and / or simultaneously with the entity's respiration). In this embodiment, the tracking algorithm 104 may generate a signal amplitude for the entity respiration amplitude signal upon receipt of each of the plurality of chirps. For example, upon receiving a new chirp of the plurality of chirps (e.g., upon receiving a chirp in real time that has not been previously received), the tracking algorithm 104 may generate a new local amplitude (e.g., a previously ungenerated local amplitude) for the entity respiration amplitude signal. The new local amplitude may correspond to the new chirp. In this manner, the tracking algorithm 104 may thereby convert each chirp of the plurality of chirps, and thus the continuous chirp radar signal, into an entity respiration amplitude signal that may track (e.g., mimic, simulate, replicate, etc.) and / or synchronize with the entity's respiration in real time (e.g., live, in parallel, and / or simultaneously with the entity's respiration). Further details describing how the tracking algorithm 104 may convert the continuous chirp radar signal into an entity respiration amplitude signal such that the entity respiration amplitude signal may track (e.g., mimic, simulate, replicate, etc.) and / or synchronize with the entity's respiration in real time (e.g., live, in parallel, and / or simultaneously with the entity's respiration) are provided below with reference to the exemplary embodiment shown in FIG.

[0049] The alignment algorithm 106 may include, configure, and / or apply a spectral similarity process to compare the entity breathing signal and / or the entity breathing amplitude signal to the proposed breathing signal and / or to determine the degree to which the entity breathing signal and / or the entity breathing amplitude signal is aligned with the proposed breathing signal. The degree to which the entity breathing signal and / or the entity breathing amplitude signal is aligned with the proposed breathing signal may be expressed as an alignment score, which may range, for example, from a value of about zero (0) to a value of about one (1). For example, a value of zero (0) may indicate relatively poor alignment (e.g., no alignment) and / or a value of one (1) may indicate relatively good alignment (e.g., perfect alignment). In this manner, the alignment algorithm 106 may determine an alignment score that may indicate the degree of alignment between the entity breathing signal and / or the entity breathing amplitude signal and the proposed breathing signal. Details describing how the alignment algorithm 106 may compare the entity respiration signal and / or the entity respiration amplitude signal with the proposed respiration signal and / or determine the above-mentioned alignment score are provided below with reference to the exemplary embodiment shown in FIG. 3.

[0050] The suggested breathing signal described above may indicate a suggested breathing pattern. In one embodiment, the suggested breathing signal may indicate a suggested breathing pattern that may be defined and / or recommended, for example, by the user computing device 710 and / or the server component system 740. In this or another embodiment, the suggested breathing and / or suggested breathing signal may be defined, generated, and / or recommended based, for example, at least in part, on one or more attributes and / or biometrics that may correspond to the entity (e.g., the entity's age, weight, height, real-time heart rate and / or average heart rate, real-time blood pressure and / or average blood pressure, etc.).

[0051] Alignment feedback data 108 may include and / or consist of the alignment score described above and / or one or more other types of alignment feedback data that may be indicative of an alignment of the entity respiration signal and / or the entity respiration amplitude signal with the proposed respiration signal. In embodiments in which user computing device 710 and / or server component system 740 implements dataflow process 100, user computing device 710 and / or server component system 740, described below, may generate such one or more other types of alignment feedback data based at least in part on (e.g., using) the alignment score. For example, user computing device 710 and / or server component system 740 may generate such one or more other types of alignment feedback data such that the one or more other types of alignment feedback data correlate and / or correspond to the alignment score.

[0052] The alignment feedback data 108 may include and / or consist of audio data (e.g., a digital voice, a buzzer, an audible alarm, etc.), textual, numerical, and / or alphanumeric data (e.g., letters, numbers, words, written messages, push notifications, etc.), graphical data (e.g., symbols, signs, icons, emojis, etc.), haptic data (e.g., vibration of a device associated with the entity, such as a smartphone, a wearable computing device, etc.), visual data (e.g., a light having an intensity that correlates with and / or varies depending on the degree of alignment), and / or another form of data. In one or more embodiments, the alignment feedback data 108 may include and / or consist of an alignment visualization that may include the entity respiratory signal (e.g., the entity respiratory amplitude signal) and / or the suggested respiratory signal. For example, such an alignment visualization may include and / or consist of a visualization of the entity respiratory signal (e.g., the entity respiratory amplitude signal) overlaid and / or proximate (e.g., superimposed and / or adjacent to) the suggested respiratory signal. In one embodiment, such alignment visualization may include and / or consist of an image (e.g., a still image) of the entity respiratory signal (e.g., the entity respiratory amplitude signal) overlaid and / or proximate (e.g., superimposed and / or adjacent to) the proposed respiratory signal. In another embodiment, such alignment visualization may include and / or consist of a video (e.g., live video, real-time video) of the entity respiratory signal (e.g., the entity respiratory amplitude signal) overlaid and / or proximate (e.g., superimposed and / or adjacent to) the proposed respiratory signal.

