Method, system and program product for characterizing user interface

Abstract

To provide a system and a method for classifying and / or characterizing a user interface on the basis of an acoustic reflection of an acoustic signal.SOLUTION: AA system receives generated acoustic data which is associated with an acoustic reflection of an acoustic signal, the acoustic reflection indicates at least partially, one or more characteristics of at least one of a user interface and a conduit, the user interface is coupled to a respiratory therapy device through a conduit and analyzes the generated acoustic data. The analysis of the acoustic data includes windowing the generated acoustic data on the basis of at least partially, the at least one characteristic out of one or more characteristics of at least one of the user interface and conduit. The at least one characteristic includes a known length of the conduit. The user interface is characterized on the basis of at least partially, the windowed and analyzed acoustic data.SELECTED DRAWING: Figure 6

Description

Technical Field

[0001] The present disclosure generally relates to systems and methods for classifying and / or characterizing user interfaces, and more particularly, to systems and methods for classifying and / or characterizing user interfaces based on acoustic reflections of acoustic signals.

Background Art

[0002] Many people suffer from sleep-related and / or respiratory disorders such as periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), central sleep apnea (CSA), and other types of apnea such as mixed apnea and hypopnea, respiratory effort-related arousal (RERA), Cheyne-Stokes respiration (CSR), respiratory insufficiency, obesity hypoventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), rapid eye movement (REM) behavior disorder (also called RBD), dream enactment behavior (DEB), hypertension, diabetes, stroke, insomnia, and chest wall disorders. These disorders are typically treated using a respiratory therapy system. A respiratory therapy system can be used to treat sleep-disordered breathing. Each respiratory therapy system generally has a respiratory therapy device connected to a user interface (e.g., a mask) via a conduit. The user wears the user interface and is supplied with a pressurized air flow from the respiratory therapy device via the conduit.

[0003] The user interface is generally a specific category and type of user interface for users, including, as a category of the user interface, direct or indirect connection, etc., and as a type of the user interface, a full-face mask, a partial face mask (for example, a mask that covers the mouth but not the nose, or a mask that covers only a part of the nose), a nasal mask, a nasal pillow, etc. In addition to the specific category and type, the user interface is usually a specific model manufactured by a specific manufacturer. Recognizing the specific category and type, and thus the specific model, of the user interface worn by the user is beneficial to the respiratory therapy system for various reasons, such as to confirm that the user is using the correct user interface.

[0004] Various different user interfaces can be used, such as nasal pillows, nasal masks, nose and mouth masks (for example, full-face masks or partial face masks). In some implementations, different forms of air delivery conduits can be used. It is advantageous to recognize the user interface and conduit connected to the respiratory therapy device in order to improve the control of the treatment provided to the user. For example, it may be advantageous for the measurement or estimation of treatment parameters such as the pressure and ventilation flow within the mask. Therefore, by recognizing which user interface is being used, the treatment can be enhanced.

[0005] The breathing device can include a menu system through which the type of user interface being used can be input, for example, by type, model, manufacturer, etc., but the user may enter incorrect or incomplete information.

[0006] The present disclosure solves these and other problems by classifying and / or characterizing the user interface based on analyzed acoustic data generated from acoustic reflections indicative of one or more characteristics of the user interface. Summary of the Invention Means for Solving the Problems

[0007] According to some implementations of the present disclosure, the method includes generating acoustic data related to an acoustic reflection of an acoustic signal. The acoustic reflection at least partially indicates one or more features of a user interface coupled to a respiratory therapy device via a conduit. The method further includes analyzing the generated acoustic data. The analysis includes windowing the generated acoustic data based at least in part on at least one of the one or more features of the user interface. The method further includes characterizing the user interface based at least in part on the analyzed acoustic data.

[0008] According to some aspects of this method, windowing the generated acoustic data includes determining a reference point within the generated acoustic data. According to some aspects of this method, the reference point is the minimum point within a predetermined section of the deconvolution of the generated acoustic data. According to some aspects of this method, the reference point is the maximum point within a predetermined section of the deconvolution of the generated acoustic data. According to some aspects of this method, the reference point corresponds to the position of one or more features along a path at least partially formed by the conduit and the user interface. According to some aspects of this method, one or more features cause a change in acoustic impedance. According to some aspects of this method, the change in acoustic impedance is at least partially based on a narrowing of the path at (i) the user interface, (ii) the junction of the conduit and the user interface, or (iii) a combination thereof. According to some aspects of this method, the change in acoustic impedance is at least partially based on an expansion of the path at (i) the user interface, (ii) the junction of the conduit and the user interface, or (iii) a combination thereof. According to some aspects of this method, windowing includes a first windowing of the generated acoustic data and a second windowing of the generated acoustic data. According to some aspects of this method, the first windowing and the second windowing are selected based on an amount of the generated acoustic data before the reference point within the generated acoustic data, after the reference point within the generated acoustic data, or a combination thereof. The amount of generated data can be a predetermined number of data points / samples, a predetermined distance, a predetermined cepstrum, etc. According to some aspects of this method, analyzing the generated acoustic data includes calculating the deconvolution of the generated acoustic data prior to windowing the generated acoustic data. According to some aspects of this method, calculating the deconvolution of the generated acoustic data includes calculating the cepstrum of the generated acoustic data.According to some aspects of this method, the cepstrum identifies distances associated with acoustic reflections, and these distances indicate the positions of one or more features of the user interface relative to the acoustic sensor along the acoustic path that the acoustic signal travels. The cepstrum can identify one or more distances associated with each of the corresponding one or more acoustic reflections, thereby indicating the position of each of the one or more physical features along the acoustic path of the acoustic signal. According to some aspects of this method, the analysis of the generated acoustic data includes calculating the derivative of the cepstrum to determine the rate of change of the cepstrum signal. According to some aspects of this method, analyzing the generated acoustic data includes normalizing the generated acoustic data. According to some aspects of this method, normalizing the generated acoustic data includes subtracting the mean value from the generated acoustic data, dividing by the standard deviation of the generated acoustic data, or a combination thereof. According to some aspects of this method, normalizing the generated acoustic data accounts for interference conditions. According to some aspects of this method, the interference conditions are due to microphone gain, respiratory amplitude, treatment pressure, or a combination thereof. According to some aspects of this method, characterizing the user interface includes inputting the analyzed acoustic data into a machine learning model. In one or more implementations, the machine learning model can be supervised or unsupervised. In one or more implementations, the machine learning model can be a neural network. In one or more implementations, the neural network can be a deep neural network or a shallow neural network. In one or more implementations, the deep neural network can be a convolutional neural network. Machine learning models such as deep neural networks and convolutional neural networks can determine the form factor of the user interface, the model of the user interface, the size of one or more elements of the user interface, or a combination thereof. According to some aspects of this method, the deep neural network includes one or more convolutional layers and one or more max pooling layers. According to some aspects of this method, the convolutional neural network includes N features with max pooling of M samples of N features, and the ratio of N to M is from 1:1 to 4:1. According to some aspects of this method, this method further includes emitting an acoustic signal into a conduit connected to the user interface via an acoustic transducer. According to some aspects of this method, this method further includes emitting an acoustic signal into the conduit via a motor of a respiratory therapy device connected to the conduit. According to some aspects of this method, the acoustic signal is a sound audible to humans. According to some aspects of this method, the acoustic single is a sound inaudible to humans. According to some aspects of this method, whether the sound is audible or not is determined based on the frequency of the sound, the amplitude of the sound, or a combination thereof. According to some aspects of this method, the acoustic signal is an ultrasonic sound. According to some aspects of this method, the user interface is not connected to the user during the generation of the acoustic data. In this configuration, specific interfering factors such as the user's breathing and unwanted noise generated from the respiratory therapy device can be avoided. According to some aspects of this method, the user interface is connected to the user during the generation of the acoustic data. According to some aspects of this method, this method further includes providing a flow of pressurized air entering the user interface through the conduit during the generation of the acoustic data. According to some aspects of this method, this method further includes emitting an acoustic signal into the conduit while there is no flow of pressurized air entering the user interface through the conduit. According to some aspects of this method, the generated acoustic data is generated from a plurality of acoustic reflections from a plurality of acoustic signals. According to some aspects of this method, the generated acoustic data is the average of a plurality of acoustic reflections from a plurality of acoustic signals.This averaging can improve the signal-to-noise ratio of the generated acoustic data by suppressing unwanted time-varying components or artifacts in the acoustic data.

[0009] According to some implementations, a system is disclosed that includes a control system having one or more processors and a memory storing machine-readable instructions. The control system is coupled to the memory, and when the machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system, any of the above aspects can be realized.

[0010] According to some implementations, a system for characterizing a user interface is disclosed, the system including a control system having one or more processors configured to implement any one of the above methods.

[0011] According to some implementations, a computer program product is disclosed that includes instructions that, when executed by a computer, cause the computer to execute any one of the above methods. According to some aspects, the computer program product is a non-transitory computer-readable medium.

[0012] According to some implementations of the present disclosure, a method includes generating acoustic data related to an acoustic reflection of an acoustic signal. The acoustic reflection at least partially represents one or more characteristics of a user interface coupled to a respiratory therapy device via a conduit. The method further includes analyzing the generated acoustic data to identify one or more signatures correlated with the one or more characteristics of the user interface. Generally, a signature can be composed of one or more acoustic feature quantities in the acoustic data. The method further includes classifying the user interface based at least in part on the one or more signatures.

[0013] According to some aspects of this method, the category of the user interface is associated with a direct connection between the conduit and the cushion and / or frame of the user interface. According to some aspects of this method, one or more features and one or more signatures indicate a direct connection between the conduit and the cushion and / or frame. According to some aspects of this method, the category of the user interface is associated with an indirect connection between the conduit and the cushion and / or frame of the user interface. According to some aspects of this method, one or more features and one or more signatures indicate an indirect connection between the conduit and the cushion and / or frame. According to some aspects of this method, the indirect connection is characterized by a further conduit arranged and configured to provide a fluid connection between the conduit and the cushion and / or frame. According to some aspects of this method, this further conduit is a headgear conduit, and this headgear conduit forms part of a headgear for holding the user interface against the user's face. The headgear conduit is configured to send pressurized air from the conduit to the cushion and / or frame. According to some aspects of this method, this further conduit is a user interface conduit that is (i) more flexible than the conduit, (ii) has a diameter smaller than the diameter of the conduit, or has both (i) and (ii). The user interface conduit may have a length shorter than the conduit. The user interface conduit is configured to send pressurized air from the conduit to the cushion and / or frame. According to some aspects of this method, analyzing the generated acoustic data includes calculating the spectrum of the generated acoustic data. According to some aspects of this method, analyzing the generated acoustic data includes calculating the logarithm of the spectrum. Alternatively, a non-linear operation can be used instead of the logarithm calculation. According to some aspects of this method, analyzing the generated acoustic data includes calculating the cepstrum of the logarithmic spectrum.According to some aspects of this method, analyzing the generated acoustic data includes selecting segments of the logarithmic (log) spectrum of the generated acoustic data, calculating the Fourier transform of the segments of the log spectrum, and associating one or more features of the user interface with one or more signatures within the Fourier transform of the segments. According to some aspects of this method, one or more signatures include the maximum amplitude of the cepstrum, the minimum amplitude of the cepstrum, the standard deviation of the cepstrum, the skewness of the cepstrum, the kurtosis of the cepstrum, the median of the absolute value of the cepstrum, the sum of the absolute values of the cepstrum, the sum of the positive areas of the cepstrum, the sum of the negative areas of the cepstrum, the fundamental frequency, the energy corresponding to the fundamental frequency, the average energy, at least one resonance frequency of a combination of the conduit and the user interface, a change in at least one resonance frequency, the number of peaks within a certain range, peak prominences, the distance between peaks, or a combination or variation thereof. According to some aspects of this method, the generated acoustic data includes a primary reflection within the acoustic reflection of the acoustic signal. According to some aspects of this method, the generated acoustic data includes a secondary reflection within the acoustic reflection of the acoustic signal. According to some aspects of this method, the generated acoustic data includes a tertiary reflection within the acoustic reflection of the acoustic signal. According to some aspects of this method, the generated acoustic data includes a primary reflection and a secondary reflection within the acoustic reflection of the acoustic signal. According to some aspects of this method, the user interface category related to the direct connection between the conduit and the cushion and / or the frame is classified based on one or more signatures having a maximum amplitude of the cepstrum greater than a threshold value. According to some aspects of this method, the user interface category related to the indirect connection between the conduit and the cushion and / or the frame via the user interface conduit is classified based on one or more signatures satisfying a threshold number of peaks. According to some aspects of this method, the user interface category related to the indirect connection between the conduit and the cushion and / or the frame via the user interface conduit is classified based on one or more signatures having a maximum amplitude of the cepstrum less than a threshold value.According to some aspects of this method, the user interface category related to the indirect connection between the conduit via the headgear conduit and the cushion and / or the frame is classified based on one or more signatures having an average cepstrum value below a threshold. According to some aspects of this method, one or more signatures. Classifying the user interface based at least in part on includes comparing one or more signatures with one or more known signatures of one or more user interfaces of a known category.

[0014] According to some implementations, a system is disclosed that includes a memory storing machine-readable instructions and a control system including one or more processors configured to execute the machine-readable instructions to perform any one or more of the above methods and / or method implementations.

[0015] The above summary is not intended to represent each embodiment or every aspect of the present disclosure. Rather, the foregoing summary provides only some examples of the novel aspects and features described herein. The above features and advantages of the present disclosure, as well as other features and advantages, will become readily apparent from the following detailed description of representative embodiments and modes for carrying out the invention in connection with the accompanying drawings and the appended claims. Additional aspects of the present disclosure will be apparent to those skilled in the art in view of the detailed description of the various embodiments with reference to the drawings presented below for a brief description.

Brief Description of the Drawings

[0016]

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[0017] The present disclosure, along with its advantages and the accompanying drawings, will be better understood from the following description of exemplary embodiments with reference to the accompanying drawings. These drawings show only exemplary embodiments and thus should not be considered as limiting various embodiments or claims.

[0018] The present invention is capable of various changes and other forms, and specific implementations of the present invention are illustrated in the drawings and will be described in more detail herein. However, it should be understood that the present invention is not intended to be limited only to the specific forms disclosed. Rather, the present invention covers all modifications, equivalents, and alternatives that fall within the spirit and scope of the present invention as defined by the appended claims.

[0019] Various embodiments are described with reference to the accompanying drawings, in which like or equivalent components are given the same reference numerals in each figure. Each figure is not drawn to scale and is provided merely for the purpose of illustrating the present invention. Some aspects of the present invention are described below with reference to exemplary applications for purposes of explanation. It should be understood that numerous specific details, relationships, and methods are set forth in order to provide a thorough understanding of the present invention. However, one of ordinary skill in the art will readily recognize that the present invention may be practiced without one or more of these specific details, or using other methods. In other instances, well-known structures or operations are not shown in detail to avoid obscuring the present invention. Since some acts may occur in different orders and / or concurrently with other acts or events, various embodiments are not limited by the order of acts or events described. Further, not all acts or events described are required to practice the methodology according to the present invention. For example, among the elements and limitations described in the abstract, summary of the invention, and detailed description of the invention, those not expressly recited in the claims should not be incorporated into the claims singly or collectively, by implication, inference, or otherwise. For the purposes of the detailed description here, unless otherwise specifically disclaimed, the singular form includes the plural and vice versa. The term "comprising" means "including but not limited to". Further, approximating words such as "about", "substantially", "approximately", "generally", etc. can be used herein to mean, for example, "within", "near", "in the vicinity of", "3 - 5%", "within manufacturing tolerances", or logical combinations thereof.

[0020]

[0021] ​Many people suffer from sleep-related and / or breathing-related disorders such as periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), central sleep apnea (CSA), and other types of apnea such as mixed apnea and hypopnea, respiratory effort-related arousals (RERA), Cheyne-Stokes respiration (CSR), respiratory insufficiency, obesity hypoventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), rapid eye movement (REM) behavior disorder (also called RBD), dream enactment behavior (DEB), hypertension, diabetes, stroke, insomnia, and chest wall disorders.

[0022] Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing (SDB) characterized by events such as the obstruction or blockage of the upper airway during sleep due to a combination of an abnormally small upper airway and a normal loss of muscle tone in the regions of the tongue, soft palate, and posterior pharyngeal wall.

[0023] Central sleep apnea (CSA) is another form of SDB that occurs when the brain temporarily stops sending signals to the muscles that control breathing. More generally, apnea generally refers to a cessation of breathing or a stoppage of respiratory function caused by an obstruction of air. Typically, an individual stops breathing for between about 15 seconds and about 30 seconds during an obstructive sleep apnea event. Mixed sleep apnea is another form of SDB that is a combination of OSA and CSA.

[0024] Other types of apnea include hypopnea, hyperpnea, and hypercapnia. Hypopnea is generally characterized by slow or shallow breathing caused by a narrowed airway rather than an obstructed airway. Hyperpnea is generally characterized by an increase in the depth and / or rate of breathing. Hypercapnia is generally characterized by a rapid increase or excess of the amount of carbon dioxide in the bloodstream and is usually caused by inadequate breathing.

[0025] Respiratory effort-related arousals (RERAs) are typically characterized by an increase in respiratory effort for 10 seconds or more leading to arousal from sleep and do not meet the criteria for apnea or hypopnea events. In 1999, the AASM Task Force defined RERA as "a series of breaths characterized by an increase in respiratory effort leading to arousal from sleep but not meeting the criteria for apnea or hypopnea." These events must meet both criteria: 1) a pattern of gradually increasing negative esophageal pressure that ends with a sharp change in negative pressure level and arousal, and 2) the event lasting for 10 seconds or more. In 2000, the study "Non-Invasive Detection of Respiratory Effort-Rel ated Arousals (RERAs) by a Nasal Cannula / Pressure Transducer System" conducted at the School of Medicine, New York University and published in Sleep, vol. 23, No. 6, pp. 763-771 demonstrated that a nasal cannula / pressure transducer system is appropriate and reliable in detecting RERAs. A RERA detector can be based on the actual flow signal derived from a respiratory therapy (e.g., PAP) device. For example, a measure of flow limitation can be determined based on the flow signal. Subsequently, a measure of arousal can be derived as a function of the measure of flow limitation and a measure of a sharp increase in ventilation volume. Such methods are described in International Publication No. WO 2008 / 138040, U.S. Patent No. 9,358,353, U.S. Patent No. 10,549,053, and U.S. Patent Application Publication No. 2020 / 0197640, each of which is incorporated herein by reference in its entirety.

[0026] Cheyne-Stokes respiration (CSR) is another form of SDB. CSR is a disorder of the patient's respiratory controller, and is characterized by alternating periodic increases and decreases in ventilation, known as the CSR cycle. CSR is characterized by repeated arterial blood deoxygenation and reoxygenation.

[0027] Obesity hypoventilation syndrome (OHS) is defined as a combination of severe obesity and chronic hypercapnia during wakefulness in a state where the cause of hypoventilation is not otherwise clearly identified. Symptoms include dyspnea, headache upon waking, and excessive daytime sleepiness.

[0028] Chronic obstructive pulmonary disease (COPD) encompasses any of a group of lower airway diseases that share certain characteristics, such as an increased resistance to the movement of air, an extended expiratory phase of breathing, and a loss of normal elasticity of the lungs.

[0029] Neuromuscular diseases (NMD) encompass a number of disorders and illnesses that impair muscle function either directly through intrinsic muscle pathology or indirectly through neuropathy. Thoracic wall disorders are a group of chest wall deformities that result in an inefficient coupling between the respiratory muscles and the thorax.

[0030] These and other disorders are characterized by specific events that occur while an individual is sleeping (e.g., snoring, apnea, hypopnea, restless legs, sleep disturbances, choking, increased heart rate, labored breathing, asthma attacks, epileptic seizures, attacks, or any combination thereof).

[0031] The apnea-hypopnea index (AHI) is an index used to indicate the severity of sleep apnea during a sleep session. The AHI is determined by dividing the number of apnea and / or hypopnea events experienced by the user during the sleep session by the total number of sleep hours in that sleep session. This event can be, for example, a pause in breathing that lasts for at least 10 seconds or more. An AHI of less than 5 is considered normal. An AHI of 5 or more but less than 15 is considered to indicate mild sleep apnea. An AHI of 15 or more but less than 30 is considered to indicate moderate sleep apnea. An AHI of 30 or more is considered to indicate severe sleep apnea. In children, an AHI greater than 1 is considered abnormal. Sleep apnea can be considered "controlled" when the AHI is normal or when the AHI is normal or mild. The AHI can also be used in combination with oxygen desaturation to indicate the severity of obstructive sleep apnea.

[0032] Generally, in the present disclosure, a system and method are described for analyzing a user interface connected to a respiratory therapy device via a conduit and classifying and / or characterizing the user interface by utilizing acoustic reflections. By classifying the user interface, the category of the user interface is directly or indirectly determined. As will be described in more detail below, an indirect user interface may be a user interface of an indirect conduit or an indirect frame. By characterizing the user interface, a particular type of user interface, such as the manufacturer and specific model of the user interface, is determined. In one or more implementations, the user interface can be classified according to the disclosed method without being characterized. Alternatively, in one or more implementations, the user interface can be characterized according to the disclosed method without directly classifying the user interface. In that case, the user interface is indirectly classified through a characterization that implicitly identifies a category. Alternatively, in one or more implementations, the user interface can be (directly) classified and characterized according to the disclosed method. In one or more implementations, classifying the user interface can, for example, verify the characterization of the user interface or create a subset of the user interfaces that characterize the user interface.

[0033] Referring to FIG. 1, a system 100 according to some implementations of the present disclosure is shown. Using the system 100, among other users, the user interface used by the user can be identified. The system 100 includes a control system 110, a storage device 114, an electronic interface 119, one or more sensors 130, and, optionally, one or more user devices 170. In some implementations, the system 100 further includes a respiratory therapy system 120 including a respiratory therapy device 122. As will be disclosed in more detail herein, the user interface 124 can be detected and / or identified using the system 100.

[0034] The control system 110 includes one or more processors 112 (hereinafter, the processor 112). The control system 110 is generally used to control (e.g., operate) various components of the system 100 and / or analyze data acquired and / or generated by the components of the system 100. The processor 112 may be a general-purpose or special-purpose processor or microprocessor. Although one processor 112 is shown in FIG. 1, the control system 110 may include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that may be present within a single housing or located remotely from each other. One or more steps of any of the methods described herein and / or claimed may be performed using a part of the control system 110, such as the control system 110 (or any other control system) or the processor 112 (or any other processor or part of any other control system). The control system 110 can be coupled to and / or positioned within the housing of the user device 170 and / or one or more of the housings of the sensors 130, for example. The control system 110 can be centralized (within one such housing) or distributed (within two or more such physically distinct housings). In such an implementation including two or more housings containing the control system 110, such housings may be located proximate to and / or remotely from each other.

[0035] The memory device 114 stores machine-readable instructions executable by the processor 112 of the control system 110. The memory device 114 can be any suitable computer-readable storage device or medium, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, and the like. Although one memory device 114 is shown in FIG. 1, the system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 114 can be coupled to and / or located within the housing of the respiratory therapy device 122 of the respiratory therapy system 120, the housing of the user device 170, one or more of the sensors 130, or any combination thereof. Similar to the control system 110, the memory device 114 can be centralized (within one such housing) or distributed (within two or more such physically distinct housings).

[0036] In some implementations, the memory device 114 stores a user profile associated with a user. The user profile may include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more previous sleep sessions), or any combination thereof. Demographic information may include, for example, information indicating the user's age, gender, race, family history (e.g., a family history of insomnia or sleep apnea), the user's employment status, the user's education level, the user's socioeconomic status, or any combination thereof. Medical information may include, for example, information indicating one or more medical conditions associated with the user, the user's medication usage, or both. Medical information data may further include a fall risk assessment associated with the user (e.g., a fall risk score according to the Morse Fall Scale), the results of a Multiple Sleep Latency Test (MSLT), a score and / or a value of the Pittsburgh Sleep Quality Index (PSQI). Self-reported user feedback may include information indicating a self-reported subjective sleep score (poor, average, good, etc.), a self-reported subjective stress level of the user, a self-reported subjective fatigue level of the user, a self-reported subjective health status of the user, recent life events experienced by the user, or any combination thereof.

[0037] The electronic interface 119 is configured to receive data (e.g., physiological data and / or acoustic data) from one or more sensors 130 so that the data can be stored in the storage device 114 and / or analyzed by the processor 112 of the control system 110. The electronic interface 119 can communicate with one or more sensors 130 using a wired connection or a wireless connection (e.g., RF communication protocol, WiFi communication protocol, Bluetooth (registered trademark) communication protocol, IR communication protocol, cellular network, other optical communication protocols, etc.). The electronic interface 119 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 may also include another processor and / or another storage device that is the same as or similar to the processor 112 and the storage device 114 described herein. In some implementations, the electronic interface 119 is coupled or integrated with the user device 170. In other implementations, the electronic interface 119 is coupled to or integrated with (e.g., within a housing) the control system 110 and / or the storage device 114.

[0038] As described above, in some implementations, system 100 includes, as needed, a respiratory therapy system 120 (also referred to as a respiratory pressure therapy system). The respiratory therapy system 120 may include a respiratory therapy device 122 (also referred to as a respiratory pressure device), a user interface 124 (also referred to as a mask, patient interface), a conduit 126 (also referred to as a tube or air circuit), a display device 128, a humidification tank 129, or a combination thereof. In some implementations, one or more of the control system 110, the memory device 114, the display device 128, the sensor 130, and the humidification tank 129 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to applying an air supply at a controlled target pressure that is nominally positive relative to the atmosphere throughout the user's respiratory cycle (e.g., different from negative pressure therapies such as a tank ventilator or cuirass). The respiratory therapy system 120 is generally used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, mixed sleep apnea), other respiratory disorders such as COPD, and other disorders leading to respiratory insufficiency, which can occur during both sleep and wakefulness.

[0039] The respiratory therapy device 122 generally includes, for example, one or more motors driving one or more compressors It is used to generate pressurized air sent to the user (using the compressor). In some implementations, the respiratory therapy device 122 generates a continuous and constant air pressure sent to the user. In other implementations, the respiratory therapy device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In still other implementations, the respiratory therapy device 122 is configured to generate various different air pressures within a predetermined range. For example, the respiratory therapy device 122 can deliver at least about 6 cmH2O, at least about 10 cmH2O, at least about 20 cmH2O, from about 6 cmH2O to about 10 cmH2O, from about 7 cmH2O to about 12 cmH2O. The respiratory therapy device 122 can also deliver pressurized air at a predetermined flow rate between, for example, about 20 L / min and about 150 L / min while maintaining a positive pressure (with respect to the ambient pressure). In some implementations, the control system 110, the storage device 114, the electronic interface 119, or any combination thereof may be connected and / or disposed within the housing of the respiratory therapy device 122.

[0040] The user interface 124 engages with a part of the user's face, delivers pressurized air from the respiratory therapy device 122 to the user's airway, and assists in preventing the airway from narrowing and / or closing during sleep. Thereby, the oxygen uptake of the user during sleep can also be increased. Depending on the applied therapy, the user interface 124 can, for example, form a seal with an area or part of the user's face to facilitate the supply of gas at a pressure significantly different from the ambient pressure, such as a positive pressure of about 10 cmH2O with respect to the ambient pressure, to make the treatment effective. In other treatment modalities such as delivering oxygen, the user interface may not include a sufficient seal to facilitate gas supply to the airway at a positive pressure of about 10 cmH2O.

[0041] In some implementations, user interface 124 is a face mask that covers the user's nose and mouth (see, e.g., FIG. 2). Alternatively, user interface 124 is or includes a nasal mask that provides air to the user's nose, or a nasal pillow mask that delivers air directly to the user's nostrils. User interface 124 can include a strap assembly having a plurality of straps (e.g., including hook-and-loop fasteners) for positioning and / or stabilizing user interface 124 at a desired location (e.g., the face) of the user, and an isosceles cushion (e.g., silicone, plastic, foam, etc.) that helps provide an airtight seal between user interface 124 and the user. In some implementations, user interface 124 may include a connector 127 and one or more vents 125. The one or more vents 125 may be used to allow carbon dioxide and other gases exhaled by the user to escape. In other implementations, user interface 124 includes a mouthpiece (e.g., a night guard mouthpiece shaped to fit the user's teeth, a mandibular repositioning device, etc.). In some implementations, connector 127 is separate from, but connectable to, user interface 124 (and / or conduit 126). Connector 127 is configured to connect user interface 124 to conduit 126 to fluidly couple them.

[0042] Conduit 126 allows air to flow between two components of respiratory therapy system 120, such as respiratory therapy device 122 and user interface 124. In some implementations, the conduit may have separate branches for inhalation and exhalation. In other implementations, a single-branch circuit conduit is used for both inhalation and exhalation. Generally, respiratory therapy system 120 forms an air path that extends between the motor of respiratory therapy device 122 and the user and / or the user's airway. Thus, the air path generally includes at least the motor of respiratory therapy device 122, user interface 124, and conduit 126.

[0043] One or more of the respiratory therapy device 122, user interface 124, conduit 126, display device 128, and humidification tank 129 may include one or more sensors (e.g., a pressure sensor, a flow sensor, or more generally, any of the other sensors 130 described herein). These one or more sensors can be used, for example, to measure the air pressure and / or flow rate of the pressurized air supplied by the respiratory therapy device 122.

[0044] The display device 128 is generally used to display images (single or plural), including still images, moving images, or both, and / or information regarding the respiratory therapy device 122. For example, the display device 128 can provide information regarding the state of the respiratory therapy device 122 (e.g., whether the respiratory therapy device 122 is on / off, the pressure of the air being sent by the respiratory therapy device 122, the temperature of the air being sent by the respiratory therapy device 122, etc.) and / or other information (e.g., a sleep score or a therapy score (such as myAir scores as described in the entire International Publication No. WO 2016 / 061629 and U.S. Patent Application Publication No. 2017 / 0311879, which are hereby incorporated by reference in their entireties), the current date / time, the user's personal information, a questionnaire to the user, etc.). In some implementations, the display device 128 functions as a human-machine interface (HMI) that includes a graphical user interface (GUI) configured to display images as an input interface. The display device 128 may be an LED display, an OLED display, an LCD display, or the like. The input interface may be, for example, a touch screen or a touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the respiratory therapy device 122. TM

[0045] ​The humidification tank 129 is coupled or integrated with the respiratory therapy device 122 and includes a reservoir for water that can be used to humidify pressurized air sent from the respiratory therapy device 122. The respiratory therapy device 122 may include a heater that heats the water in the humidification tank 129 to humidify the pressurized air provided to the user. Further, in some implementations, the conduit 126 may also include a heating element (e.g., coupled to and / or embedded in the conduit 126) that heats the pressurized air sent to the user. The humidification tank 129 is fluidly coupled to the water vapor inlet of the air path and can send water vapor into the air path through the water vapor inlet or can be formed in line with the air path as part of the air path itself. In other implementations, the respiratory therapy device 122 or the conduit 126 may include a dry air humidifier. The dry air humidifier can incorporate sensors that interface with other sensors located elsewhere in the system 100.

[0046] The respiratory therapy system 120 can be used, for example, as a ventilator, or as a positive airway pressure (PAP) system such as a continuous positive airway pressure (CPAP) system, an auto positive airway pressure (APAP) system, a bilevel or variable positive airway pressure (BPAP or VPAP) system, or any combination thereof. The CPAP system sends a predetermined air pressure (e.g., determined by a sleep physician) to the user. The APAP system automatically varies the air pressure sent to the user, for example, based at least in part on respiratory data associated with the user. The BPAP or VPAP system is configured to send a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure lower than the first predetermined pressure (e.g., expiratory positive airway pressure or EPAP).

[0047] Referring to FIG. 2, a portion of the system 100 (FIG. 1) according to some implementations is shown. The user 210 and the co-sleeper 220 of the respiratory therapy system 120 are located on the bed 230 and are lying on the mattress 232. The user interface 124 (e.g., a full-face mask) can be worn by the user 210 during a sleep session. The user interface 124 is fluidly coupled and / or connected to the respiratory therapy device 122 via the conduit 126. Next, the respiratory therapy device 122 delivers pressurized air to the user 210 via the conduit 126 and the user interface 124, raising the air pressure in the user 210's throat to help prevent airway closure and / or narrowing during sleep. The respiratory therapy device 122 may include a display device 128 that enables the user to interact with the respiratory therapy device 122 The respiratory therapy device 122 may further include a humidification tank 129 that stores water used to humidify the pressurized air. The respiratory therapy device 122 can be placed on the nightstand 240 that is directly adjacent to the bed 230 as shown in FIG. 2, or more generally, on any surface or structure that is substantially adjacent to the bed 230 and / or the user 210. The user can also wear the blood pressure device 180 and the activity tracker 190 while lying on the mattress 232 of the bed 230.

[0048] Referring again to FIG. 1, one or more sensors 130 of system 100 include a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared (IR) sensor 152, a photoplethysmogram (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, a specimen sensor 174, a moisture sensor 176, a light detection and ranging (LiDAR) sensor 178, or any combination thereof. Generally, each of the one or more sensors 130 is configured to output sensor data received and stored by the memory device 114 or one or more other memory devices. The sensors 130 may further include an electrooculogram (EOG) sensor, a peripheral oxygen saturation (SpO2) sensor, a galvanic skin response (GSR) sensor, a carbon dioxide (CO2) sensor, or any combination thereof.

[0049] One or more sensors 130 are shown and described as including each of a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, an RF receiver 146, an RF transmitter 148, a camera 150, an IR sensor 152, a PPG sensor 154, an ECG sensor 156, an EEG sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an EMG sensor 166, an oxygen sensor 168, a specimen sensor 174, a moisture sensor 176, and a LiDAR sensor 178, but more generally, one or more sensors 130 may include any combination and any number of each of the sensors described and / or shown herein.

[0050] One or more sensors 130 can be used to generate physiological data, acoustic data, or both, related to, for example, a user of the respiratory therapy system 120 (such as user 210 in FIG. 2), the respiratory therapy system, both the user and the respiratory therapy system 120, or other entities, objects, activities, etc. The physiological data generated by the one or more sensors 130 can be used by the control system 110 to determine a sleep-wake signal related to the user during a sleep session and one or more sleep-related parameters. The sleep-wake signal can indicate one or more sleep stages (also referred to as sleep states), including distinct sleep stages such as sleep, wakefulness, relaxed wakefulness, micro-awakenings, or rapid eye movement (REM) stages (which can include both typical and atypical REM stages), the first non-REM stage (often referred to as "N1"), the second non-REM stage (often referred to as "N2"), the third non-REM stage (often referred to as "N3"), or any combination thereof. Methods for determining sleep stages based on physiological data generated by one or more sensors such as sensor 130 are described, for example, in International Publication No. WO 2014 / 047310, U.S. Pat. No. 10,492,720, U.S. Pat. No. 10,660,563, U.S. Patent Application Publication No. 2020 / 0337634, International Publication No. WO 2017 / 132726, International Publication No. WO 2019 / 122413, U.S. Patent Application Publication No. 2021 / 0150873, International Publication No. WO 2019 / 122414, U.S. Patent Application Publication No. 2020 / 0383580, each of which is incorporated herein by reference in its entirety.

[0051] The sleep-wake signal can be timestamped to indicate the time when the user goes to bed and the time when the user gets out of bed It can also indicate the time when the user tried to fall asleep, the time when the user woke up, etc. The sleep-wake signal can be measured by one or more sensors 130 during a sleep session at a predetermined sampling rate, such as 1 sample / second, 1 sample / 30 seconds, 1 sample / minute, etc. Examples of one or more sleep-related parameters that can be determined for the user during a sleep session based at least in part on the sleep-wake signal include total bedtime, total sleep time, total wake-up time, sleep latency, middle-of-the-night awakening parameter, sleep efficiency, fragmentation index, amount of sleep time, consistency of respiratory rate, sleep time, wake-up time, percentage of sleep disorders, number of movements, or any combination thereof.