[0053] In embodiments in which the user computing device 710 and / or the server component system 740 implement the data flow process 100, the user computing device 710 and / or the server component system 740, described below, may provide alignment feedback data 108 to the entity in real time (e.g., live, in parallel, and / or simultaneously with the entity's breathing) based at least in part on (e.g., in response to) the entity's breathing. For example, based at least in part on (e.g., in response to) receiving input data 102 (e.g., a continuous chirp radar signal), the user computing device 710 and / or the server component system 740 may (e.g., immediately upon receiving the input data 102, in real time) implement a tracking algorithm 104 to convert the respiratory data into an entity respiratory signal (e.g., an entity respiratory amplitude signal), implement an alignment algorithm 106 to compare the entity respiratory signal (e.g., the entity respiratory amplitude signal) to a proposed respiratory signal and / or determine an alignment score corresponding to such signal, and / or provide alignment feedback data 108 (e.g., alignment score, alignment visualization as described above, etc.) to the entity in real time in response to the entity's breathing (e.g., live, in parallel, and / or simultaneously with the entity's breathing). In at least one embodiment, the user computing device 710 and / or server component system 740 may perform such operations in real time while the entity is performing a guided breathing exercise that may be defined and / or communicated to the entity by the user computing device 710 and / or server component system 740, for example, based on the above-described suggested breaths and / or suggested breathing signals that may be defined and / or generated by the user computing device 710 and / or server component system 740 as described above.For example, the user computing device 710 and / or server component system 740 may perform such operations in real time while the entity is attempting to align the entity's breathing with the suggested breathing and / or align the entity breathing signal (e.g., the entity breathing amplitude signal) with the suggested breathing signal.

[0054] 2 illustrates a data flow diagram of an exemplary, non-limiting data flow process 200 according to one or more exemplary embodiments of the present disclosure. A computing system described herein may implement the data flow process 200 that facilitates a closed-loop, non-contact respiratory guidance process that provides quantified alignment feedback data in real time according to exemplary embodiments of the present disclosure. For example, a user computing device 710 and / or a server component system 740 described below and shown in FIG. 7 may implement (e.g., execute, operate, etc.) the tracking algorithm 104 to execute the data flow process 200 that facilitates a closed-loop, non-contact respiratory guidance process that provides quantified alignment feedback data in real time according to exemplary embodiments of the present disclosure.

[0055] The data flow process 200 may include and / or comprise a data flow process for data flowing through the tracking algorithm 104. For example, the data flow process 200 may include and / or comprise a data flow process for the input data 102 and / or the respiratory data described above through the tracking algorithm 104. More specifically, in the exemplary embodiment shown in FIG. 2, the data flow process 200 may include and / or comprise a data flow process for the input data 102 and / or the respiratory data through the tracking algorithm 104 for converting the respiratory data into an entity respiratory signal 214 such that the entity respiratory signal 214 may track (e.g., mimic, simulate, replicate, etc.) and / or synchronize with the entity's breathing in real time (e.g., live, in parallel, and / or simultaneously with the entity's breathing). In at least one embodiment described herein, the entity respiratory signal 214 may include and / or comprise an entity respiratory amplitude signal.

[0056] 2, at 202, the tracking algorithm 104 may remove clutter from the input data 102. For example, the tracking algorithm 104 may use an exponential filter and / or an exponential smoothing process to remove noise data from the input data 102 and / or the respiratory data. The noise data may include and / or consist of data that may be indicative of at least one movement corresponding to one or more second entities (e.g., movement of an object and / or another person).

[0057] At 204, the tracking algorithm 104 may use an exponential filter and / or an exponential smoothing process to map a range that may be associated with the input data 102 and / or the respiratory data. The range may include and / or constitute at least a portion of the entity's respiratory data.

[0058] At 206, the tracking algorithm 104 may normalize the ranges into a range probability map that may include and / or comprise a plurality of range bins, each of which may include and / or comprise at least a portion of the respiration data.

[0059] At 208, the tracking algorithm 104 may apply one or more inertial functions to the range probability map and / or the plurality of range bins to determine a center of mass that may correspond to the range probability map and / or the plurality of range bins.

[0060] At 210, the tracking algorithm 104 may extract (e.g., from the probability map) phase data that may correspond to the center of mass. The phase data may represent a phase signal (e.g., a wrapped phase signal or an unwrapped phase signal) that may correspond to the center of mass.

[0061] At 212, the tracking algorithm 104 may apply a filter to the phase signal to obtain and output an entity respiration signal 214 (e.g., an entity respiration amplitude signal). The filter may be operable to remove data that may be indicative of defined entity movements that may be associated with the entity's respiration (e.g., subtle movements the entity makes while inhaling and / or exhaling).

[0062] In embodiments where the input data 102 includes and / or comprises a combination of different types of input data, each having a different type of respiratory data as described above, the tracking algorithm 104 may perform the operations described above with respect to each different type of input data to convert all of the different types of respiratory data into the entity respiratory signal 214. For example, in embodiments where the input data 102 includes and / or comprises a combination of, for example, radar data indicative of the entity's breathing, sonar data indicative of the entity's breathing, and video data indicative of the entity's breathing, the tracking algorithm 104 may perform the operations described above with respect to each different type of input data to convert all of the different types of respiratory data into the entity respiratory signal 214. For example, in these embodiments, the tracking algorithm 104 may convert all of the different types of respiratory data into a single entity respiratory signal 214 such that the entity respiratory signal 214 may include, comprise, and / or take into account all of such different types of respiratory data.

[0063] 3 illustrates a data flow diagram of an exemplary, non-limiting data flow process 300 according to one or more exemplary embodiments of the present disclosure. A computing system described herein may implement the data flow process 300 that facilitates a closed-loop, non-contact respiratory guidance process that provides quantified alignment feedback data in real time according to exemplary embodiments of the present disclosure. For example, a user computing device 710 and / or a server component system 740 described below and shown in FIG. 7 may implement (e.g., execute, operate, etc.) the alignment algorithm 106 to execute the data flow process 300 that facilitates a closed-loop, non-contact respiratory guidance process that provides quantified alignment feedback data in real time according to exemplary embodiments of the present disclosure.