[0052] The physiological data and / or acoustic data generated by one or more sensors 130 can also be used to determine a respiratory signal related to the user during a sleep session. The respiratory signal generally indicates the respiration of the user during the sleep session. The respiratory signal can indicate, for example, respiratory rate, respiratory rate variation, inspiration amplitude, expiration amplitude, inspiration / expiration amplitude ratio, inspiration / expiration duration ratio, number of events per hour, pattern of events, pressure setting of the respiratory therapy device 122, or any combination thereof. Events can include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, RERA, airflow limitation (e.g., an event resulting in no increase in airflow despite an increase in negative intrathoracic pressure indicating increased effort), mask leak (e.g., leak from the user interface 124), restless legs, sleep disorders, choking, increased heart rate, heart rate variation, dyspnea, asthma attack, onset of epilepsy, seizure, fever, cough, sneeze, presence of diseases such as snoring, wheezing, cold, or influenza, increased stress level, etc. Events can be detected by any means known in the art, such as described in U.S. Patent No. 5,245,995, U.S. Patent No. 6,502,572, International Publication No. 2018 / 050913, International Publication No. 2020 / 104465, each of which is hereby incorporated by reference in its entirety.

[0053] The pressure sensor 132 outputs pressure data that can be stored in the storage device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the pressure sensor 132 is a pneumatic sensor (e.g., a barometric pressure sensor) that generates sensor data indicative of the respiration (e.g., inhalation and / or exhalation) of a user of the respiratory therapy system 120 and / or the ambient pressure. In such implementations, the pressure sensor 132 can be coupled or integrated with the respiratory therapy device 122. The pressure sensor 132 can be, for example, a volume sensor, an electromagnetic sensor, an inductive sensor, a resistive sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof. In one example, the pressure sensor 132 can be used to determine the blood pressure of a user.

[0054] The flow sensor 134 outputs flow data that can be stored in the storage device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the flow sensor 134 is used to determine the air flow from the respiratory therapy device 122, the air flow through the conduit 126, the air flow through the user interface 124, or any combination thereof. In such implementations, the flow sensor 134 can be coupled or integrated with the respiratory therapy device 122, the user interface 124, or the conduit 126. The flow sensor 134 can be, for example, a mass flow sensor such as a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot wire sensor, a vortex sensor, a membrane sensor, or any combination thereof.

[0055] The temperature sensor 136 outputs temperature data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the temperature sensor 136 generates temperature data indicating the core body temperature of the user, the skin temperature of the user 210, the temperature of the air flowing from and / or through the conduit 126 from the respiratory therapy device 122, the temperature within the user interface 124, the ambient temperature, or any combination thereof. The temperature sensor 136 can be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap cap temperature sensor or a semiconductor sensor, a resistance temperature detector, or any combination thereof.

[0056] The motion sensor 138 outputs motion data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The motion sensor 138 can be used to detect the movement of the user during a sleep session and / or the movement of any component of the respiratory therapy system 120, such as the respiratory therapy device 122, the user interface 124, or the conduit 126. The motion sensor 138 can include one or more inertial sensors such as an accelerometer, a gyroscope, and a magnetometer. The motion sensor 138 can be used to detect motion or acceleration related to arterial pulses, such as those in or around the user's face and proximal to the user interface 124, and is configured to detect characteristics of the shape, velocity, amplitude, or volume of the pulse. In some implementations, the motion sensor 138 alternatively or additionally generates one or more signals representative of the body movement of the user, and from this signal, a signal representative of the sleep state of the user can be obtained, for example, via the respiratory movement of the user.

[0057] The microphone 140 outputs acoustic data that can be stored in the storage device 114 and / or analyzed by the processor 112 of the control system 110. As described in more detail herein, the acoustic data generated by the microphone 140 can be reproduced as one or more sounds (e.g., sounds from the user) during a sleep session to determine one or more sleep-related parameters (e.g., using the control system 110). As described in more detail herein, the acoustic data from the microphone 140 can also be used to identify events experienced by the user during a sleep session (e.g., using the control system 110). In other implementations, the acoustic data from the microphone 140 represents noise associated with the respiratory therapy system 120. In some implementations, the system 100 includes a plurality of microphones (e.g., an array of two or more microphones and / or microphones using beamforming) such that the audio data generated by each of the plurality of microphones can be used to identify the audio data generated by other microphones among the plurality of microphones. The microphone 140 can be coupled or integrated with the respiratory therapy system 120 (or the system 100) in generally any configuration. For example, the microphone 140 can be disposed inside the respiratory therapy device 122, the user interface 124, the conduit 126, or other components. The microphone 140 can also be disposed adjacent to or coupled to the outside of the respiratory therapy device 122, the outside of the user interface 124, the outside of the conduit 126, or any other component. The microphone 140 can be a component of the user device 170 (e.g., the microphone 140 is a microphone of a smartphone). The microphone 140 can be integrated with the user interface 124, the conduit 126, the respiratory therapy device 122, or any combination thereof. Generally, the microphone 140 can be disposed at any point within or adjacent to the air path of the respiratory therapy system 120, including at least the motor of the respiratory therapy device 122, the user interface 124, and the conduit 126.Therefore, the air path is also called the acoustic path.

[0058] Speaker 142 outputs sound waves audible to the user. In one or more implementations, the sound waves may or may not be audible to the user of system 100 (e.g., ultrasonic). Speaker 142 can be used, for example, as an alarm clock or to play a warning or message to the user (e.g., in response to an event). In some implementations, speaker 142 can be used to transmit acoustic data generated by microphone 140 to the user. Speaker 142 can be coupled or integrated with respiratory therapy device 122, user interface 124, conduit 126, or user device 170.

[0059] The microphone 140 and the speaker 142 can be used as separate devices. In some implementations, the microphone 140 and the speaker 142 can be combined with an acoustic sensor 141 (e.g., a SONAR sensor), as described in International Publication No. WO 2018 / 050913 and International Publication No. WO 2020 / 104465, which are incorporated herein by reference in their entireties. In such implementations, the speaker 142 generates or emits sound waves at a predetermined interval and / or frequency, and the microphone 140 detects the reflection of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency that is inaudible to the human ear (e.g., less than 20 Hz or greater than about 18 kHz) so as not to interfere with the sleep of the user or the user's bed partner (e.g., the bed partner 220 in FIG. 2). The control system 110 can determine the position of the user and / or one or more sleep-related parameters described herein, such as a respiratory signal, respiratory rate, inspiration amplitude, expiration amplitude, inspiration-to-expiration ratio, number of events per hour, event pattern, sleep stage, pressure setting of the respiratory therapy device 122, mouth leak state, or any combination thereof, based at least in part on data from the microphone 140 and / or the speaker 142. In this context, the SONAR sensor can be understood to be involved in active acoustic sensing by generating / transmitting an ultrasonic or low-frequency ultrasonic sensing signal (e.g., within a frequency range of about 17 - 23 kHz, 18 - 22 kHz, or 17 - 18 kHz) through the air. Such a system can be considered in relation to International Publication No. WO 2018 / 050913 and International Publication No. WO 2020 / 104465 described above. In some implementations, the speaker 142 is a bone conduction speaker. In some implementations, the one or more sensors 130 include (i) a first microphone that is the same as or similar to the microphone 140 and is integrated with the acoustic sensor 141, and (ii) a second microphone that is the same as or similar to the microphone 140 but is separate and distinct from the first microphone integrated with the acoustic sensor 141.

[0060] The RF transmitter 148 generates and / or radiates radio waves having a predetermined frequency and / or a predetermined amplitude (e.g., within a high-frequency band, within a low-frequency band, long-wave signal, short-wave signal, etc.). The RF receiver 146 can detect the reflection of the radio waves radiated from the RF transmitter 148 and analyze this data by the control system 110 to determine the user's position and / or one or more sleep-related parameters described herein. The RF receiver (either the RF receiver 146 and the RF transmitter 148 or another RF pair) can also be used for wireless communication between the control system 110, the respiratory therapy device 122, one or more sensors 130, the user device 170, or any combination thereof. The RF receiver 146 and the RF transmitter 148 are shown as separate elements separated in FIG. 1, but in some implementations, the RF receiver 146 and the RF transmitter 148 are combined as part of an RF sensor 147 (e.g., a RADAR sensor). In some such implementations, the RF sensor 147 includes a control circuit. The specific form of RF communication may be Wi-Fi, Bluetooth (registered trademark), etc.

[0061] In some implementations, the RF sensor 147 is part of a mesh system. An example of a mesh system may be a Wi-Fi mesh system that can include mesh nodes, mesh routers (singular or plural), and mesh gateways (singular or plural), each of which may be mobile / movable or stationary. In such an implementation, the Wi-Fi mesh system includes a Wi-Fi router and / or a Wi-Fi controller, each including an RF sensor identical or similar to the RF sensor 147, and one or more satellites (e.g., access points). The Wi-Fi router and the satellites communicate continuously with each other using Wi-Fi signals. The Wi-Fi mesh system generates motion data based at least in part on changes in the Wi-Fi signals (e.g., differences in received signal strength) that occur between the router and the satellite(s) due to the movement of an object or person partially obstructing the signal. It can be used for this purpose. This motion data can indicate motion, breathing, heart rate, walking, falling, behavior, etc., or any combination thereof.

[0062] Camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, moving images, thermal images, or a combination thereof) that can be stored in the storage device 114. The image data from camera 150 can be used by the control system 110 to determine one or more sleep-related parameters described herein. For example, the image data from camera 150 can be used to identify the user's position and determine the time when the user enters the user's bed (such as bed 230 in FIG. 2) and the time when the user exits bed 230. Camera 150 can also be used to track eye movement, pupil dilation (when one or both of the user's eyes are open), blink rate, or any changes during REM sleep. Camera 150 can also be used to track the position of the user that can affect the duration and / or severity of the apnea symptoms of a user having positional obstructive sleep apnea.

[0063] IR sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, moving images, or both) that can be stored in the storage device 114. The infrared data from IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including the user's temperature and / or the user's movement. IR sensor 152 can also be used in combination with camera 150 when measuring the user's presence, position, and / or movement. IR sensor 152 can detect infrared light having a wavelength between, for example, about 700 nm and about 1 mm, whereas camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0064] The IR sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, moving images, or both) that can be stored in the storage device 114. The infrared data from the IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including the user's temperature and / or the user's movement. The IR sensor 152 can also be used in combination with the camera 150 when measuring the user's presence, position, and / or movement. The IR sensor 152 can detect infrared light having a wavelength between, for example, about 700 nm and about 1 mm, whereas the camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0065] The PPG sensor 154 outputs physiological data related to the user that can be used to determine one or more sleep-related parameters, such as, for example, heart rate, heart rate pattern, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory to expiratory ratio, estimated blood pressure parameter(s), or any combination thereof. The PPG sensor 154 can be worn by the user, embedded in the clothing and / or fabric worn by the user, embedded in and / or coupled to the user interface 124 and / or its associated headgear (e.g., straps, etc.).

[0066] The ECG sensor 156 outputs physiological data related to the electrical activity of the user's heart. In some implementations, the ECG sensor 156 includes one or more electrodes disposed on or around a portion of the user during a sleep session. The physiological data from the ECG sensor 156 can be used, for example, to determine one or more sleep-related parameters described herein.

[0067] The EEG sensor 158 outputs physiological data related to the electrical activity of the user's brain. In some implementations, the EEG sensor 158 includes one or more electrodes disposed on or around the user's scalp during a sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine the user's sleep state or sleep stage at any given time during the sleep session. In some implementations, the EEG sensor 158 can be integrated with the user interface 124 and / or its associated headgear (e.g., straps, etc.).

[0068] The capacitance sensor 160, force sensor 162, and strain gauge sensor 164 output data that can be stored in the storage device 114 and used by the control system 110 to determine one or more sleep-related parameters described herein. The EMG sensor 166 outputs physiological data related to the electrical activity generated by one or more muscles. The oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of a gas (e.g., within the conduit 126 or at the user interface 124). The oxygen sensor 168 can be, for example, an ultrasonic oxygen sensor, an electro-chemical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some implementations, the one or more sensors 130 also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, an oxygen measurement sensor, or any combination thereof.

[0069] The analyte sensor 174 can be used to detect the presence of analytes in the user's breath. The data output by the analyte sensor 174 can be stored in the storage device 114 and used by the control system 110 to determine the identity and concentration of any analyte in the user's breath. In some implementations, the analyte sensor 174 is disposed near the user's mouth to detect analytes in the user's breath. For example, when the user interface 124 is a face mask that covers the user's nose and mouth, the analyte sensor 174 can be disposed within the face mask to monitor the user's mouth breathing. In other implementations, when the user interface 124 is a nasal mask or a nasal pillow mask, etc., the analyte sensor 174 can be disposed near the user's nose to detect analytes in the breath from the user's nose. In yet other implementations, when the user interface 124 is a nasal mask or a nasal pillow mask, the analyte sensor 174 can be disposed near the user's mouth. In this implementation, the analyte sensor 174 can be used to detect whether air is inadvertently leaking from the user's mouth. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemical substances or compounds such as carbon dioxide. In some implementations, the analyte sensor 174 can also detect whether the user is breathing through the nose or mouth. For example, when the presence of an analyte is detected based on the data output by the analyte sensor 174 disposed near the user's mouth or (in the implementation where the user interface 124 is a face mask) within the face mask, the control system 110 can use this data as an indication that the user is breathing through their mouth.

[0070] The moisture sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The moisture sensor 176 can be used to detect moisture in various areas surrounding the user (e.g., inside the conduit 126 or the user interface 124, near the user's face, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the respiratory therapy device 122, etc.). Thus, in some implementations, the moisture sensor 176 can be coupled or integrated within the user interface 124 or the conduit 126 to monitor the humidity of the pressurized air from the respiratory therapy device 122. In other implementations, the moisture sensor 176 is placed near any area where it is necessary to monitor the moisture level. The moisture sensor 176 can also be used to monitor the humidity of the surrounding environment around the user, such as the air in the user's bedroom. The moisture sensor 176 can also be used to track the user's biological response to environmental changes.

[0071] One or more LiDAR sensors 178 can be used to sense depth. This type of optical sensor (such as a laser sensor) can be used to detect objects and create a three-dimensional (3D) map of the surrounding environment such as the living space. LiDAR is generally The time of flight is measured using a pulsed laser. LiDAR is also called 3D laser scanning. In one example of the use of such a sensor, a fixed or mobile device (such as a smartphone) having a LiDAR sensor 178 can measure and map an area more than 5 meters away from the sensor. LiDAR data can be fused with, for example, point cloud data estimated by an electromagnetic RADAR sensor. The LiDAR sensor 178 can also automatically create a geofence for the RADAR system by using artificial intelligence (AI) to detect and classify features in a space where problems may occur in the RADAR system, such as a glass window (which may be highly reflective to RADAR). LiDAR can also be used to estimate changes in height that occur when a person is sitting, fallen, etc., in addition to the person's height. LiDAR can be used to form a 3D mesh representation of the environment. In a further application, for solid surfaces through which radio waves pass (for example, radio wave transmissive materials), LiDAR may reflect off such surfaces, enabling the classification of different types of obstacles.

[0072] Although shown separately in FIG. 1, any combination of one or more sensors 130 can be integrated and / or coupled to any one or more of the components of system 100, including respiratory therapy device 122, user interface 124, conduit 126, humidification tank 129, control system 110, user device 170, or any combination thereof. For example, acoustic sensor 141 and / or RF sensor 147 can be integrated and / or coupled to user device 170. In such an implementation, user device 170 can be considered a secondary device that generates additional or secondary data used by system 100 (e.g., control system 110) according to some aspects of the present disclosure. In some implementations, pressure sensor 132 and / or flow sensor 134 are integrated and / or coupled to respiratory therapy device 122. In some implementations, at least one of the one or more sensors 130 is not coupled to respiratory therapy device 122, control system 110, or user device 170, but is disposed generally adjacent to the user during a sleep session (e.g., disposed on or in contact with a part of the user, worn by the user, coupled or disposed on a nightstand, coupled to a mattress, coupled to a ceiling, etc.). More generally, one or more sensors 130 can be disposed at any suitable location relative to the user such that they can generate physiological data related to the user and / or co-sleeper 220 during one or more sleep sessions.

[0073] Data from one or more sensors 130 can be analyzed to determine one or more sleep-related parameters that may include a respiratory signal, respiratory rate, respiratory pattern, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, occurrence of one or more events, number of events per hour, event pattern, average duration of an event, range of event durations, ratio of different numbers of events, sleep stage, apnea-hypopnea index (AHI), or any combination thereof. The one or more events can include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, intentional user interface leak, unintentional user interface leak, mouth leak, cough, restless legs, sleep disorder, choking, increased heart rate, dyspnea, asthma attack, onset of epilepsy, seizure, increased blood pressure, hyperventilation, or any combination thereof. Many of these sleep-related parameters are physiological parameters, but some of the sleep-related parameters can be considered non-physiological parameters. Other types of physiological and non-physiological parameters can also be determined based on either data from one or more sensors 130 or other types of data.

[0074] The user device 170 includes a display device 172. The user device 170 can be, for example, a mobile device such as a smartphone, tablet, laptop, game console, smartwatch, etc. Alternatively, the user device 170 can be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., a smart speaker (singular or plural) such as Google Home, Amazon Echo, Alexa, etc.) This is acceptable. In some implementation forms, the user device 170 is a wearable device (for example, a smartwatch). The display device 172 is generally used to display an image (single or plural) including a still image, a moving image, or both. In some implementation forms, the display device 172 functions as a human-machine interface (HMI) including a graphic user interface (GUI) configured to display an image (single or plural) and an input interface. The display device 172 may be an LED display, an OLED display, an LCD display, or the like. The input interface may be, for example, a touch screen or a touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense an input made by a human user interacting with the user device 170. In some implementation forms, one or more user devices 170 can be used by and / or included in the system 100.