[0064] The data flow process 300 may include and / or comprise a data flow process for data flowing through the alignment algorithm 106. For example, the data flow process 300 may include and / or comprise a data flow process for the entity respiratory signal 214 (e.g., the entity respiratory amplitude signal) through the alignment algorithm 106. More specifically, in the exemplary embodiment shown in FIG. 3, the data flow process 300 may include and / or comprise a data flow process for the entity respiratory signal 214 through the alignment algorithm 106 to compare the entity respiratory signal 214 to a proposed respiratory signal and / or to determine the degree to which the entity respiratory signal 214 is aligned with the proposed respiratory signal. That is, for example, the data flow process 300 may include and / or comprise a data flow process for the entity respiratory signal 214 through the alignment algorithm 106 to determine an alignment score 310.

[0065] 3, at 302, the alignment algorithm 106 may calculate a spectral vector of the respiratory data for phase invariance by calculating a first spectral vector that may correspond to the entity's respiratory data and / or the entity's respiratory signal 214 (e.g., the entity's respiratory amplitude signal) and calculating a second spectral vector that may correspond to the proposed respiratory data (e.g., data indicative of a proposed respiratory signal) and / or the corresponding proposed respiratory signal. In at least one embodiment, the proposed respiratory data and / or the corresponding proposed respiratory signal may be defined, generated, and / or recommended by the user computing device 710 and / or the server component system 740, for example, as described above.

[0066] At 304, the alignment algorithm 106 may apply a normalization function to the first and second spectral vectors to calculate first and second normalized spectral vectors, respectively. For example, the alignment algorithm 106 may apply an L2 normalization function to the first and second spectral vectors to calculate first and second L2 normalized spectral vectors, respectively.

[0067] At 306, the alignment algorithm 106 may calculate an alignment score for the first and second L2 normalized spectral vectors. For example, the alignment algorithm 106 may perform a dot product operation to calculate the alignment score for the first and second L2 normalized spectral vectors.

[0068] At 308, the alignment algorithm 106 may apply a softmax function to the alignment scores to obtain and output alignment scores 310. The alignment algorithm 106 may apply the softmax function to improve the dynamic range associated with the alignment scores 310.

[0069] 4 illustrates a data flow diagram of an exemplary, non-limiting data flow process 400 according to one or more exemplary embodiments of the present disclosure. A computing system described herein may implement the data flow process 400 that facilitates a closed-loop, non-contact respiratory guidance process that provides quantified alignment feedback data in real time according to exemplary embodiments of the present disclosure. For example, a user computing device 710 and / or a server component system 740 described below and shown in FIG. 7 may implement (e.g., execute, operate, etc.) the tracking algorithm 104 to execute the data flow process 400 that facilitates a closed-loop, non-contact respiratory guidance process that provides quantified alignment feedback data in real time according to exemplary embodiments of the present disclosure.

[0070] The data flow process 400 may include and / or configure a data flow process for data flowing through the tracking algorithm 104. The data flow process 400 may include and / or configure an exemplary non-limiting alternative embodiment of the data flow process 200 described above and shown in FIG. 2. For example, the data flow process 400 may include and / or configure a data flow process for a continuous chirp radar signal 402 through the tracking algorithm 104 rather than the input data 102. More specifically, in the exemplary embodiment shown in FIG. 4, the data flow process 400 may include and / or configure a data flow process for a continuous chirp radar signal 402 through the tracking algorithm 104 to convert the continuous chirp radar signal 402 to an entity respiration amplitude signal 418 such that the entity respiration amplitude signal 418 may track (e.g., mimic, simulate, replicate, etc.) and / or be synchronized with the entity's respiration in real time (e.g., live, in parallel, and / or simultaneously with the entity's respiration).

[0071] 4, at 404, the tracking algorithm 104 may remove clutter from the continuous chirp radar signal 402. For example, the tracking algorithm 104 may use an exponential filter and / or an exponential smoothing process to remove noise data from the continuous chirp radar signal 402. The noise data may include and / or consist of data that may be indicative of at least one movement corresponding to one or more second entities (e.g., movement of an object and / or another person).

[0072] At 406, the tracking algorithm 104 may use an exponential filter and / or an exponential smoothing process to map a range that may be associated with the continuous chirp radar signal 402. The range may include and / or constitute at least a portion of the entity's respiration data.

[0073] At 408, the tracking algorithm 104 may normalize the ranges into a range probability map that may include and / or comprise a plurality of range bins, each of which may include and / or comprise at least a portion of the respiration data.

[0074] At 410, the tracking algorithm 104 may apply one or more inertial functions to the range probability map and / or the multiple range bins to determine a center of mass that may correspond to the range probability map and / or the multiple range bins.

[0075] At 412, the tracking algorithm 104 may extract (e.g., from the probability map) phase data that may correspond to the center of mass. The phase data may represent a wrapped phase signal that may correspond to the center of mass.

[0076] At 414, the tracking algorithm 104 may perform a signal phase unwrapping process on the phase data and / or the wrapped phase signal to obtain a continuous phase signal that may correspond to the center of mass.

[0077] At 416, the tracking algorithm 104 may apply a filter to the continuous phase signal to obtain and output an entity respiration amplitude signal 418. The filter may be operable to remove data that may be indicative of defined entity movements that may be associated with the entity's respiration (e.g., subtle movements that the entity makes while inhaling and / or exhaling).