[0075] The blood pressure device 180 is generally used to assist in generating physiological data for determining one or more blood pressure measurement values related to a user. The blood pressure device 180 may include, for example, at least one of the one or more sensors 130 for measuring a systolic blood pressure component and / or a diastolic blood pressure component.

[0076] In some implementations, the blood pressure device 180 is a sphygmomanometer that includes an inflatable cuff that can be worn by the user and a pressure sensor (e.g., the pressure sensor 132 described herein). For example, as shown in the example of FIG. 2, the blood pressure device 180 can be worn on the user's upper arm. In implementations where the blood pressure device 180 is a sphygmomanometer, the blood pressure device 180 also includes a pump (e.g., a manually operated valve) that inflates the cuff. In some implementations, the blood pressure device 180 is coupled to the respiratory therapy device 122 of the respiratory therapy system 120, and the respiratory therapy device 122 supplies pressurized air to inflate the cuff. More generally, the blood pressure device 180 can be communicatively coupled and / or physically integrated (e.g., within a housing) to the control system 110, the storage device 114, the respiratory therapy system 120, the user device 170, and / or the activity meter 190.

[0077] The activity meter 190 is generally used to assist in generating physiological data for determining activity measurements related to the user. Activity measurements can include, for example, the number of steps, distance traveled, number of steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, average respiratory rate, resting respiratory rate, maximum respiratory rate, respiratory rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, calorie consumption, blood oxygen saturation, skin electrical activity (also known as skin conductance or galvanic skin response), or any combination thereof. The activity meter 190 can include one or more sensors 130 described herein, such as a motion sensor 138 (e.g., one or more accelerometers and / or gyroscopes), a PPG sensor 154, and / or an ECG sensor 156.

[0078] In some implementations, the activity meter 190 is a wearable device that can be worn by a user, such as a smartwatch, a wristband, a ring, or a patch. For example, referring to FIG. 2, the activity meter 190 is worn on the user's wrist. The activity meter 190 can also be coupled or integrated with the clothing or garments worn by the user. Alternatively, the activity meter 190 can also be coupled or (e.g., within the same housing) integrated with the user device 170. More generally, the activity meter 190 can be communicatively coupled and / or (e.g., within a housing) physically integrated with the control system 110, the memory device 114, the respiratory therapy system 120, the user device 170, and / or the blood pressure monitor 180.

[0079] The control system 110 and the memory device 114 are described and shown in FIG. 1 as separate and distinct components of the system 100, but in some implementations, the control system 110 and / or the memory device 114 are integrated with the user device 170 and / or the respiratory therapy device 122. Alternatively, in some implementations, the control system 110 or a part thereof (e.g., the processor 112) can be placed in the cloud (e.g., integrated with a server, integrated with an Internet of Things (IoT) device, connected to the cloud, receiving edge cloud processing) and placed in one or more servers (e.g., a remote server, a local server, etc., or any combination thereof).

[0080] System 100 is shown as including all of the above components, but according to embodiments of the present disclosure, a system for identifying a user interface used by a user may include more or fewer components. For example, a first alternative system includes at least one of control system 110, storage device 114, and one or more sensors 130. As another example, a second alternative system includes at least one of control system 110, storage device 114, one or more sensors 130, and user device 170. As yet another example, a third alternative system includes control system 110, storage device 114, respiratory therapy system 120, at least one of one or more sensors 130, and user device 170. As a further example, a fourth alternative system includes control system 110, storage device 114, respiratory therapy system 120, at least one of one or more sensors 130, user device 170, and blood pressure device 180 and / or activity meter 190. Thus, various systems for changing pressure settings can be formed using any part(s) of the components shown and described herein and / or in combination with one or more other components.

[0081] Referring again to FIG. 2, in some embodiments, control system 110, storage device 114, any one or combination of one or more sensors 130 can be disposed on and / or within any surface and / or structure generally adjacent to bed 230 and / or user 210. For example, in some embodiments, at least one of one or more sensors 130 can be disposed at a first position on and / or within one or more components of respiratory therapy system 120 adjacent to bed 230 and / or user 210. One or more sensors 130 can be coupled to respiratory therapy system 120, user interface 124, conduit 126, display device 128, humidification tank 129, or a combination thereof.

[0082] Alternatively or additionally, at least one of the one or more sensors 130 can be disposed at a second position on and / or within the bed 230 (e.g., the one or more sensors 130 are coupled and / or integrated with the bed 230). Further alternatively or additionally, at least one of the one or more sensors 130 can be disposed at a third position on and / or within the mattress 232 adjacent to the bed 230 and / or the user 210 (e.g., the one or more sensors 130 are coupled and / or integrated with the mattress 232). Alternatively or additionally, at least one of the one or more sensors 130 can be disposed at a fourth position on and / or within a pillow generally adjacent to the bed 230 and / or the user 210.

[0083] Alternatively or additionally, at least one of the one or more sensors 130 can be disposed at a fifth position on and / or within the nightstand 240 generally adjacent to the bed 230 and / or the user 210. Alternatively or additionally, at least one of the one or more sensors 130 can be disposed at a sixth position so as to be coupled and / or disposed on the user 210 (e.g., the one or more sensors 130 are embedded or coupled to a fabric, clothing, and / or smart device worn by the user 210). More generally, at least one of the one or more sensors 130 can be disposed at any suitable position relative to the user 210 so as to generate sensor data related to the user 210.

[0084] In some implementations, a primary sensor such as the microphone 140 is configured to generate acoustic data related to the user 210 during a sleep session. For example, one or more microphones (identical or similar to the microphone 140 of FIG. 1) can be integrated and / or coupled to (i) the circuit board of the respiratory treatment device 122, (ii) the conduit 126, (iii) the connectors between components of the respiratory treatment system 120, (iv) the user interface 124, (v) the headgear (e.g., straps) associated with the user interface, or (vi) combinations thereof.

[0085] In some implementations, in addition to the primary sensor, one or more secondary sensors can generate additional data. In some such implementations, the one or more secondary sensors include a microphone (e.g., microphone 140 of system 100), a flow sensor (e.g., flow sensor 134 of system 100), a pressure sensor (e.g., pressure sensor 132 of system 100), a temperature sensor (e.g., temperature sensor 136 of system 100), a camera (e.g., camera 150 of system 100), a vane air flow sensor (VAF), a hot wire air flow sensor (MAF), a cold wire sensor, a laminar flow sensor, an ultrasonic sensor, an inertial sensor, or a combination thereof.

[0086] Alternatively or additionally, one or more microphones (identical or similar to microphone 140 of FIG. 1) can be integrated and / or coupled to a collocated smart device such as a user device 170, a TV, a watch (e.g., a mechanical watch or another smart device worn by the user), a pendant, a mattress 232, a bed 230, bedding disposed on the bed 230, a pillow, a speaker (e.g., speaker 142 of FIG. 1), a radio, a tablet-type device, a dry air humidifier, or a combination thereof. A collocated smart device can be any smart device within range for detecting sound emitted by the user, the respiratory therapy system 120, and / or any part of the system 100. In some implementations, a collocated smart device is a smart device in the same room as the user during a sleep session.

[0087] Alternatively, or in addition, in some implementations, one or more microphones (identical or similar to microphone 140 in FIG. 1) may be remote from system 100 (FIG. 1) and / or user 210 (FIG. 2), provided there is an air passage to convey acoustic signals thereto. For example, one or more microphones may be installed in a room different from the room in which system 100 is housed. In some implementations, physical obstructions in a user's airway can be characterized and / or determined, at least in part, based on analysis of acoustic data.

[0088] Here, a sleep session can be defined in several ways, for example, at least in part based on an initial start time and an end time. In some implementations, a sleep session is the duration during which a user is asleep, i.e., a sleep session has a start time and an end time, and the user does not wake up until the end time during the sleep session. That is, the time when the user is awake is not included in the sleep session. According to this first definition of a sleep session, if a user wakes up and falls asleep several times during the night, each sleep interval separated by an awake interval becomes a sleep session.

[0089] Alternatively, in some implementations, a sleep session has a start time and an end time, and during the sleep session, the user can wake up without the sleep session ending as long as the cumulative duration during which the user is awake is below an awake duration threshold. The awake duration threshold can be defined as a percentage of the sleep session. The awake duration threshold can be, for example, about 20% of the sleep session, about 15% of the sleep session duration, about 10% of the sleep session duration, about 5% of the sleep session duration, about 2% of the sleep session duration, etc., or any other arbitrary threshold percentage. In some implementations, the awake duration threshold is defined as a fixed time, such as about 1 hour, about 30 minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 2 minutes, etc., or any other arbitrary time. duration, etc., or any other arbitrary threshold percentage. In some implementations, the awake duration threshold is defined as a fixed time, such as about 1 hour, about 30 minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 2 minutes, etc., or any other arbitrary time.

[0090] In some implementations, a sleep session is defined as the total time from the time when the user first goes to bed at night until the time when the user finally gets out of bed the next morning. In other words, a sleep session starts at the first time (e.g., 10:00 p.m.) on the first date (e.g., January 6, 2020, Monday), which can be called tonight, when the user first goes to bed with the intention of going to sleep (except when the user intends to first watch TV or enjoy a smartphone before going to bed), and ends at the second time (e.g., 7:00 a.m.) on the second date (e.g., January 7, 2020, Tuesday), which can be called the next morning, when the user first gets out of bed with the intention of not going back to sleep.

[0091] In some implementations, the user can manually set the start of a sleep session or manually set the end of a sleep session. For example, the user can select (e.g., click or tap) one or more user-selectable elements displayed on the display device 172 of the user device 170 (FIG. 1) to manually start or end a sleep session.

[0092] Referring to FIG. 3, an exemplary timeline 300 of a sleep session is shown. The timeline 300 includes a time of going to bed (t bed ), a time of going to sleep (t GTS ), an initial sleep time (t sleep ), a first micro-awakening MA1, a second micro-awakening MA2, an awakening A, a time of waking up (t wake ), and a time of getting up (t rise ).

[0093] The time of going to bed t bed is associated with the time when the user first goes to bed (e.g., the bed 230 in FIG. 2) before falling asleep (e.g., the user lies down or sits on the bed). The time of going to bed t bedCan be identified at least in part based on a bedtime threshold duration to distinguish between the time when a user goes to bed to sleep and the time when the user goes to bed for other reasons (e.g., watching TV). For example, the bedtime threshold duration can be at least about 10 minutes, at least about 20 minutes, at least about 30 minutes, at least about 45 minutes, at least about 1 hour, at least about 2 hours, etc. In this specification, the bedtime time t bed is described in relation to a bed, but more generally, the bedtime time t bed can mean the time when the user first takes a seat at any position (e.g., a sofa, a chair, a sleeping bag, etc.) to sleep.

[0094] The bedtime (GTS) is associated with the time when the user first attempts to fall asleep after going to bed (t bed ). For example, after going to bed, the user may engage in one or more activities (e.g., reading, watching TV, listening to music, using the user device 170, etc.) to relax before attempting to fall asleep. The initial sleep time (t sleep ) is the time when the user first falls asleep. For example, the initial sleep time (t sleep ) can be the time when the user first enters the first non-REM stage.

[0095] The wake-up time t wake is the time associated with the time when the user wakes up without returning to sleep (e.g., a time different from when the user wakes up in the middle of the night and returns to sleep). After first falling asleep, the user may experience one of more unconscious micro-awakenings (e.g., micro-awakenings MA1 and MA2) having a short duration (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.). In contrast to the wake-up time t wake , the user returns to sleep after each micro-awakening MA1 or MA2. Similarly, after first falling asleep, the user may have one or more conscious awakenings (e.g., awakening A1) (e.g., getting up to use the toilet, taking care of a child or pet, sleepwalking, etc.). However, the user returns to sleep after awakening A. Therefore, the wake-up time t wakecan be defined at least in part based on, for example, a wake threshold duration (e.g., 15 minutes or more, 20 minutes or more, 30 minutes or more, 1 hour or more, etc.).

[0096] Similarly, the wake-up time t rise is associated with the time when the user gets out of bed and remains out of bed (e.g., gets up at night to use the toilet, takes care of children or pets, as opposed to sleepwalking, etc.) for the purpose of ending the sleep session. In other words, the wake-up time t rise is the time when the user last left the bed without returning to it until the next sleep session (e.g., the next night). Therefore, the wake-up time t rise can be defined at least in part based on, for example, a wake-up threshold duration (e.g., when 15 minutes or more, 20 minutes or more, 30 minutes or more, 1 hour or more have elapsed since the user left the bed). The second and subsequent bedtimes t bed can also be defined at least in part based on a wake-up threshold duration (e.g., when 4 hours or more, 6 hours or more, 8 hours or more, 12 hours or more have elapsed since the user left the bed).

[0097] As described above, the user may wake up and get out of bed one more time at night between the first t bed and the last t rise . In some implementations, the last wake-up time t wake and / or the last wake-up time t rise is identified or determined at least in part based on a predetermined threshold duration of time following an event (e.g., falling asleep or leaving the bed). Such a threshold duration can be customized for the user. For a standard user who goes to bed at night and wakes up and gets out of bed in the morning, from the time the user wakes up (t wake ) or gets up (t rise ) at any time period until going to bed (t bed ), going to sleep (t GTS ) or falling asleep (t sleepThe period up to is available for use from about 12 hours to about 18 hours. For users with longer bedtimes, a shorter threshold period (e.g., from about 8 hours to about 14 hours) may be used. The threshold period may be initially selected and / or later adjusted based at least in part on a system that monitors the user's sleep behavior.

[0098] Total sleep time (TIB) is the time from bedtime t bed to wake-up time t rise The total duration. Total sleep time (TST) is associated with the duration from the initial sleep time to the waking time, excluding conscious or unconscious awakenings and / or micro-awakenings. Generally, total sleep time (TST) is shorter than total sleep time (TIB) (e.g., 1 minute shorter, 10 minutes shorter, 1 hour shorter, etc.). For example, referring to the timeline 300 in FIG. 3, the total sleep time (TST) is between the initial sleep time t sleep and the waking time t wake However, the durations of the first micro-awakening MA1, the second micro-awakening MA2, and the awakening A are excluded. As shown, in this example, the total sleep time (TST) is shorter than the total sleep time (TIB).

[0099] In some implementations, total sleep time (TST) can be defined as total persistent sleep time (PTST). In such implementations, the total persistent sleep time excludes a predetermined initial portion or initial period of the first non-REM stage (e.g., the light sleep stage). For example, the predetermined initial portion can be from about 30 seconds to about 20 minutes, from about 1 minute to about 10 minutes, from about 3 minutes to about 5 minutes, etc. The total persistent sleep time is an indicator of persistent sleep and smooths the sleep / wake hypnogram. For example, when the user first falls asleep, the user enters the first non-REM stage for a very short time (e.g., about 30 seconds), then returns to the wake stage for a short time (e.g., 1 minute), and then can return to the first non-REM stage. In this example, the total persistent sleep time excludes the first instance of the first non-REM stage (e.g., about 30 seconds).

[0100] In some implementations, a sleep session begins at the time of going to bed (t bed ) and ends at the time of waking up (t rise ), that is, the sleep session is defined as the total in-bed time (TIB). In some implementations, the sleep session begins at the initial sleep time (t sleep ) and ends at the waking time (t wake ), and is defined as such. In some implementations, the sleep session is defined as the total sleep time (TST). In some implementations, the sleep session begins at the bedtime (t GTS ) and ends at the waking time (t wake ), and is defined as such. In some implementations, the sleep session begins at the bedtime (t GTS ) and ends at the waking up time (t rise ), and is defined as such. In some implementations, the sleep session begins at the time of going to bed (t bed ) and ends at the waking time (t wake ), and is defined as such. In some implementations, the sleep session begins at the initial sleep time (t sleep ) and ends at the waking up time (t rise ).

[0101] Referring to FIG. 4, an exemplary hypnogram 350 corresponding to a timeline 300 (FIG. 3) according to some implementations is shown. As shown, the hypnogram 350 includes a sleep-wake signal 351, a wake stage axis 360, a REM stage axis 370, a light sleep stage axis 380, and a deep sleep stage axis 390. The intersection of the sleep-wake signal 351 with one of the axes 360-390 indicates the sleep stage at any given time during the sleep session.

[0102] The sleep-wake signal 351 can be generated based at least in part on physiological data related to the user (e.g., generated by one or more of the sensors 130 described herein). The sleep-wake signal can indicate one or more sleep stages including a wake state, a relaxed wake state, a micro-awakening, a REM stage, a first non-REM stage, a second non-REM stage, a third non-REM stage, or any combination thereof. In some implementations, one or more of the first non-REM stage, the second non-REM stage, and the third non-REM stage can be grouped and classified as a light sleep stage or a deep sleep stage. For example, the light sleep stage can include the first non-REM stage, and the deep sleep stage can include the second non-REM stage and the third non-REM stage. As shown in FIG. 4, the hypnogram 350 includes a light sleep stage axis 380 and a deep sleep stage axis 390, but in some implementations, the hypnogram 350 can include axes for each of the first non-REM stage, the second non-REM stage, and the third non-REM stage. In other implementations, the sleep-wake signal can indicate a respiratory signal, a respiratory rate, an inspiration amplitude, an expiration amplitude, an inspiration / expiration amplitude ratio, an inspiration / expiration duration ratio, the number of events per hour, a pattern of events, or any combination thereof. Information describing the sleep-wake signal can be stored in the memory device 114.

[0103] The hypnogram 350 can be used, for example, to determine one or more sleep-related parameters such as sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), a sleep fragmentation index, a sleep block, or any combination thereof.

[0104] The sleep onset latency (SOL) is from bedtime (t GTS ) to the initial sleep time (t sleep) is defined as the time until. In other words, the sleep latency indicates the time required for the user to actually fall asleep after first attempting to fall asleep. In some implementations, the sleep latency is defined as the Persistent Sleep Onset Latency (PSOL). The Persistent Sleep Onset Latency differs from the sleep latency in that it is defined as the duration from bedtime to a predetermined amount of continuous sleep. In some implementations, the predetermined amount of continuous sleep can include, for example, at least 10 minutes of sleep in the second non-REM stage, the third non-REM stage, and / or the REM stage including an awake state of 2 minutes or less, the first non-REM stage, and / or the transitions therebetween. In other words, the Persistent Sleep Onset Latency requires, for example, a maximum of 8 minutes of continuous sleep within the second non-REM stage, the third non-REM stage, and / or the REM stage. In other implementations, the predetermined amount of continuous sleep can include at least 10 minutes of sleep in the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or the REM stage after the initial sleep time. In such implementations, the predetermined amount of continuous sleep can exclude any micro-awakenings (e.g., a 10-second micro-awakening does not start 10 minutes from the beginning).

[0105] The Wake After Sleep Onset (WASO) is associated with the total time the user is awake between the initial sleep time and the wake time. Thus, the Wake After Sleep Onset includes short-duration micro-awakenings (e.g., micro-awakenings MA1 and MA2 shown in FIG. 4) during the sleep session, whether conscious or unconscious. In some implementations, the Wake After Sleep Onset (WASO) is defined as the Persistent Wake After Sleep Onset (PWASO) that includes only the total time of awakenings having a predetermined length (e.g., 10 seconds or more, 30 seconds or more, 60 seconds or more, about 5 minutes or more, about 10 minutes or more, etc.).

[0106] Sleep efficiency (SE) is determined as the ratio of total in-bed time (TIB) to total sleep time (TST). For example, if the total in-bed time is 8 hours and the total sleep time is 7.5 hours, the sleep efficiency of that sleep session is 93.75%. Sleep efficiency indicates the user's sleep hygiene. For example, if the user goes to bed and spends time on other activities (e.g., watching TV) before sleep, the sleep efficiency will decrease (e.g., a penalty is given to the user). In some implementations, the sleep efficiency (SE) can be calculated based at least in part on the total in-bed time (TIB) and the total time the user attempts to fall asleep. In such implementations, the total time the user attempts to fall asleep is defined as the duration from the bedtime (GTS) time described herein to the wake-up time. For example, if the total sleep time is 8 hours (e.g., from 11 PM to 7 AM), the bedtime is 10:45 PM, and the wake-up time is 7:15 AM, the sleep efficiency parameter is calculated to be approximately 94%.

[0107] The fragmentation index is determined based at least in part on the number of awakenings during a sleep session. For example, if the user has two micro-awakenings (e.g., micro-awakenings MA1 and MA2 shown in FIG. 4), the fragmentation index can be represented as 2. In some implementations, the fragmentation index is scaled between integers in a predetermined range (e.g., between 0 and 10).

[0108] A sleep block is associated with a transition between any sleep stage (e.g., the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or REM) and the wake stage. A sleep block can be calculated, for example, with a resolution of 30 seconds.

[0109] In some implementations, the systems and methods described herein generate or analyze a hypnogram that includes sleep-wake signals and, based at least in part on the sleep-wake signals of the hypnogram, determine the in-bed time (t bed ), the bedtime (t GTS ), the initial sleep time (t sleep ), one or more first micro-awakenings (e.g., MA1 and MA2), the wake-up time (t wake) determining or identifying the bedtime (t rise ) or any combination thereof.

[0110] In other implementations, one or more sensors 130 can be used to define a sleep session by determining or identifying the bedtime (t bed ), the bedtime (t GTS ), the initial sleep time (t sleep ), one or more first micro-awakenings (e.g., MA1 and MA2), the wake-up time (t wake ), the wake-up time (t rise ), or any combination thereof. For example, the bedtime t bed can be determined at least in part based on data generated by, for example, the motion sensor 138, the microphone 140, the camera 150, or any combination thereof. The bedtime can be determined, for example, based on data from the motion sensor 138 (e.g., data indicating no movement by the user), data from the camera 150 (e.g., data indicating no movement by the user and / or data indicating that the user has turned off the light), data from the microphone 140 (e.g., data indicating that the TV has been turned off), data from the user device 170 (e.g., data indicating that the user has stopped using the user device 170), data from the pressure sensor 132 and / or the flow sensor 134 (e.g., data indicating that the user has turned on the respiratory therapy device 122, data indicating that the user has put on the user interface 124, etc.), or any combination thereof at least in part.

[0111] FIG. 5 is a diagram showing the generation of acoustic data in response to an acoustic reflection indicating one or more features of a user interface according to an aspect of the present disclosure. The generation of acoustic data includes an acoustic sensor 141 that includes a microphone 140 (or any type of audio transducer) and a speaker 142. However, as described above, the speaker 142 can be replaced with another device capable of generating an acoustic signal, such as a motor of the respiratory therapy device 122. The microphone 140 and the speaker 142 are shown at specific positions with respect to a conduit 126 that is connected to a respiratory therapy device 122 (not shown). However, the positions of the microphone 140 and the speaker 142 may be different from those shown, as described above.

[0112] The speaker 142 emits an acoustic signal 302 within the conduit 126. The acoustic signal 302 is in the form of sound. The sound may be one or more of a standard sound (e.g., an original unmodified sound from a commercially available sound application), a custom sound, a non-audible frequency, white noise sound, a broadband impulse, a continuous sine wave, a square wave, a sawtooth wave, a frequency modulated sine wave (e.g., a chirp). According to some other implementations, the acoustic signal 302 can be in the form of one or more forms of audible sound or ultrasonic sound. According to some other implementations, the acoustic signal 302 is in the form of a non-audible sound where the sound cannot be heard, based on one or both of the frequency of the sound (e.g., a frequency outside the frequency range of human hearing) or the amplitude of the sound (e.g., an amplitude so low that the sound is not loud enough for human perception).

[0113] In one or more implementations, the acoustic signal 302 is transmitted at a specific time, such as when the user first wears the user interface, after the user removes the user interface, or after detecting an apnea event or a hypopnea event (e.g., after detecting an apnea event or a hypopnea event using a respiratory therapy device). The specific monitoring time is selected to be, for example, a duration of at least 4 seconds at intervals of 0.1 seconds.

[0114] The acoustic signal 302 moves downward along the length L of the conduit 126 until it contacts a feature 304 of the user interface 124, such as at or near the connection 306 between the user interface 124 and the conduit 126. The feature 304 may include an expansion (or constriction) of a path 308 formed through the conduit 126 and the user interface 124. The expansion at the feature 304 causes a change in the acoustic impedance of the acoustic signal 302 and an acoustic reflection 310. The acoustic reflection 310 moves back upward along the length L of the conduit 126 until it reaches the microphone 140. The microphone 140 detects the acoustic reflection 310 and generates acoustic data in response to the acoustic reflection 310. As will be described below with reference to FIGS. 6 and 14A, the generated acoustic data is analyzed to classify and / or characterize the user interface.

[0115] The acoustic signal 302 can continue beyond the feature 304 to the user interface 124. Although not shown, the user interface 124 may include one or more additional features that change the acoustic impedance of the acoustic signal 302 to further generate an acoustic reflection 310. Thus, although only the feature 304 is illustrated and described as causing the acoustic reflection 310, there may be multiple features of the user interface 124 that can all contribute to the acoustic reflection 310.

[0116] FIG. 6 is a flowchart of a method 400 for characterizing a user interface according to an aspect of the present disclosure. For convenience, the following description relates to the method 400 executed by a respiratory therapy system, such as the respiratory therapy system 120. However, one or more other devices, such as one or more user devices 170, may execute the method 400. Such a user device 170 may communicate, for example, with the respiratory therapy system 120 that analyzes and characterizes data as described below.

[0117] In step 402, the respiratory therapy system 120 generates acoustic data associated with the acoustic reflections of the acoustic signal. The acoustic data may be generated based on an acoustic transducer that detects the acoustic reflections. For example, the acoustic transducer may be a microphone that converts the pressure wave of the acoustic reflection into an electrical signal representing the data.

[0118] The acoustic reflections at least partially indicate one or more features of the user interface coupled to the respiratory therapy device via the conduit. As the acoustic signal travels down along the conduit, it interacts with one or more features of the conduit, one or more features of the connection between the conduit and the user interface, one or more features of the user interface, etc. This interaction can reflect the acoustic signal to generate an acoustic reflection. Although described in detail later, in FIGS. 15-20, the acoustic reflections are at least partially caused by one or more features of the user interface, thus indicating one or more features of the user interface.

[0119] In one or more implementations, the acoustic signal may be transmitted to the conduit connected to the user interface via an acoustic transducer such as a speaker. Alternatively, or additionally, the acoustic signal may be transmitted into the conduit via a motor of the respiratory therapy device connected to the conduit. For example, the motor of the respiratory therapy device may make a sound when forcing air through the conduit. The emitted sound may be an acoustic signal or at least part of the acoustic signal.

[0120] In one or more implementations, the acoustic signal may be a sound audible to a human (e.g., an average human with average human hearing). The sound is audible to a human when it has a large enough amplitude for human detection and has a frequency within the frequency range of human hearing, typically from about 20 Hz to about 20,000 Hz.

[0121] In one or more implementations, the acoustic signal may be a sound that is inaudible to humans (e.g., an average human with average human hearing). A sound is inaudible if its amplitude is less than the lowest amplitude perceptible to humans. Alternatively, or additionally, a sound is inaudible to humans if it has a frequency outside the human audible frequency range, e.g., from about 20 Hz to about 20,000 Hz as described above. For example, in one or more implementations, the acoustic signal may be an ultrasonic sound having a frequency higher than the highest frequency perceptible by human hearing.

[0122] In one or more implementations, method 400 may be performed when a user interface is connected to a user (e.g., when worn by the user or engaged with the user's face). That is, an acoustic signal may be transmitted and acoustic data may be generated based on an acoustic reflection by the user interface connected or coupled to the user. Also, the user interface may be connected or coupled to the user during, before, or after the respiratory therapy system provides treatment to the user. For example, a flow of pressurized air may be provided to the user interface via a conduit during generation of the acoustic data. Providing treatment may generate an acoustic signal, e.g., by a motor of a respiratory therapy device that transmits the acoustic signal. Alternatively, the acoustic signal may be transmitted within the conduit with no flow of pressurized air through the conduit to the user interface.

[0123] Alternatively, this method may be performed when the user interface is not connected or coupled to the user. Specifically, an acoustic reflection indicating one or more features of the user interface does not require the presence of the user. Thus, method 400 may be performed before the user wears the user interface or after the user removes the user interface. Yes.

[0124] Step 402 was described as being based on an acoustic signal and an acoustic reflection. In one or more implementations, however, acoustic data generated from multiple acoustic reflections from multiple acoustic signals may be generated. The acoustic signal may be the same acoustic signal that is repeatedly transmitted, or different acoustic signals that are transmitted together, or a single acoustic signal that is transmitted separately. If there are multiple acoustic signals of multiple acoustic reflections, the generated acoustic data may be the average of the multiple acoustic reflections from the multiple acoustic signals.

[0125] In step 404, the respiratory therapy system 120 analyzes the generated acoustic data. The analysis includes windowing the generated acoustic data based at least in part on at least one feature of the respiratory therapy system, such as at least one feature of the user interface and at least one feature of the conduit. At least one feature of the user interface may be, for example, the expansion from the connection of the conduit to the chamber, which is formed by the cushion of the full-face mask against the user's face. At least one feature of the conduit may be, for example, the length of the conduit. More specifically, the length may be the distance from the acoustic sensor to the location where the conduit connects to the user interface. In one or more implementations, the windowing may be based at least in part on at least one of one or more features of the user interface. Windowing, as described below, is a selection of a portion of the generated acoustic data that is used for additional analysis and / or characterization of the user interface. Windowing can focus on the generated acoustic data that can distinguish and identify the various user interfaces present in the respiratory therapy system. Thus, windowing can focus on the generated acoustic data indicative of the user interface. Windowing can advantageously ignore regions of the signal that may confuse the estimation of the user interface, such as changes in the length of the conduit, differences in respiratory therapy systems, etc., such as different respiratory therapy devices. Thus, when windowing the generated acoustic data, only the region of the acoustic signal and the corresponding data associated with the user interface are selected. As a result, windowing can ignore regions of the acoustic signal and corresponding data that may confuse the estimation of the user interface, such as the factors described above.

[0126] At least one feature has a corresponding point in the generated acoustic data that functions as a reference point. The reference point provides a basis for analyzing the generated acoustic data of different types of user interfaces, conduits, respiratory therapy devices, etc. Thus, the reference point functions as a reference for further analyzing the generated acoustic data and provides consistency between different acoustic data generated under different circumstances.

[0127] In one or more implementations, the reference point may correspond to the position of one or more features along a path that is at least partially formed by the conduit and the user interface. For example, the reference point may correspond to a feature of the conduit or a feature of the connection between the conduit and the user interface. In one or more implementations, the reference point may correspond to a feature of the user interface itself. In one or more implementations, the reference point is the same in a given user interface. In one or more implementations, the reference point is the same in all possible different user interfaces. Features can be made common across different types of user interfaces such that there are features, or at least a variable amount of features, in all respiratory therapy systems including the user interface connected to the conduit. For example, this feature may be an expansion of the acoustic path that occurs at the connection between the conduit and the user interface. Such an expansion may not be the same for each combination of conduit and user interface, but each combination of conduit and user interface includes such an expansion. Thus, the expansion at the connection between the conduit and the user interface can be made the feature corresponding to the reference point user in the window processing of the generated acoustic data. It can be made the feature corresponding to the reference point user in the window processing of the generated acoustic data.

[0128] Broadly speaking, the reference point may be any feature that causes a change in acoustic impedance and thus exists in the acoustic data generated from the acoustic reflection. In one or more implementations, the change in acoustic impedance may be based at least in part on a constriction of a path formed by the conduit and the user interface at (i) the user interface, (ii) the connection between the conduit and the user interface, or (iii) a combination thereof. Alternatively, the change in acoustic impedance may be based at least in part on an expansion of a path at (i) the user interface, (ii) the connection between the conduit and the user interface, or (iii) a combination thereof.

[0129] In one or more implementations, further described below, the analysis of the generated acoustic data includes performing a deconvolution of the generated acoustic data, such as calculating the cepstrum of the generated acoustic data. In that case, the reference point is the minimum point within a predetermined interval of the deconvolution of the generated acoustic data. Alternatively, the reference point is the maximum point within a predetermined interval of the deconvolution of the generated acoustic data. As described above, the reference point may be based on the connection between the user interface and the conduit, and the predetermined interval may be based on the length of the conduit. More specifically, the predetermined interval may be based on the distance between the connection between the conduit and the user interface and the acoustic sensor.

[0130] Referring to FIG. 7, calculated cepstra 500a and 500b resulting from acoustic data generated for two different acoustic reflections are shown. The two cepstra 500a and 500b are shown for illustrative purposes only and are not intended to be limiting. For example, one cepstrum can be calculated for the generated acoustic data characterizing the user interface. The two cepstra 500a and 500b are two cepstra calculated from different generated acoustic data for the same user interface.

[0131] These plots show the cephlency on the X-axis and the amplitude on the Y-axis. Cephlency is similar to time or distance, but the unit on the x-axis is simply the sample or number of samples of the acoustic reflection. The unit on the Y-axis may be the unit for measuring the amplitude. The values near the value of 250 on the X-axis are the large minima 502a and 502b that can be used as reference points for analyzing the generated acoustic data. This corresponds to the expansion of the path within the conduit, such as the connection between the conduit and the user interface.

[0132] Referring again to step 404 of FIG. 6, in one or more implementations, the window processing can include a first window processing of the generated acoustic data and a second window processing of the generated acoustic data. In one or more implementations, the first window processing may identify or specify the features of the user interface corresponding to the reference point. The second window processing may identify or specify further features of the user interface. The first window processing of the generated acoustic data and the second window processing of the generated acoustic data can be varied by the amount of the generated acoustic data selected before the reference point of the generated acoustic data, the amount of the generated acoustic data selected after the reference point of the generated acoustic data, or a combination thereof. Regarding the length of the path by the conduit and the user interface, the first window processing can capture only about 10 cm before and after the reference point, for example, only about 10 cm before and after the connection point between the conduit and the user interface. In contrast, the second window processing can capture about 35 cm before the reference point and about 50 cm after the reference point.

[0133] The amount of data captured by the window processing is associated with the window processing It may vary based on the amount of generated acoustic data required for a particular analysis. For example, the first windowing process is simply to identify the reference point. As a result, since the focus is purely on determining the reference point, the size of the window resulting from the windowing process may be small. Once the reference point is determined, a second windowing process may occur, resulting in a potentially larger second window. The larger second window may capture the generated acoustic data that is sent to the characterization step described below. Since the second window generated by the second windowing process may be larger than the first window generated by the first windowing process, the characterization step can benefit from additional data using a large window that captures more features to characterize the user interface compared to the first window. Alternatively, the sizes of the windows generated during the first and second windowing processes may be reversed so that the first window is larger than the second window. The larger first window can assist in determining the correct reference point for analysis.

[0134] Referring to FIG. 8, windowed cepstra 600a and 600b corresponding to cepstra 500a and 500b respectively are shown after the windowing process has been performed as described above. The windowing process removed the generated acoustic data after approximately 350 values on the X-axis of FIG. 7 and before approximately 200 values on the X-axis of FIG. 7. The windowing process is not used for characterizing the user interface and is performed to remove generated acoustic data that potentially interferes with additional analysis and characterization of the acoustic data for determining the user interface. The remaining generated acoustic data is approximately 80 on both sides of reference points 502a and 502b.

[0135] Referring to FIG. 9, as shown by the windowed cepstrum 600a for the windowed cepstrum 600b, a further result of the windowing process is an alignment based on the reference points 502a and 502b. This alignment can further enhance the consistency between the additional analysis of the generated acoustic data and the characteristics of the analyzed acoustic data, as described below.