[0078] In additional and / or alternative embodiments, the entity respiration amplitude signal 418 may be input to an alignment algorithm 106 that compares the entity respiration amplitude signal 418 to a proposed respiration signal and / or determines the degree to which the entity respiration amplitude signal 418 is aligned with a proposed respiration signal. That is, for example, the entity respiration amplitude signal 418 may be input (e.g., by the user computing device 710 and / or the server component system 740) to the alignment algorithm 106 to determine an alignment score 310 as described above with reference to FIG.

[0079] 5A and 5B show diagrams of example, non-limiting signal evaluation processes 500a and 500b, respectively, according to one or more example embodiments of the present disclosure. A computing system described herein may perform the signal evaluation process 500a and / or 500b to facilitate a closed-loop, non-contact respiratory guidance process that provides quantified alignment feedback data in real time, according to example embodiments of the present disclosure. For example, a user computing device 710 and / or a server component system 740, described below and shown in FIG. 7, may implement (e.g., execute, operate, etc.) the tracking algorithm 104 and / or the alignment algorithm 106 to perform the signal evaluation process 500a and / or 500b to facilitate a closed-loop, non-contact respiratory guidance process that provides quantified alignment feedback data in real time, according to example embodiments of the present disclosure.

[0080] As described above with reference to Figures 1, 2, 3, and 4, the user computing device 710 and / or server component system 740 may implement (e.g., execute, operate, etc.) the tracking algorithm 104 to generate the entity respiration signal 214. In this example, the user computing device 710 and / or server component system 740 may generate the suggested respiration signal 502 corresponding to a suggested respiration (e.g., data indicative of a suggested respiration), which may be defined and / or recommended by the user computing device 710 and / or server component system 740 as described above. In this example, the user computing device 710 and / or server component system 740 may implement (e.g., execute, operate, etc.) the alignment algorithm 106 to calculate a spectral vector 504, which may correspond to the entity's respiration data and / or the entity respiration signal 214 (e.g., the entity respiration amplitude signal), and a spectral vector 506, which may correspond to the suggested respiration (e.g., data indicative of a suggested respiration) and / or the suggested respiration signal 502. In this embodiment, the user computing device 710 and / or the server component system 740 may implement (e.g., execute, operate, etc.) the alignment algorithm 106 to calculate the spectral vector 504 and the spectral vector 506 in the inner product space 508 as shown in Figures 5A and 5B.

[0081] In the exemplary embodiments shown in Figures 5A and 5B, the dot product space 508 may include and / or constitute an L2 normalized vector space. In these embodiments, the spectral vector 504 and the spectral vector 506 may each describe and / or correspond to a normalized vector (e.g., an L2 normalized vector). In embodiments where the spectral vector 504 and the spectral vector 506 are close to each other (e.g., as shown by the depiction of the spectral vector 504 and the spectral vector 506 in the dot product space 508 shown in Figure 5A), that means that the dot product is maximized. In embodiments where the spectral vector 504 and the spectral vector 506 are not close to each other, e.g., they are farther away from each other (e.g., as shown by the depiction of the spectral vector 504 and the spectral vector 506 in the dot product space 508 shown in Figure 5B), that means that the dot product is smaller. In the above embodiments, this is because, mathematically, the dot product of the L2 normalized vectors is used to measure the "angle" between the L2 normalized vectors in the dot product space. 5A and 5B, user computing device 710 and / or server component system 740 may implement alignment algorithm 106 to take a dot product of spectral vector 504 and spectral vector 506 as described herein to determine the angle between spectral vector 504 and spectral vector 506 in dot product space 508. In these or other exemplary embodiments, user computing device 710 and / or server component system 740 may thereby determine the degree to which the entity's breathing (e.g., represented by entity breathing signal 214 in FIGS. 5A and 5B) is aligned with the proposed breathing (e.g., represented by proposed breathing signal 502 in FIGS. 5A and 5B).

[0082] In one embodiment, based on performing signal evaluation process 500a, user computing device 710 and / or server component system 740 may determine that entity respiratory signal 214 has a relatively good alignment with proposed respiratory signal 502. Accordingly, user computing device 710 and / or server component system 740 may further calculate and / or output (e.g., via alignment algorithm 106) an alignment score (e.g., alignment score 310) having a value reflecting such a relatively good alignment of entity respiratory signal 214 with proposed respiratory signal 502 (e.g., a value of about 1).

[0083] In another embodiment, based on performing signal evaluation process 500b, user computing device 710 and / or server component system 740 may determine that entity respiratory signal 214 has relatively poor alignment with proposed respiratory signal 502. Accordingly, user computing device 710 and / or server component system 740 may further calculate and / or output (e.g., via alignment algorithm 106) an alignment score (e.g., alignment score 310) having a value (e.g., a value of about zero (0)) reflecting such relatively poor alignment of entity respiratory signal 214 with proposed respiratory signal 502.

[0084] FIG. 6 illustrates a diagram of exemplary non-limiting alignment feedback data 600, according to one or more exemplary embodiments of the present disclosure. The alignment feedback data 600 may include and / or consist of exemplary non-limiting embodiments of the alignment feedback data 108 described above with reference to FIG. 1. For example, the alignment feedback data 600 may include and / or consist of exemplary non-limiting embodiments of alignment visualization described above, which may include and / or consist of visualizations of the entity respiratory signal (e.g., the entity respiratory amplitude signal) and / or the proposed respiratory signal. In one embodiment, the alignment feedback data 600 may include and / or consist of images (e.g., still images) of the entity respiratory signal (e.g., the entity respiratory amplitude signal) and / or the proposed respiratory signal. In another embodiment, the alignment feedback data 600 may include and / or consist of video (e.g., live video, real-time video) of the entity respiratory signal (e.g., the entity respiratory amplitude signal) and / or the proposed respiratory signal.