[0136] As described above, in one or more implementations, the respiratory therapy system 120 that analyzes the generated acoustic data may include deconvolution of the generated acoustic data prior to windowing the generated acoustic data. In one or more implementations, the deconvolution of the generated acoustic data may include calculating the cepstrum of the generated acoustic data. The cepstrum identifies distances associated with acoustic reflections that indicate the positions of one or more characteristics of the user interface. More specifically, the cepstrum indicates the position of each of one or more physical characteristics along the acoustic path of the acoustic signal by identifying one or more distances associated with each of the corresponding one or more acoustic reflections. The calculation of the cepstrum can be realized by processing the acoustic reflections in a series of steps: calculating the frequency spectrum of the acoustic reflections by fast Fourier transform, taking the natural logarithm of the absolute amplitude of this frequency spectrum, and performing an inverse Fourier transform to calculate the real part of the signal to generate the cepstrum. This process may be repeated at multiple time intervals to estimate the average cepstrum. The resulting cepstrum signal transforms the acoustic reflections into a cephalic region similar to time and / or distance reflection decomposition. In one or more implementations, the cepstrum may be calculated using a fast Fourier transform of 4096 samples of acoustic reflections with a 50% sample overlap as needed. These 4096 samples represent acoustic data generated in approximately 0.2 seconds.

[0137] In one or more implementations, the analysis of the generated acoustic data may include calculating the derivative of the cepstrum to determine the rate of change of the cepstrum signal. Step 406 As will be described later, the derivative of the cepstrum may provide additional information used to characterize the user interface. With the derivative of the cepstrum, the network can also find alternative features from the same input.

[0138] In one or more implementations, a respiratory therapy system 120 that analyzes the generated acoustic data may include normalizing the generated acoustic data. Normalizing the generated acoustic data may account for confounding conditions. Confounding conditions include, for example, microphone gain, respiratory amplitude, treatment pressure, changes in conduit length, coiled or extended conduits, different treatment pressures, ambient temperature, or combinations thereof. In one or more implementations, normalizing the generated acoustic data may include subtracting the mean value from the generated acoustic data, dividing by the standard deviation of the generated acoustic data, or combinations thereof. The purpose of normalization is to input the cepstra of all analyzed acoustic data of similar and amplitude into a natural range for characterization. In one or more implementations, the purpose of normalization is to always set the mean value of the cepstrum to zero.

[0139] Referring to FIG. 10, normalized cepstra 800a and 800b corresponding to cepstra 500a and 500b, respectively, are shown. As indicated by the Y-axis values of FIG. 10 relative to FIG. 9, the amplitudes are adjusted such that the cepstra have similar amplitudes and are within the range of natural characterization, as described below.

[0140] Referring again to FIG. 6, at step 406, the respiratory therapy system 120 characterizes the user interface based at least in part on the analyzed acoustic data. Characterizing the user interface may include inputting the analyzed acoustic data into a supervised machine learning model (e.g., a (deep) neural network such as a convolutional neural network) to determine the user interface. In one or more implementations, determining the user interface may include determining the form factor or type of the user interface (e.g., full face, nasal, pillow, etc.), the model of the user interface (e.g., the AirFit TM F20 mask), the size of one or more elements of the user interface, or a combination thereof.

[0141] In one or more embodiments, the convolutional neural network includes one or more convolutional layers and one or more max pooling layers. In one or more embodiments, the CNN includes one or more convolutional layers. In one or more embodiments, the CNN may include N features. Each of the N features may be L samples in width. The CNN can further enable max pooling of M samples. Each of the N features is a unique plot or wave within the analyzed acoustic data, such as within the cepstrum. In one or more embodiments, since N is equal to 64, the CNN may account for 64 unique plots or features within the analyzed acoustic data. Each of the L samples in width represents a data point generated based on an acoustic reflection. Thus, the L samples represent L data points generated in response to an acoustic reflection. The L samples approximately match the expected size of the features within the acoustic reflection. In one or more embodiments, the L samples can approximately correspond to a length of about 25 cm. The M samples can be, for example, 20 samples, whereby the natural time invariance within the model is considered to account for the shift between different mask reflections. In one or more embodiments, the ratio of N to M can be from 1:1 to 4:1. In one or more embodiments, before classification, the output of the convolutional layer can be fed to one or more fully connected dense layers.

[0142] The disclosed embodiments have been illustrated and described with respect to one or more embodiments. However, those skilled in the art can conceive of equivalent changes and modifications upon reading and understanding this specification and the accompanying drawings. Note that a particular feature of the present invention may be disclosed with respect to only one of several embodiments, but such a feature can be combined with one or more other features in other embodiments that are desirable and advantageous for any given or particular application.

[0143] In one or more implementations, the CNN is trained based on an additional training set by adding 20 samples of random cyclic shifts to the features used for training the CNN. Specifically, a transformation can be applied to the features within the dataset used for training the CNN, thereby making the time portions of the acoustic signal shifts, the shifts of the acoustic data within the window, the addition of noise, and other types of shifts for a more robust system more robust.

[0144] While the focus of this application is to identify a user interface, in one or more implementations, the same methods disclosed in this application for identifying a user interface can be used to identify a conduit based on the same methods disclosed for identifying a user interface. For example, different conduits (e.g., different diameters, lengths, materials, etc.) can be used within a respiratory therapy system. The system and method can be used to determine different conduits.

[0145] In one or more implementations, conduit 126 can be identified instead of the user interface. Further details regarding the identification of conduit 126 can be found in U.S. Provisional Patent Application No. 63 / 107,763, filed October 30, 2020, which is hereby incorporated by reference in its entirety. In one or more implementations, the conduit is identified along with the identification of the user interface. When both are identified, the order of identification includes both being identified simultaneously or one being identified first (e.g., the conduit or the user interface) and then the other being identified.

[0146] In one or more implementations, identifying one of a conduit or a user interface can narrow down possible options for identifying the other of the conduit or the user interface. For example, a particular conduit may be compatible with only a subset of all user interfaces of a respiratory therapy system. Thus, identifying the conduit can limit subsequent identification of the user interface to only those user interfaces within the subset. By a similar narrowing, simplification of characterization is achieved by first identifying the user interface and then identifying the conduit.

[0147] The characterization of the above user interface generally focuses on the highest level of the type of user interface and can be narrowed down to specific models, aesthetic styles, etc. However, user interfaces such as the user interface 124 in FIGS. 1 and 2 are distinguished in different categories, and this distinction can be regarded as a distinction of the user interface wider than the type (for example, full face, nose, nasal pillow, etc.), or at least as wide as the type. According to some implementations, the category is defined, at least in part, as a directly connected user interface (referred to herein as a "direct category" user interface) or an indirectly connected user interface (referred to herein as an "indirect category" user interface). In one or more implementations, the difference between direct and indirect connections is based on the elements of the user interface that connect to the conduit of the respiratory therapy system. That is, the description of directly and indirectly connected user interfaces relates to how pressurized air is sent from the end of the conduit of the respiratory therapy system through the user interface to the user. As will be described in more detail below, a directly connected user interface generally directly connects the conduit of the respiratory therapy system to the cushion and / or frame of the user interface, optionally via a connector of the user interface. Thus, pressurized air is sent directly from the conduit of the respiratory therapy system (optionally via a connector) to a chamber formed by the cushion (or cushion and frame) of the user interface against the user's face is sent.

[0148] Figures 11A and 11B respectively show a perspective view and an exploded view of one implementation of a directly connected user interface (a user interface of the "direct category") according to an aspect of the present disclosure. The direct category of the user interface 900 generally includes a cushion 930 and a frame 950 that define a spatial volume around the user and / or the nose. During use, the spatial volume receives pressurized air for passage into the user's airway. In some embodiments, the cushion 930 and the frame 950 of the user interface 900 form an integrated component of the user interface. The user interface 900 assembly may further be considered to include a headgear 910, which is generally a strap assembly in the case of the user interface 900, and an optional connector 970. The headgear 910 is configured to be generally disposed around at least a portion of the user's head when the user wears the user interface 900. The headgear 910 is coupled to the frame 950 and may be disposed over the user's head such that the user's head is disposed between the headgear 910 and the frame 950. The cushion 930 is disposed between the user's face and the frame 950 and forms a seal with the user's face. The optional connector 970 is configured to couple at one end to the frame 950 and / or the cushion 930 and to couple to a conduit of a respiratory therapy device (not shown). Pressurized air can flow directly from the conduit of the respiratory therapy system, through the connector 970, into the spatial volume defined by the cushion 930 (or the cushion 930 and the frame 950) of the user interface 900. From the user interface 900, the pressurized air reaches the user's airway through the user's mouth, nose, or both. Alternatively, if the user interface 900 does not include the connector 970, the conduit of the respiratory therapy system can be directly connected to the cushion 930 and / or the frame 950.

[0149] In the case of an indirectly connected user interface (a user interface in the "indirect category"), as described in more detail below, the conduit of the respiratory therapy system is indirectly connected to the cushion and / or frame of the user interface. Another element of the user interface other than the connector is between the conduit of the respiratory therapy system and the cushion and / or frame. This additional element sends pressurized air from the conduit of the respiratory therapy system to the volumetric space formed between the cushion of the user interface (or the cushion and frame) and the user's face. Thus, the pressurized air is indirectly sent from the conduit of the respiratory therapy system to the volumetric space defined by the cushion of the user interface (or the cushion and frame) against the user's face. Further, according to some implementations, the category of indirectly connected user interfaces can be described as at least two different categories: "indirect headgear" and "indirect conduit". In the category of indirect headgear, the conduit of the respiratory therapy system is optionally connected to the headgear conduit via a connector, and the headgear conduit is then connected to the cushion (or cushion and frame). Thus, the headgear is configured to send pressurized air from the conduit of the respiratory therapy system to the cushion of the user interface (or cushion and frame). Thus, this headgear conduit within the headgear of the user interface is configured to send pressurized air from the conduit of the respiratory therapy system to the cushion of the user interface.

[0150] Figures 12A and 12B respectively show a perspective view and an exploded view of one implementation of an indirect conduit user interface 1000 according to an aspect of the present disclosure. The indirect conduit user interface 1000 includes a cushion 1030 and a frame 1050. In some embodiments, the cushion 1030 and the frame 1050 form an integral component of the user interface 1000. The indirect conduit user interface 1000 further includes a headgear 1010, such as a strap assembly, an optional connector 1070, and a user interface conduit 1090 (referred to in the art as a "mini-tube" or "flexi-tube") It may be considered to include (which is often the case). Generally, the user interface conduit is (i) more flexible than the conduit 126 of the respiratory therapy system, (ii) has a diameter smaller than the diameter of the conduit 126 of the respiratory therapy system, or both (i) and (ii). The user interface conduit may have a length shorter than that of the conduit. Similar to the user interface 900, the headgear 1010 is configured to be generally disposed around at least a portion of the user's head when the user wears the user interface 1000. The headgear 1010 is coupled to the frame 1050 and is disposed on the user's head such that the user's head is disposed between the headgear 1010 and the frame 1050. The cushion 1030 is disposed between the user's face and the frame 1050 and forms a seal on the user's face. The optional connector 1070 is configured to be coupled to the frame 1050 and / or the cushion 1030 at one end and to the conduit 1090 of the user interface 1000 at the other end. In other implementations, the conduit 1090 may be directly connected to the frame 1050 and / or the cushion 1030. The conduit 1090 is configured to be connected to the conduit 126 (FIG. 12A) of a respiratory therapy system (not shown) at an end opposite to the frame 1050 and the cushion 1030. Pressurized air can flow from the conduit 126 (FIG. 12A) of the respiratory therapy system through the user interface conduit 1090 and, optionally, through the connector 1070 into the volume of space defined by the cushion 1030 (or the cushion 1030 and the frame 1050) of the user interface 1000 against the user's face. From the volume of space, the pressurized air reaches the user's airway through the user's mouth, nose, or both.

[0151] Considering the above configuration, the pressurized air is not directly sent from the conduit 126 (FIG. 12A) of the respiratory therapy system (not shown), but is sent from the conduit 126 (FIG. 12A) of the respiratory therapy system through the user interface conduit 1090 to the cushion 1030 (or the cushion 1030 and the frame 1050). Therefore, the user interface 1000 is an indirectly connected user interface.

[0152] Figures 13A and 13B respectively show a perspective view and an exploded view of an implementation of an indirect headgear user interface 1100 according to an aspect of the present disclosure. The indirect headgear user interface 1100 includes a cushion 1130. The indirect headgear user interface 1100 may further be considered to include a headgear 1110 (including a strap 1110a and a headgear conduit 1110b), and optionally a connector 1170. Similar to the user interfaces 900 and 1000, the headgear 1110 is configured to be generally disposed around at least a portion of the user's head when the user wears the user interface 1100. The headgear 1110 includes a strap 1110a, and the strap 1110a is coupled to the headgear conduit 1110b and is disposed on the user's head such that the user's head is disposed between the strap 1110a and the headgear conduit 1110b. The cushion 1130 is disposed between the user's face and the headgear conduit 1110b and forms a seal with the user's face. The connector 1170 is configured to couple to the headgear 1110 at one end and to a conduit of a respiratory therapy system at the other end. In other implementations, the connector 1170 can be optional, or the headgear 1110 can be directly connected to a conduit of a respiratory therapy system. The headgear conduit 1110b can be configured to send pressurized air from a conduit of a respiratory therapy system to the cushion 1130, or more specifically, to a volume of space surrounded by the user cushion around the user's mouth and / or nose. Accordingly, the headgear conduit 1110b is hollow and provides a passage for pressurized air. Both sides of the headgear conduit 1110b can be hollow to provide two passages for pressurized air. Alternatively, only one side of the headgear conduit 1110b can be made hollow to provide a single passage. In the implementation shown in Figures 13A and 13B, the headgear conduit 1110b includes two passages disposed on both sides of the user's head / face during use. Alternatively, only one passage of the headgear conduit 1110b can be made hollow to provide a single passage. Pressurized air is from a conduit of a respiratory therapy system, through the connector 1170 (if present) and It can flow through the headgear conduit 1110b into the space volume between the cushion 1130 and the user's face. From the space volume between the cushion 1130 and the user's face, the pressurized air reaches the user's airway through the user's mouth, nose, or both.

[0153] Considering the above configuration, since the pressurized air is not directly sent from the conduit of the respiratory therapy system to the space volume between the cushion 1130 and the user's face, but is sent from the conduit of the respiratory therapy system to the space volume between the cushion 1130 and the user's face via the headgear conduit 1110b, the user interface 1100 is an indirect headgear user interface.

[0154] In one or more implementations, the difference between the direct category and the indirect category is defined in terms of the distance that the pressurized air travels before reaching the space volume defined by the cushion of the user interface that forms a seal on the user's face, excluding the connector of the user interface that connects to the conduit after exiting the conduit of the respiratory therapy device. This distance is shorter for the user interface of the direct category than for the user interface of the indirect category, such as less than 1 centimeter (cm), less than 2 cm, less than 3 cm, less than 4 cm, or less than 5 cm. This is because the pressurized air travels through additional elements of the conduit of the respiratory therapy system, such as the user interface conduit 1090 or the headgear conduit 1110b, before reaching the space volume defined by the cushion (or cushion and frame) of the user interface that forms a seal on the user's face for the indirect category of user interfaces.

[0155] FIG. 14A is a flowchart of a method 1200 for classifying a user interface according to an aspect of the present disclosure. For convenience purposes, the following description relates to a method 1200 executed by a respiratory therapy system, such as respiratory therapy system 120. However, one or more other devices, such as one or more user devices 170, may execute method 1200. Such a user device 170 may communicate, for example, with a respiratory therapy system 120 that analyzes and classifies the data described below.

[0156] In step 1202, the respiratory therapy system 120 generates acoustic data related to an acoustic reflection of an acoustic signal. Similar to step 402 above, the acoustic data may be generated based on an acoustic transducer that detects the acoustic reflection. For example, the acoustic transducer may be a microphone that converts the pressure wave of the acoustic reflection into an electrical signal representing data. The acoustic reflection at least partially indicates one or more features of the user interface coupled to the respiratory therapy device via a conduit. As the acoustic signal travels down the conduit, it interacts with one or more features of the conduit, one or more features of the connection between the conduit and the user interface, one or more features of the user interface, etc. This interaction can cause the acoustic signal to be reflected and generate an acoustic reflection. Since the acoustic reflection is at least partially caused by one or more features of the user interface, it indicates one or more features of the user interface.

[0157] In one or more implementations, the acoustic signal may be transmitted into a conduit connected to the user interface via an acoustic transducer, such as a speaker. Alternatively, or additionally, the acoustic signal may be transmitted into the conduit via a motor of the respiratory therapy device connected to the conduit. For example, the motor of the respiratory therapy device may make a sound when forcing air through the conduit. The emitted sound may be an acoustic signal or at least part of the acoustic signal.

[0158] In one or more implementations, the acoustic signal may be a sound audible to a human (e.g., an average human with average human hearing). A sound is audible to a human if it has an amplitude large enough for human detection and has a frequency within the human hearing frequency range, typically from about 20 Hz to about 20,000 Hz. This is the case when it has an amplitude large enough for human detection and has a frequency within the human hearing frequency range, typically from about 20 Hz to about 20,000 Hz.

[0159] In one or more implementations, the acoustic signal may be a sound inaudible to a human (e.g., an average human with average human hearing). If the amplitude of the sound is less than the lowest amplitude perceptible by a human, the sound cannot be heard. Alternatively, or additionally, if the sound has a frequency outside the human hearing frequency range, from about 20 Hz to about 20,000 Hz as described above, the sound is inaudible to a human. For example, in one or more implementations, the acoustic signal may be an ultrasonic sound having a frequency higher than the highest frequency perceptible by human hearing.

[0160] In one or more implementations, method 1200 is performed when a user interface is connected or worn by a user. That is, an acoustic signal may be transmitted and acoustic data may be generated based on an acoustic reflection by a user interface connected or coupled to the user. Also, the user interface may be connected or coupled to the user while the respiratory therapy system is providing treatment to the user, before providing treatment to the user, or after providing treatment to the user. For example, a flow of pressurized air may be provided to the user interface via a conduit during generation of the acoustic data. The provision of treatment may, for example, generate an acoustic signal by a motor of a respiratory therapy device that transmits the acoustic signal. Alternatively, an acoustic signal can be transmitted in the conduit with no flow of pressurized air through the conduit to the user interface.

[0161] Alternatively, this method may be performed when the user interface is not connected or coupled to the user. Specifically, the acoustic reflections indicating one or more features of the user interface do not require the presence of the user. Thus, method 1200 may be performed before the user wears the user interface or after the user removes the user interface.

[0162] Step 1202 has been described as being based on an acoustic signal and an acoustic reflection, but in one or more implementations, acoustic data generated from multiple acoustic reflections from multiple acoustic signals may be generated. The acoustic signals may be the same acoustic signal transmitted repeatedly, or different acoustic signals transmitted together, or a single acoustic signal transmitted separately. If there are multiple acoustic signals for multiple acoustic reflections, the generated acoustic data may be the average of the multiple acoustic reflections from the multiple acoustic signals.

[0163] In one or more implementations, the generated acoustic data can include a primary reflection within the acoustic reflection of the acoustic signal. Further, or alternatively, the generated acoustic data can include a secondary reflection within the acoustic reflection of the acoustic signal. Further, or alternatively, the generated acoustic data can include a tertiary reflection within the acoustic reflection of the acoustic signal. The analysis performed in one or more of the next steps can be performed from a primary reflection, a secondary reflection, a tertiary reflection, or a combination thereof, for example, a combination of a primary reflection and a secondary reflection.

[0164] Step 402 of method 400 in FIG. 6 and step 1202 of method 1200 in FIG. 14A are described in different parts of this specification and shown as different steps in different flow diagrams, but in one or more implementations, step 402 and step 1202 can be the same step.

[0165] In step 1204, the respiratory therapy system 120 analyzes the generated acoustic data to associate one or more features of the user interface with one or more signatures within the generated acoustic data. The one or more signatures can include, for example, the maximum amplitude of the transform, the minimum amplitude of the transform, the standard deviation of the transform, the skewness of the transform, the kurtosis of the transform, the median of the absolute value of the transform, the sum of the absolute value of the transform, the sum of the positive region of the transform, the sum of the negative region of the transform, the fundamental frequency, the energy corresponding to the fundamental frequency, the average energy, at least one resonance frequency of the combination of the conduit and the user interface, the change in at least one resonance frequency, the number of peaks within the range, the peak prominence, the distance between peaks, or a combination thereof. Generally, a signature can be composed of one or more acoustic feature quantities within the acoustic data. In one or more implementations, one or more of the signatures may have one or more of the above aspects changing over time. Since the change in the aspect over time can be specific to a category, the change in the aspect over time is considered to be one or more signatures specific to a particular category. The change occurs during an iteration of method 1200, such as a continuous iteration over several minutes, or during multiple iterations of method 1200 during a sleep session or over the course of a night, or may occur during multiple iterations of method 1200 over several nights, weeks, months, years, etc. In one or more implementations, analyzing the generated acoustic data includes generating a spectrum of the generated acoustic data, such as by calculating a transform (e.g., discrete Fourier transform DFT or discrete cosine transform DCT) of the generated acoustic data. Thereafter, analyzing the generated acoustic data can further include calculating the logarithm of the spectrum. Thereafter, analyzing the generated acoustic data can include calculating a cepstrum of the logarithmic spectrum, such as by calculating an inverse transform (e.g., iDFT or iDCT) of the logarithmic spectrum. Next, the calculated cepstrum can be analyzed to obtain one or more signatures that can be used to classify the user interface. Non-linear operations can also be used instead of logarithm calculations.

[0166]

[0167] ​ In one or more implementations, in analyzing the generated acoustic data, it is not necessary to analyze all of the generated acoustic data. For example, in one or more implementations, analyzing the generated acoustic data can include selecting the segments of the spectrum described above. After selecting the segments, the analysis can further include calculating a direct transform (e.g., DFT or DCT) of the segments of the spectrum. From the direct transform, one or more features of the user interface can be associated with one or more signatures within the Fourier transform of the segments to identify the category of the user interface.

[0168] Alternatively, analyzing the generated acoustic data can include selecting segments of the log spectrum of the generated acoustic data described above. From the log spectrum, the analysis can include calculating a transform of the log spectrum, such as a Fourier transform of the log spectrum. Or, the analysis can include calculating an inverse transform of the log spectrum, or an inverse transform of the transform of the log spectrum.

[0169] In one or more particular implementations, FIG. 14B shows a flowchart of a particular method for analyzing acoustic data generated from step 1202, according to some implementations of the present disclosure. The method of FIG. 14B is a sub-method within step 1204 of the method of FIG. 14A.

[0170] In step 1204-1, a spectrum is generated from the acoustic data. For example, a transform such as a DFT can be generated from the acoustic data. In step 1204-2, a log is obtained from the spectrum generated in step 1204-1. Thereafter, in step 1204-3, a cepstrum can be generated from the log spectrum of step 1204-2. In step 1204-4, one or more signatures can be identified from the cepstrum generated in step 1204-3.

[0171] Return to step 1204-1. Instead of proceeding to step 1204-2, in step 1204-5, as will be further described below with respect to FIGS. 22 and 23, a subset of the spectrum can be extracted. In step 1204-6, then, a direct transformation such as a DFT can be calculated from the subset spectrum. In step 1204-7, one or more signatures can be identified from the direct transformation of the subset spectrum generated in step 1204-6.

[0172] Return to step 1204-2. Instead of proceeding to step 1204-3, in step 1204-8, as will be further described below with respect to FIGS. 22 and 23, a subset of the logarithmic spectrum can be extracted. Thereafter, in step 1204-9, a direct transformation such as a DFT can be calculated from the subset logarithmic spectrum. Alternatively, in step 1204-10, an inverse transformation such as an iDFT can be calculated from the subset logarithmic spectrum. After steps 1204-9 and 1204-10, in step 1204-7, one or more signatures can be identified from either the direct transformation or the inverse transformation from the subset logarithmic spectrum.

[0173] Referring back to FIG. 14A, segments of the generated acoustic signal can be selected by trimming the generated acoustic data based on the general positions of one or more signatures in the generated acoustic data that indicate categories of the user interface. For example, FIG. 22 shows the spectrum of an exemplary generated acoustic data. The hashed segment 2001 of the plot of the generated acoustic data can be a segment of the generated acoustic data corresponding to acoustic data related to user interface features that can be used to classify the user interface. Thus, the generated acoustic data before and after segment 2001 can be deleted, and only the generated acoustic data in segment 2001 can be analyzed (or further analyzed) to classify the user interface. FIG. 23 shows a further transformation of segment 2001 of FIG. 22, for example, the inverse Fourier transform of the logarithmic spectrum of the generated acoustic data plotted in FIG. 22. According to the plot of FIG. 23, the signatures in the plot can be used to classify the user interface.

[0174] In particular, in step 1206, the respiratory therapy system 120 classifies the user interface based at least in part on one or more signatures. As described above, the category of the user interface may be, for example, a direct frame, an indirect frame, or an indirect conduit. The one or more signatures are supplied, for example, to a machine learning model (such as linear regression, logistic regression, nearest neighbor, decision tree, PCA, naive Bayes classifier, K-means, random forest, etc.) and return a category (such as direct conduit, indirect conduit, indirect frame). In one or more implementations, the user interface can be classified based at least in part on one or more signatures that match one or more known signatures of one or more user interfaces of a known category. The user interface can be determined based on the category of the known user interface having the matching signature.

[0175] In one or more implementations, the signature used to classify the user interface can be related to primary reflections in the generated acoustic data. Alternatively, the signature used to classify the user interface can be related to reflections other than primary reflections in the generated acoustic data, such as secondary reflections, tertiary reflections, etc. Alternatively, the signature used to classify the user interface can include combinations of reflections, such as a combination of a first reflection and a second reflection, a combination of a first reflection and a third reflection, a combination of a first reflection, a second reflection, and a third reflection, a combination of a second reflection and a third reflection, etc.

[0176] According to some implementations, the direct category may include one or more signatures where the maximum amplitude of the cepstrum is greater than a threshold. FIGS. 15 and 16 are plots of the calculated cepstrum of a direct user interface according to some implementations of the present disclosure. Referring to FIG. 15, the plot shows the cepstrum of a user interface (full face mask, particularly AirFit F10 (trademark)) in the direct category, as shown in FIGS. 11A and 11B. The maximum amplitude of the cepstrum meets (e.g., is greater than) the threshold, which at least partially indicates that the associated user interface is a direct user interface. For example, the threshold may be a y-axis value of 0.2. Since the maximum peak 1301 is greater than the threshold of 0.2, the cepstrum indicates that the user interface belongs to the direct category. Similarly, in FIG. 16, the plot shows the calculated cepstrum of a user interface (nasal mask, particularly AirFit N20 Classic) in another direct category. The maximum amplitude of the cepstrum meets (e.g., is greater than) a threshold, such as the same threshold of 0.2 as in FIG. 15. Specifically, the maximum peak 1401 is greater than the same threshold of 0.2. Thus, the cepstrum indicates that the user interface belongs to the direct category.

[0177] According to some implementations, the indirect frame category includes one or more signatures whose average cepstrum value is less than a threshold. FIGS. 17 and 18 are plots of the cepstrum of an example user interface of an indirect frame category according to some implementations of the present disclosure. Referring to FIG. 17, the plot shows the cepstrum of a user interface (full face mask, particularly the AirFit F30i (trademark)) of an indirect frame category as shown in FIGS. 12A and 12B. The maximum amplitude of the cepstrum meets (e.g., is less than) the threshold, indicating that the associated user interface is an indirect frame user interface. For example, the threshold may be a y-axis value of 0.2. Since there are no peaks greater than the threshold of 0.2, the cepstrum meets the threshold, at least partially indicating that the user interface belongs to the indirect frame category. Further, the cepstrum plot of FIG. 17 shows a dispersed cepstral signature. This also at least partially indicates that the user interface belongs to the indirect frame category. Referring to FIG. 18, the plot shows the cepstrum of a user interface (nasal mask, particularly the AirFit N30i) of another indirect frame category. The maximum amplitude of the cepstrum meets a threshold less than 0.2. The cepstrum plot of FIG. 18 shows a dispersed cepstral signature that at least partially indicates that the user interface belongs to the indirect frame category.

[0178] According to some implementations, the indirect conduit category may include one or more signatures that meet the peak threshold number. This can be the result of, for example, the connection between the conduit of the respiratory therapy system and the conduit of the user interface, and the connection between the conduit of the user interface and the connector of the user interface. FIGS. 19 and 20 are plots of the cepstrum of an example of an indirect frame conduit user interface according to some implementations of the present disclosure. Referring to FIG. 19, the plot shows the cepstrum of a user interface (nasal mask, particularly AirFit N20 (trademark)) of the indirect conduit category as shown in FIGS. 13A and 13B. The cepstrum includes two prominent peaks 1701 and 1703. The prominent peak 1701 may represent, for example, the connection between the conduit of the respiratory therapy system and the conduit of the user interface. The prominent peak 1703 may represent, for example, the connection between the conduit of the user interface and the connector of the user interface. Thus, based on the presence of these two prominent peaks 1701 and 1703, the cepstrum indicates that the category of the user interface is an indirect conduit.

[0179] Referring to FIG. 20, similar to FIG. 19, the prominent peak 1801 may represent, for example, the connection between the conduit of the respiratory therapy system and the conduit of the user interface. The prominent peak 1803 may represent, for example, the connection between the conduit of the user interface and the connector of the user interface. Thus, based on the presence of these two prominent peaks 1801 and 1803 the cepstrum indicates that the category of the user interface is an indirect conduit.

[0180] Based on the above analysis, the signature in the generated acoustic data can indicate the category of the user interface. In one or more implementations, a single signature such as a prominent peak that meets a large threshold may be used to classify the user interface. However, in one or more implementations, multiple signatures may be used to classify the user interface. In one or more implementations, the classification may be similar to the characterization. For example, the classification can be performed based on inputting the analyzed acoustic data into a supervised machine learning model (such as a (deep) neural network like a convolutional neural network) to determine the user interface.

[0181] Referring to FIG. 21, as described above, the signature used to determine the category of the user interface can be related to the primary and secondary reflections in the generated acoustic data. Box 1901 in FIG. 21 shows the cepstrum portion corresponding to the primary reflection. Box 1903 in FIG. 21 shows the cepstrum portion corresponding to the secondary reflection. The amplitude of the cepstrum corresponding to the primary reflection may be greater than the amplitude of the cepstrum corresponding to the secondary reflection. Further, the shapes of the plots of the primary and secondary reflections are similar. In one or more implementations, the signature in the secondary reflection can be used to confirm the signature in the primary reflection with respect to the category of the user interface. In one or more implementations, the presence or absence of additional reflections (such as secondary and / or tertiary reflections) can be used as a signature for classifying the user interface. In one or more implementations, the ratio of the secondary reflection (and / or tertiary reflection) to the primary reflection can be used as a signature for classifying the user interface.

[0182] As described with respect to FIG. 14A, the classification of the user interface may be performed as an independent process. Alternatively, in one or more implementations, the classification of the user interface may be performed as part of a method for characterizing the user interface to determine a particular manufacturer, type, model, etc. of the user interface. The classification of the user interface may be performed before characterizing the user interface. Once the category of the user interface is determined, that category can be used to define a subset of the user interface for use in characterizing the user interface. This can improve the characterization of the user interface by removing user interfaces of different categories that may negatively affect the characterization.

[0183] Alternatively, first, the user interface may be characterized. Thereafter, the user interface may be classified. The category of the user interface can be used to verify that it matches the category of the determined user interface's manufacturer, type, model, etc. If the category does not match, the user interface can be re-characterized, with or without considering the category in the characterization process.

[0184] In one or more implementations, a confidence score may be determined during the characterization of the user interface. The confidence score can be determined, for example, based on how well the user interface is characterized. Depending on the confidence score, the characterized user interface may be classified to verify that the category of the user interface matches the type, model, manufacturer, etc. of the user interface. For example, the user interface can be classified if the confidence score is less than or greater than a threshold. This category can be used to verify the characterization of the user interface. Thus, the method 400 of FIG. 6 and the method 1200 of FIG. 14A are the present Although described in different parts of the specification and shown in different flow diagrams, in one or more implementations, the elements of methods 400 and 1200 may overlap such that the same steps of the same method achieve parallel results in the described methods 400 and 1200.

[0185] In some implementations of the present disclosure, analyzing the acoustic data may include estimating the length of conduit 126. The estimated length of conduit 126 can be used to define an analysis window of the acoustic data (e.g., a window of acoustic data used to characterize and / or classify the user interface after windowing the acoustic data).

[0186] Referring again to FIGS. 5, 6, 14A, and 14B, microphone 140 can detect reflection 310 due to feature 304 and various other reflections of acoustic signal 302. In some implementations, reflection 310 can be used to determine the length of the conduit. The acoustic data representing acoustic reflection 310 can be used to generate a time-domain intensity signal. The time-domain intensity signal is a measure of the intensity and amplitude of reflection 310 over a certain time period measured by microphone 140. The time-domain intensity signal can be converted to a frequency-domain intensity signal and then used to determine the length of conduit 126. The length of conduit 126 can be used to assist in windowing the acoustic data and / or identifying the acoustic signature of user interface 124.

[0187] In some implementations, since the acoustic data represents multiple reflections from multiple acoustic signals, the time-domain intensity signal can represent not only acoustic signal 302 but also additional reflections. For example, the reflections from each of the multiple acoustic signals can be averaged together to generate the time-domain intensity signal from this averaging.

[0188] Figure 24A shows a plot of the frequency domain intensity signal 2400 that can be obtained from the time domain intensity signal. In some implementations, the frequency domain intensity signal 2400 can be obtained from the time domain intensity signal by performing a Fourier transform on the time domain intensity signal. The frequency domain intensity signal 2400 represents the intensities of various different frequencies of the acoustic reflection 310 over a frequency band. Thus, the frequency domain intensity signal 2400 shows the frequency spectrum of the reflection 310 of the acoustic signal 302. In Figure 24A, the Y-axis represents the absolute value of the intensity of the reflection 310 shown as |x|. The intensity is plotted on a logarithmic scale using any unit. The X-axis represents the frequency of the reflection 310 measured in Hertz (Hz). In some implementations, the intensity is plotted on an absolute scale rather than a logarithmic scale.

[0189] The frequency domain intensity signal 2400 in Figure 24A represents various frequencies of the reflection 310 from the acoustic signal 302. The acoustic signal 302 and the reflection 310 form standing waves in the conduit 126 and / or the user interface 124. The conduit 126 can support standing waves at various frequencies therein. Generally, the lowest frequency of the standing wave supported by the conduit 126 is called the resonance frequency.

[0190] The wavelength of the standing wave at the resonance frequency can be obtained from the resonance frequency and is generally equal to 2L C where L C is the length of the conduit 126. The intensity of the standing wave propagating in the conduit 126 at the resonance frequency is generally greater than the intensity of the standing wave propagating in the conduit 126 at frequencies other than the resonance frequency.

[0191] The frequency domain intensity signal 2400 shows the intensities of standing waves having various frequencies propagating in the conduit 126. The peaks of the frequency domain intensity signal 2400 correspond to different standing waves supported in the conduit 126. However, the conduit 126 is the breathing apparatus 122 and the user interface Since it is connected to both the face 124, the frequency domain intensity signal 2400 also represents a standing wave that propagates partially in either the breathing device 122, the user interface 124, or both. Thus, some of the peaks of the frequency domain intensity signal 2400 correspond to standing waves that propagate partially in either the breathing device 122, the user interface 124, or both.