[0085] A computing system according to an exemplary embodiment of the present disclosure may implement one or more of the processes, algorithms, and / or methods (e.g., computer-implemented methods) described herein to generate alignment feedback data 600. For example, to generate alignment feedback data 600, user computing device 710 and / or server component system 740, described below and shown in FIG. 7, may implement dataflow process 100, tracking algorithm 104, alignment algorithm 106, dataflow process 200, dataflow process 300, dataflow process 400, signal evaluation processes 500a and / or 500b, and / or computer-implemented methods 800 and / or 900, described below and shown in FIG. 8 and FIG. 9, respectively.

[0086] 6, the alignment feedback data 600 may include an entity respiration signal plot 602 of an entity respiration curve 606 and / or an alignment score plot 604 of an alignment score curve 608. In this embodiment, the entity respiration signal plot 602 illustrates respiration amplitude values ​​of the entity respiration curve 606 over time, and the alignment score plot 604 illustrates alignment score values ​​of the alignment score curve 608 over time.

[0087] In one embodiment, the entity respiration curve 606 may correspond to and / or represent the entity respiration signal 214. In another embodiment, the entity respiration curve 606 may correspond to and / or represent the entity respiration amplitude signal 418. In any of the above embodiments, the alignment score curve 608 may correspond to and / or represent the alignment score 310. In the exemplary embodiment shown in FIG. 6, the entity respiration signal plot 602 and the alignment score plot 604 indicate that the entity respiration signal corresponding to the entity respiration curve 606 (e.g., the entity respiration signal 214 or the entity respiration amplitude signal 418) has a relatively good alignment with the proposed respiration signal (not shown).

[0088] 7 illustrates a block diagram of an exemplary, non-limiting computing system 700 in accordance with one or more exemplary embodiments of the present disclosure. The computing system 700 may include a user computing device 710 and / or a server computing system 740 that may be communicatively coupled via a network 730. The computing system 700, the user computing device 710, and / or the server component system 740 may be used to implement one or more of the processes, algorithms, and / or methods (e.g., computer-implemented methods) described herein to facilitate a closed-loop, non-contact respiratory guidance process that provides quantified alignment feedback data in real time.

[0089] The user computing device 710 may be any type of computing device, such as, for example, a personal computing device (e.g., a laptop or desktop), a mobile computing device (e.g., a smartphone or tablet), a game console or controller, a wearable computing device, an embedded computing device, or any other type of computing device.

[0090] The user computing device 710 includes one or more processors 712 and memory 714. The one or more processors 712 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be a single processor or multiple processors operably connected. The memory 714 may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 714 may store data 716 and instructions 718 that are executed by the processor 712 to cause the user computing device 710 to perform operations, such as any of the operations described herein. In at least one embodiment, the data 716 and / or instructions 718 may include and / or configure, for example, a tracking algorithm 104 and / or an alignment algorithm 106.

[0091] The user computing device 710 may also include one or more user input components 720 that receive, acquire, capture, and / or collect input data (e.g., user input, input data 102, respiratory data as described above, etc.). For example, the user input component 720 may be a touch-sensitive component (e.g., a touch-sensitive display screen or touchpad) that senses the touch of a user input object (e.g., a finger or stylus). The touch-sensitive component may serve to implement a virtual keyboard. Other exemplary user input components include a microphone, a traditional keyboard, or other means by which a user may provide user input. In at least one embodiment, the user input component 720 may include and / or consist of the non-contact sources and / or devices described above that may capture, collect, and / or otherwise obtain input data (e.g., input data 102) and / or respiratory data of an entity without physically engaging with the entity (e.g., without touching the entity). For example, the user input component 720 may include and / or consist of a radar device (e.g., a high frequency radar, a real-time motion tracking radar, an FMCW radar, etc.), a sonar device, a camera device, an audio device (e.g., a microphone, etc.), and / or another non-contact source and / or device that may capture, collect, and / or otherwise obtain such input data and / or respiratory data without physically engaging with the entity. The user computing device 710 may also include a user output component 722. The user output component 722 may provide information and may include, for example, a display screen, an audio output device, a haptic device, or other suitable device.

[0092] The server computing system 740 includes one or more processors 742 and memory 744. The one or more processors 742 may be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be a single processor or multiple processors operably connected. The memory 744 may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 744 may store data 746 and instructions 748 that are executed by the processor 742 to cause the server computing system 740 to perform operations, such as any of the operations described herein.

[0093] In some implementations, server computing system 740 includes or is implemented by one or more server computing devices. When server computing system 740 includes multiple server computing devices, such server computing devices may operate according to a sequential computing architecture, a parallel computing architecture, or some combination thereof.

[0094] Network 730 may be any type of communications network, such as a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or some combination thereof, and may include any number of wired or wireless links. In general, communications over network 7300 may occur over any type of wired and / or wireless connections, using a wide variety of communications protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, Secure HTTP, SSL).

[0095] 8 illustrates a flow diagram of an exemplary, non-limiting computer-implemented method 800 in accordance with one or more exemplary embodiments of the present disclosure. The computer-implemented method 800 may be implemented using, for example, the computing system 700, the user computing device 710, and / or the server component system 740 described above with reference to FIG. 7. The exemplary embodiment illustrated in FIG. 8 illustrates operations performed in a particular order for purposes of illustration and description. Those skilled in the art will appreciate, with the disclosure provided herein, that the various operations or steps of the computer-implemented method 800, or any of the other methods disclosed herein, may be adapted, modified, rearranged, performed simultaneously, include operations not shown, and / or modified in various ways without departing from the scope of the present disclosure.

[0096] At 802, the computer-implemented method 800 may include receiving, by a computing system (e.g., computing system 700, user computing device 710, and / or server component system 740) operably coupled to one or more processors (e.g., one or more processors 712), input data (e.g., input data 102, continuous chirp radar signal 402, etc.) including respiratory data indicative of an entity's respiration.

[0097] At 804, the computer-implemented method 800 may include converting, by the computing system (e.g., via tracking algorithm 104, data flow process 200, data flow process 400, etc.), the respiratory data into an entity respiratory signal (e.g., entity respiratory signal 214, entity respiratory amplitude signal 418, etc.) such that the entity respiratory signal tracks (e.g., mimics, simulates, replicates, etc.) the entity's respiratory signal in real time (e.g., live, in parallel, and / or simultaneously with the entity's respiratory signal).

[0098] At 806, the computer-implemented method 800 may include comparing, by the computing system (e.g., via the alignment algorithm 106, the data flow process 300, the signal evaluation processes 500a and / or 500b, etc.), the entity respiratory signal to a suggested respiratory signal indicative of a suggested breath (e.g., the suggested respiratory signal 502, or another suggested respiratory signal that may be generated by the user computing device 710 and / or the server component system 740 as described above with reference to Figures 1, 2, 3, 4, 5A and 5B).

[0099] At 808, the computer-implemented method 800 may include providing, by the computing system (e.g., via the user output component 722, the network 730, a WPAN, etc.), alignment feedback data (e.g., alignment feedback data 108, alignment score 310, alignment feedback data 600, alignment feedback data 1000a and / or 1000b, described below with reference to Figures 10A and 10B, respectively) to the entity in real time (e.g., live, in parallel, and / or simultaneously with the entity's breathing) based at least in part on the entity's breathing. The alignment feedback data is indicative of an alignment of the entity's breathing signal and the proposed breathing signal.

[0100] 9 illustrates a flow diagram of an exemplary, non-limiting computer-implemented method 900 in accordance with one or more exemplary embodiments of the present disclosure. The computer-implemented method 900 may be implemented using, for example, the computing system 700, the user computing device 710, and / or the server component system 740 described above with reference to FIG. 7. The exemplary embodiment illustrated in FIG. 9 illustrates operations performed in a particular order for purposes of illustration and description. Those skilled in the art will appreciate, with the disclosure provided herein, that the various operations or steps of the computer-implemented method 900, or any of the other methods disclosed herein, may be adapted, modified, rearranged, performed simultaneously, may include operations not shown, and / or may be modified in various ways without departing from the scope of the present disclosure.

[0101] At 902, the computer-implemented method 900 may include receiving, by a computing system (e.g., the user computing device 710 and / or the server component system 740) operatively coupled to one or more processors (e.g., the one or more processors 712), a continuous chirp radar signal (e.g., the continuous chirp radar signal 402) including respiration data indicative of an entity's respiration. The continuous chirp radar signal includes a plurality of chirps.

[0102] At 904, the computer-implemented method 900 may include converting, by the computing system (e.g., via the tracking algorithm 104, the data flow process 400, etc.), the continuous chirp radar signal into an entity respiration amplitude signal (e.g., such as the entity respiration amplitude signal 418) such that the entity respiration amplitude signal tracks (e.g., mimics, simulates, replicates, etc.) the entity's respiration in real time (e.g., live, in parallel, and / or simultaneously with the entity's respiration). A signal amplitude of the entity respiration amplitude signal is generated upon receipt of each of the multiple chirps.

[0103] At 906, the computer-implemented method 900 may include comparing, by the computing system (e.g., via the alignment algorithm 106, the data flow process 300, the signal evaluation processes 500a and / or 500b, etc.), the entity respiration amplitude signal to a suggested respiration signal indicative of a suggested respiration (e.g., the suggested respiration signal 502, or another suggested respiration signal that may be generated by the user computing device 710 and / or the server component system 740 as described above with reference to Figures 1, 2, 3, 4, 5A and 5B).

[0104] At 908, the computer-implemented method 900 may include providing, by the computing system (e.g., via the user output component 722, the network 730, a WPAN, etc.), alignment feedback data (e.g., alignment feedback data 108, the alignment score 310, the alignment feedback data 600, and / or alignment feedback data 1000a and / or 1000b described below with reference to Figures 10A and 10B, respectively) to the entity in real time (e.g., live, in parallel, and / or simultaneously with the entity's breathing) based at least in part on the entity's breathing. The alignment feedback data is indicative of an alignment of the entity's breathing amplitude signal and the proposed breathing signal.

[0105] 10A and 10B show diagrams of exemplary non-limiting alignment feedback data 1000a and 1000b, respectively, in accordance with one or more exemplary embodiments of the present disclosure. The alignment feedback data 1000a and / or 1000b may include and / or consist of the exemplary non-limiting embodiment of the alignment feedback data 108 described above with reference to FIG. 1. Additionally or alternatively, the alignment feedback data 1000a and / or 1000b may include and / or consist of the exemplary non-limiting alternative embodiment of the alignment feedback data 600 described above with reference to FIG. 6. For example, the alignment feedback data 1000a and / or 1000b may include and / or consist of the exemplary non-limiting embodiment of the alignment visualization described above, which may include and / or consist of a visualization of an entity respiratory signal (e.g., an entity respiratory amplitude signal) overlaid and / or proximate (e.g., superimposed and / or adjacent to) a proposed respiratory signal. In one embodiment, alignment feedback data 1000a and / or 1000b may include and / or consist of an image (e.g., a still image) of the entity respiratory signal (e.g., the entity respiratory amplitude signal) overlaid and / or proximate (e.g., superimposed and / or adjacent to) the proposed respiratory signal. In another embodiment, alignment feedback data 1000a and / or 1000b may include and / or consist of a video (e.g., live video, real-time video) of the entity respiratory signal (e.g., the entity respiratory amplitude signal) overlaid and / or proximate (e.g., superimposed and / or adjacent to) the proposed respiratory signal.

[0106] A computing system according to an exemplary embodiment of the present disclosure may implement one or more of the processes, algorithms, and / or methods (e.g., computer-implemented methods) described herein to generate alignment feedback data 1000a and / or 1000b. For example, to generate alignment feedback data 1000a and / or 1000b, user computing device 710 and / or server component system 740 may implement dataflow process 100, tracking algorithm 104, alignment algorithm 106, dataflow process 200, dataflow process 300, dataflow process 400, signal evaluation process 500a and / or 500b, and / or computer-implemented method 800 and / or 900. In one exemplary embodiment, user computing device 710 and / or server component system 740 may implement data flow process 100, tracking algorithm 104, and / or alignment algorithm 106 to perform the above-described comparison of an entity respiratory signal (e.g., entity respiratory signal 214 or entity respiratory amplitude signal 418) to a proposed respiratory signal (e.g., proposed respiratory signal 502) and / or determine the degree to which such entity respiratory signal is aligned (or not aligned) with such proposed respiratory signal, as described above with reference to Figures 1-4. In this embodiment, user computing device 710 and / or server component system 740 may generate alignment feedback data 1000a and / or 1000b based at least in part on such comparison and the determination of the degree of alignment of such entity respiratory signal to such proposed respiratory signal.It should be appreciated that the user computing device 710 and / or server component system 740 may generate and / or provide alignment feedback data 1000a and / or 1000b to an entity implementing one or more embodiments described herein to provide the entity with user-friendly alignment feedback data that may enable the entity to conveniently, easily, and / or quickly interpret the data to understand how well or poorly the entity is mimicking (e.g., simulating) the proposed breathing signal.

[0107] 10A and 10B, alignment feedback data 1000a and 1000b may each include an entity respiratory signal representation 1002 and / or a proposed respiratory signal representation 1004. In this embodiment, the entity respiratory signal representation 1002 may be overlaid on and / or proximate to the proposed respiratory signal representation 1004 (e.g., superimposed on and / or proximate to the proposed respiratory signal representation 1004).

[0108] The entity respiratory signal representation 1002 may correspond to and / or represent an entity respiratory signal, such as, for example, the entity respiratory signal 214 or the entity respiratory amplitude signal 418. The entity respiratory signal representation 1002 may track (e.g., mimic, simulate, replicate, etc.) such an entity respiratory signal in real time (e.g., live, in parallel, and / or simultaneously with the breathing of the entity). The proposed respiratory signal representation 1004 may correspond to and / or represent a proposed respiratory signal, such as, for example, the proposed respiratory signal 502. The proposed respiratory signal representation 1004 may track (e.g., mimic, simulate, replicate, etc.) such a proposed respiratory signal in real time (e.g., live, in parallel, and / or simultaneously with the proposed respiratory signal).

[0109] 10A, the alignment feedback data 1000a indicates that the entity respiratory signal corresponding to the entity respiratory signal representation 1002 (e.g., entity respiratory signal 214 or entity respiratory amplitude signal 418) has a relatively poor alignment with the proposed respiratory signal corresponding to the proposed respiratory signal representation 1004 (e.g., proposed respiratory signal 502) as indicated by the relatively poor alignment between the entity respiratory signal representation 1002 and the proposed respiratory signal representation 1004. In the exemplary embodiment shown in FIG. 10B, the alignment feedback data 1000b indicates that the entity respiratory signal corresponding to the entity respiratory signal representation 1002 (e.g., entity respiratory signal 214 or entity respiratory amplitude signal 418) has a relatively good alignment with the proposed respiratory signal corresponding to the proposed respiratory signal representation 1004 (e.g., proposed respiratory signal 502) as indicated by the relatively good alignment between the entity respiratory signal representation 1002 and the proposed respiratory signal representation 1004.

[0110] Although the present subject matter has been described in detail with respect to various specific and exemplary embodiments thereof, each example is provided for the purpose of illustration and not to limit the present disclosure. Those skilled in the art, upon understanding the above content, may easily make modifications, variations, and equivalents of such embodiments. Thus, the present disclosure does not exclude the inclusion of such modifications, variations, and / or additions to the present subject matter as would be readily apparent to those skilled in the art. For example, features illustrated or described as part of one embodiment can be used with another embodiment to produce yet another embodiment. Thus, the present disclosure is intended to cover such modifications, variations, and equivalents.

Claims

1. 1. A computing system comprising: one or more processors; one or more non-transitory computer-readable storage media storing instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations including: receiving input data including respiration data indicative of respiration of an entity; converting said respiration data into an entity respiration signal such that the entity respiration signal tracks respiration of said entity in real time; comparing the entity breathing signal to a proposed breathing signal indicative of a proposed breath; and and providing alignment feedback data to the entity in real time based at least in part on the entity's respiration, the alignment feedback data indicating an alignment between the entity's respiration signal and the proposed respiration signal.

2. The operation includes: The computing system of claim 1 , further comprising determining an alignment score indicative of a degree of alignment between the entity respiratory signal and the proposed respiratory signal.

3. The computing system of claim 1 , wherein the alignment feedback data comprises an alignment score indicative of a degree of alignment between the entity respiratory signal and the proposed respiratory signal.

4. The computing system of claim 1 , wherein the alignment feedback data comprises an alignment visualization including at least one of the entity respiratory signal or the proposed respiratory signal.

5. 2. The computing system of claim 1 , wherein at least one of the input data or the respiration data comprises at least one of radar data indicative of the entity's respiration, high frequency radar data indicative of the entity's respiration, sonar data indicative of the entity's respiration, acoustic data indicative of the entity's respiration, video data indicative of the entity's respiration, or time series data indicative of the entity's respiration.

6. 1. A computer-implemented method comprising: receiving, by a computing system operatively coupled to one or more processors, input data including respiration data indicative of respiration of an entity; converting, by the computing system, the respiratory data into an entity respiratory signal such that the entity respiratory signal tracks respiration of the entity in real time; comparing, by the computing system, the entity respiration signal to a suggested respiration signal indicative of a suggested respiration; and providing, by the computing system, alignment feedback data to the entity in real time based at least in part on the entity's respiration, the alignment feedback data indicating an alignment between the entity's respiration signal and the proposed respiration signal.

7. The computer-implemented method of claim 6 , further comprising determining, by the computing system, an alignment score indicative of the degree of alignment between the entity respiratory signal and the proposed respiratory signal.

8. Providing, by the computing system, the alignment feedback data to the entity in real time based at least in part on respiration of the entity, further comprising: The computer-implemented method of claim 6 comprising providing, by the computing system, an alignment score indicative of the degree of alignment between the entity respiratory signal and the proposed respiratory signal.

9. Providing, by the computing system, the alignment feedback data to the entity in real time based at least in part on respiration of the entity, further comprising: The computer-implemented method of claim 6 , comprising providing, by the computing system, an alignment visualization including at least one of the entity respiratory signal or the proposed respiratory signal.

10. 7. The computer-implemented method of claim 6, wherein at least one of the input data or the respiration data comprises at least one of radar data indicative of the entity's respiration, high frequency radar data indicative of the entity's respiration, sonar data indicative of the entity's respiration, acoustic data indicative of the entity's respiration, video data indicative of the entity's respiration, or time series data indicative of the entity's respiration.

11. A computing system as claimed in particular in claims 1 to 5, one or more processors; one or more non-transitory computer-readable storage media storing instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations including: receiving a continuous chirp radar signal including respiration data indicative of respiration of an entity, the continuous chirp radar signal including a plurality of chirps, the operations including: converting the continuous chirp radar signal into an entity respiration amplitude signal such that the entity respiration amplitude signal tracks respiration of the entity in real time, a signal amplitude of the entity respiration amplitude signal being generated upon receipt of each of the plurality of chirps, the operations comprising: comparing the entity respiration amplitude signal to a proposed respiration signal indicative of a proposed respiration; and and providing alignment feedback data to the entity in real time based at least in part on the entity's respiration, the alignment feedback data indicating an alignment between the entity respiration amplitude signal and the proposed respiration signal.

12. The operation includes: The computing system of claim 11 , further comprising determining an alignment score indicative of a degree of alignment between the entity respiratory amplitude signal and the proposed respiratory signal.

13. 12. The computing system of claim 11, wherein the alignment feedback data comprises at least one of an alignment score indicating a degree of alignment between the entity respiratory amplitude signal and the proposed respiratory signal, or an alignment visualization comprising at least one of the entity respiratory amplitude signal or the proposed respiratory signal.

14. converting the continuous chirp radar signal to an entity respiration amplitude signal such that the entity respiration amplitude signal tracks respiration of the entity in real time; 12. The computing system of claim 11, further comprising removing noise data from the continuous chirp radar signal, the noise data including data indicative of at least one movement corresponding to one or more second entities.

15. The operation includes: The computing system of claim 14 further comprising mapping a range associated with the continuous chirp radar signal, the range including at least a portion of the respiration data.

16. The operation includes:

16. The computing system of claim 15, further comprising: normalizing the ranges into a range probability map comprising a plurality of range bins, each of the plurality of range bins comprising at least a portion of the respiratory data.

17. The operation includes:

20. The computing system of claim 16, further comprising applying one or more inertia functions to the range probability map or at least one of the plurality of range bins to determine a center of mass corresponding to the range probability map or at least one of the plurality of range bins.

18. The operation includes:

20. The computing system of claim 17, further comprising extracting phase data corresponding to the center of mass, the phase data representing a wrapped phase signal corresponding to the center of mass.

19. The operation includes:

20. The computing system of claim 18, further comprising: performing a signal phase unwrapping process on at least one of the phase data or the wrapped phase signal to obtain a continuous phase signal corresponding to the center of mass.

20. The operation includes:

20. The computing system of claim 19, further comprising applying a filter to the continuous phase signal to obtain the entity respiration amplitude signal, the filter operable to remove data indicative of defined entity movement associated with the entity's respiration.

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