[0192] For example, the frequency domain intensity signal 2400 includes a peak 2402 at a low frequency, and the peak 2402 is larger than the remaining peaks of the frequency domain intensity signal. The peak 2402 mainly corresponds to a standing wave that propagates within the breathing device 122. The frequency domain intensity signal 2400 also includes peaks 2404, 2406, and 2408 that represent standing waves that propagate at least partially within the user interface 124. Finally, the frequency domain intensity signal 2400 includes a series of peaks 2410 within a portion 2401 of the frequency domain intensity signal 2400. As shown in FIG. 24A, the peaks 2410 are arranged as a generally periodic series of peaks with decreasing intensity. The series of peaks 2410 mainly corresponds to standing waves that propagate at the resonant frequency within the conduit 126 and higher harmonics at that resonant frequency, but may also correspond to standing waves that propagate at the resonant frequency within the user interface 124 and their higher harmonics. These higher harmonics are standing waves at a wavelength equal to 2L C / n, where n is a positive integer greater than or equal to 2.

[0193] FIG. 24B shows a plot of the wave period intensity signal 2420 that can be obtained from the frequency domain intensity signal 2400. In some implementations, the wave period intensity signal 2420 is obtained by performing a Fourier transform on a portion 2401 of the frequency domain intensity signal 2400 that includes a periodic series of peaks 2410. The wave period intensity signal 2420 plots the intensity of the reflections 310 within the portion 2401 of the frequency domain intensity signal against the period of the standing wave (e.g., the reciprocal of the frequency). The Y-axis of the plot represents the intensity of the reflections 310 shown as |x|. The intensity is plotted on an absolute scale. The X-axis represents the period (e.g., the reciprocal of the frequency) of the reflections 310 within the portion 2401 of the frequency domain intensity signal 2400.

[0194] As shown, the wave period intensity signal 2420 includes a peak 2422, which is significantly larger than other peaks of the wave period intensity signal 2420. Because peak 2422 is larger than other peaks, peak 2422 corresponds to a standing wave propagating at the resonance frequency in conduit 126. The position of peak 2422 along the X-axis represents the period of the standing wave propagating at the resonance frequency. By taking the reciprocal of the period of this standing wave, the resonance frequency can be determined. Next, the wavelength λ of this standing wave is generally equal to v / f, where v is the speed of sound in conduit 126. In some implementations, the speed of sound v is at least partially based on the temperature and / or air humidity within conduit 126. When the wavelength of the standing wave propagating at the resonance frequency is known, the length of conduit 126 can be determined from λ = 2L C which is the reciprocal of that frequency. Thus, the length of conduit 126 is proportional to the wavelength of the standing wave propagating at the resonance frequency and inversely proportional to the resonance frequency.

[0195] In some implementations, a peak different from peak 2422 may be selected as the peak representing the resonance frequency of conduit 126. For example, due to the presence of breathing apparatus 122 and / or user interface 124, the intensity of a standing wave propagating at a frequency other than the resonance frequency may actually appear larger than the peak representing the resonance frequency of the standing wave. In these cases, the lowest frequency peak of the wave period intensity signal 2420 may be selected as the peak representing the resonance frequency of conduit 126.

[0196] By performing a Fourier transform on the initial time domain intensity signal and the frequency domain intensity signal 2400, along with any preprocessing steps, generally the noise in the data can be minimized to more easily identify the resonance frequency of the conduit. However, in some implementations, the resonance frequency is identified from the frequency domain intensity signal 2400 rather than the wave period intensity signal This can be done. For example, after identifying a series of peaks 2410 in the frequency domain intensity signal 2400, the resonance frequency may be directly selected from these series of peaks 2410. A peak having the lowest frequency among the series of peaks 2410 may be selected as the peak representing the resonance frequency of the conduit 126.

[0197] In some implementations, before obtaining the wave period intensity signal 2420, the frequency domain intensity signal 2400 itself can be windowed. In these implementations, a portion of the frequency domain intensity signal expected to include the resonance frequency of the conduit 126 is selected, and the wave period intensity signal 2420 is obtained only from this selected portion. By identifying the portion of the frequency domain intensity signal expected to include the resonance frequency, additional portions of the frequency domain intensity signal are removed, reducing the complexity in analyzing the acoustic data. The portion of the frequency domain intensity signal that includes the resonance frequency can be selected based on various factors. In some implementations, a predetermined estimated value of the length of the conduit 126 is used to provide a frequency range within which the resonance frequency is expected to fall. For example, if the conduit 126 is generally known to be between 1.5 meters and 2.0 meters, these length estimates can be used to estimate the portion of the frequency domain intensity signal where the resonance frequency is expected to be. Additionally or alternatively, the estimation can be based on the speed of sound in the conduit 126, the sampling rate of the acoustic data, known conditions both inside and outside the conduit 126 (such as temperature or humidity that may affect the speed of sound in the conduit 126), and those skilled in the art can use other factors to estimate the portion of the frequency domain intensity signal where the resonance frequency exists.

[0198] In some implementations, instead of applying a Fourier transform to the frequency domain intensity signal to obtain a wave period intensity signal, other transforms and / or analyses can be applied. For example, an inverse Fourier transform can be applied to the frequency domain intensity signal 2400 to obtain a new time domain intensity signal, and the resonance frequency can be identified from the new time domain intensity signal. In another example, cross-correlation analysis can be applied to the initial time domain intensity signal and the frequency domain intensity signal 2400 to determine the resonance frequency of the conduit 126.

[0199] In some implementations, various different preprocessing steps can be applied to the frequency domain intensity signal or the wave period intensity signal. In one implementation, the preprocessing includes trend removal that can remove variations in the frequency domain intensity signal that are not related to the length of the conduit 126. For example, the acoustic data can include artifacts (such as peaks, dips, etc.) due to physical characteristics of components other than the conduit 126, external noise and / or disturbances, data noise, and the like. In additional or alternative implementations, the preprocessing includes spectral windowing. Spectral windowing can be used to improve the quality of the frequency domain intensity signal that is the Fourier transform of the time domain intensity signal. In some cases, applying a fast Fourier transform algorithm to the time domain intensity signal can result in various artifacts and other errors in the frequency domain intensity signal. For example, if the endpoints of the time domain intensity signal are discontinuous, the frequency domain intensity signal can be slightly different from the actual frequency domain version of the time domain intensity signal. Since spectral windowing can be used to correct the discontinuity of the endpoints of the time domain intensity signal, the frequency domain intensity signal can more accurately represent the actual frequency spectrum of the reflection of the acoustic signal.

[0200] Thus, when windowing the acoustic data generated in step 404 of method 400, the windowing of the generated acoustic data can be at least partially based on the length of conduit 126 determined according to FIGS. 24A and 24B. By determining the length of conduit 126, an approximate position on the x-axis of the cepstrum can be identified where conduit 126 ends and user interface 124 begins. This position can then be used to define (e.g., can be used for windowing) an analysis window of the acoustic data. Next, the windowed acoustic data can be used to characterize user interface 124 as described herein. In some implementations, these techniques can be used to first estimate the length of conduit 126 to provide an estimated position of user interface 124. One or more reference points can then be identified based on the estimated position of user interface 124, and further analysis of user interface 124 and / or conduit 126 can be performed. As described herein, reference points can be identified and user interface 124 and / or conduit 126 can be analyzed. Additionally, the determined length of conduit 126 can also be used as part of any acoustic signature useful in characterizing and / or classifying user interface 124. In further implementations, the length of conduit 126 can be estimated using techniques other than those described herein with respect to FIGS. 24A and 24B. In this way, user interface 124 can be characterized. In some implementations, these techniques can be used to first estimate the length of conduit 126 to provide an estimated position of user interface 124. One or more reference points can then be identified based on the estimated position of user interface 124, and further analysis of user interface 124 and / or conduit 126 can be performed. As described herein, reference points can be identified and user interface 124 and / or conduit 126 can be analyzed. Additionally, the determined length of conduit 126 can also be used as part of any acoustic signature useful in characterizing and / or classifying user interface 124. In further implementations, the length of conduit 126 can be estimated using techniques other than those described herein with respect to FIGS. 24A and 24B.

[0201] Referring now to FIGS. 25A and 25B, the determined length of conduit 126 can also be used to adjust the characterization of user interface 124 based on acoustic data. As described herein, when analyzing user interface 124, identifying features of user interface 124, and characterizing and / or classifying user interface 124, the length of conduit 126 is assumed. However, conduit 126 can have a length different from what is expected, for example, due to manufacturing tolerances, different designs, stretching, damage, etc. Further, the apparent effective length of conduit 126 can vary depending on the amount of time it takes for sound to pass through conduit 126. This amount of time can vary based on air temperature, humidity, the position / orientation of conduit 126, etc. If conduit 126 has a length (actual or apparent) different from what is expected, the acoustic signature identified using the techniques described herein can be unexpectedly distorted and cannot be associated with user interface 124 that was tested / used by the user. Thus, an unexpected length of conduit 126 can potentially reduce the accuracy of the characterization and / or classification of user interface 124, and various aspects related to the characterization and / or classification of user interface 124 may need to be adjusted based on the length of conduit 126.

[0202] FIG. 25A shows an upper plot of cepstrum 2500A and a lower plot of cepstrum 2500B. Cepstrum 2500A is determined from acoustic data generated by user interface 124 and a first type of conduit 126. Cepstrum 2500B is determined from acoustic data generated by the same user interface 124 from a second type of conduit 126. For example, the first type of conduit 126 can be a ResMed® SlimLine® conduit, and the second type of conduit 126 can be a ResMed® ClimateLine® conduit. The first and second types of conduit 126 have different lengths.

[0203] The cepstrums 2500A and 2500B are shown with cephlency on the X-axis and amplitude on the Y-axis. In these implementations, the cephlency corresponds to a measure of the distance along the path through which the acoustic signal propagates. Thus, the cepstrums 2500A and 2500B are used to indicate this distance along the path related to the reflection of the acoustic signal. These reflections are successively associated with various features of the user interface. Thus, the cepstrums 2500A and 2500B can be used to characterize and / or classify the respective user interface 124. The illustrated implementation shows using the cepstrum to characterize and / or classify the user interface 124, but the analysis of acoustic data can generally include performing deconvolution of the acoustic data. Then, the deconvolution of the acoustic data can be used to characterize and / or classify the user interface 124. The cepstrum is a particular type of deconvolution that is available.

[0204] The two cepstrums 2500A and 2500B are for the same user interface 124 Since it is determined from the acoustic data generated using them, the two cepstrums 2500A and 2500B should generally have the same pattern. However, if the length of the conduit 126 is different, the cepstrum 2500B is distorted. As shown in FIG. 25A, the cepstrum 2500B is stretched or unclear relative to the cepstrum 2500A. For example, the cepstrum 2500B includes a peak 2502B that is located forward relative to the corresponding peak 2502A of the cepstrum 2500A due to the stretching or unclarity of the cepstrum 2500B. The distortion of the cepstrum 2500B due to the different length of the conduit may also affect the position of the peak along the Y-axis. If the cepstrum 2500B is stretched relative to what is expected from the expected length of the conduit 126 used to obtain it, the peak can be shorter along the Y-axis than otherwise. Conversely, if the length of the conduit 126 is shorter than expected, the cepstrum 2500B is more compressed than normal, and the peak may be higher along the Y-axis than otherwise. Therefore, the characteristics of the user interface 124 obtained from the cepstrum 2500B are different from the characteristics of the user interface 124 obtained from the cepstrum 2500A, even if both cepstrums are generated using the same user interface 124. Thus, as shown in FIG. 25A, the length of the conduit 126 can have an adverse effect on the characterization of the user interface 124.

[0205] Figure 25B shows the upper plot of the corrected cepstrum 2504A and the lower plot of the modified cepstrum 2504B. Cepstrum 2504A is a modified version of cepstrum 2500A and includes peak 2506A. Cepstrum 2504B is a modified version of cepstrum 2500B and includes peak 2506B. Cepstra 2504A and 2504B are corrected by scaling cepstra 2500A and 2500B by a factor equal to the ratio of (i) the measured length of each conduit 126 to (ii) the assumed length of conduit 126. For example, if it is assumed that the length of a particular conduit is 2 meters and analysis of the conduit reveals that the conduit is actually 1.8 meters long, the new position along the X-axis of each data point of the cepstrum is the old position along the X-axis multiplied by 1.8 / 2. As shown in Figure 25B, peak 2506A of corrected cepstrum 2504A is aligned with peak 2506B of corrected cepstrum 2504B, which is expected when the actual lengths of conduits 126 are the same.

[0206] In the illustrated implementation, both cepstra 2500A and 2500B are scaled to the same length to remove the stretching effect caused by conduits of unknown length. When two conduits 126 and their respective user interfaces 124 are being compared, scaling them to the same assumed length allows for easier comparison. However, cepstra generated from acoustic data can generally be scaled to any length. For example, in some implementations, two conduits 126 assumed to have two different lengths can be scaled to their respective assumed lengths even though their lengths are different. Generally, cepstra can be scaled to an assumed length to remove distortion caused by the actual length of conduit 126. As described herein, this distortion can include the cepstrum being stretched and the cepstrum being compressed.

[0207] Thus, the acoustic data used to characterize and / or classify the conduit 126 can be stretched and / or compressed to compensate for differences in the effective length of the conduit 126 so that the length of the conduit 126 does not distort the characterization and / or classification of the user interface 124. This scaling of the acoustic data (e.g., stretching and / or compressing) can be performed in the time domain (time signal), frequency domain (spectrum), cepstrum domain (cepstrum), other domains, or any combination of domains. The scaling can be performed on the acoustic data itself before an operation or transformation is performed on the acoustic data. The scaling can be performed using various signal processing methods such as interpolation, upsampling, phase vocoding techniques (which enable control of the frequency shift applied to the signal). Thus, the scaling can be used to aid in the characterization of the user interface 124 as described herein with reference to method 400 and FIG. 6, and can also be used to aid in the classification of the user interface 124 as described herein with reference to method 1200 and FIGS. 14A and 14B. It can also be performed on the direct transformation of the spectrum and / or cepstrum, the logarithm of the spectrum and / or cepstrum, the inverse transformation of the spectrum and / or cepstrum, the inverse transformation of the logarithm of the spectrum and / or cepstrum, or other data sets.

[0208] Additional details regarding the determination and / or identification of the length of the conduit 126 can be found in U.S. Provisional Application No. 63 / 107,763, filed Oct. 30, 2020, which is hereby incorporated by reference in its entirety.

[0209] Although the various embodiments of the present invention have been described as above, it should be understood that these are not limiting and are merely illustrative. Without departing from the spirit or scope of the present invention, numerous modifications can be made to the disclosed embodiments in accordance with the disclosure herein. Therefore, the scope of the present invention should not be limited by any of the above embodiments. Rather, the scope of the present invention should be defined in accordance with the claims and their equivalents.

[0210] One or more elements or aspects or steps or portions (singular or plural) from any one or more of the following claims 1 to 126 can be combined with one or more elements or aspects or steps or portions (singular or plural) from any one or more of the other claims 1 to 126 or combinations thereof to form one or more further implementations and / or claims of the present disclosure.

[0211] The terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the present invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. Further, as long as the terms "including", "includes", "having", "has", "with", or their variants are used in either the detailed description and / or the claims, such terms are inclusive in the same manner as the term "comprising".

[0212] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. Further, terms defined as in a commonly used dictionary should be interpreted as having a meaning that coincides with the meaning in the context of the relevant art and should not be interpreted in an idealized or overly formal sense unless explicitly so defined.

[0213] [[Cross - reference to related applications]] This application claims the benefit and priority of U.S. Provisional Patent Application No. 63 / 108,161, filed on October 30, 2020, and U.S. Provisional Patent Application No. 63 / 036,303, filed on June 8, 2020, the entire contents of each of which are incorporated herein by reference. (Appendix) (Appendix 1) Generating acoustic data associated with an acoustic reflection of an acoustic signal, wherein the acoustic reflection at least partially represents one or more features of a user interface coupled to a respiratory therapy device via a conduit; Analyzing the generated acoustic data, including windowing the generated acoustic data based at least in part on at least one of the one or more features of the user interface; Characterizing the user interface based at least in part on the analyzed acoustic data. A method comprising the steps of: (Appendix 2) Windowing the generated acoustic data as described above includes determining a reference point within the generated acoustic data. The method according to Appendix 1. (Appendix 3) The reference point is the minimum point within a predetermined section of the deconvolution of the generated acoustic data. The method according to Appendix 2. (Appendix 4) The reference point is the maximum point within a predetermined section of the deconvolution of the generated acoustic data. The method according to Appendix 2. (Appendix 5) The reference point corresponds to the position of the one or more features along a path at least partially formed by the conduit and the user interface. The method according to any one of Appendices 2 to 4. (Appendix 6) The method according to Appendix 5, wherein the one or more features cause a change in acoustic impedance. (Appendix 7) The change in the acoustic impedance is at least partially based on a narrowing of the path at (i) the user interface, (ii) the junction of the conduit and the user interface, or (iii) a combination thereof, according to the method described in Appendix 6. (Appendix 8) The change in the acoustic impedance is at least partially based on a widening of the path at (i) the user interface, (ii) the junction of the conduit and the user interface, or (iii) a combination thereof, according to the method described in Appendix 6. (Appendix 9) The windowing includes a first windowing process of the generated acoustic data and a second windowing process of the generated acoustic data, according to the method described in any one of Appendices 1 to 8. (Appendix 10) The first window process and the second window process are different depending on the amount of the generated acoustic data selected before the reference point in the generated acoustic data, after the reference point in the generated acoustic data, or a combination thereof, according to the method described in Appendix 9. (Appendix 11) Analyzing the generated acoustic data described above includes calculating the deconvolution of the generated acoustic data before the windowing process of the generated acoustic data, according to the method described in any one of Appendices 1 to 10. (Appendix 12) The deconvolution of the generated acoustic data includes calculating the cepstrum of the generated acoustic data, according to the method described in Appendix 11. (Appendix 13) The cepstrum identifies a distance indicating the position of one or more features of the user interface associated with acoustic reflections, according to the method described in Appendix 12. (Appendix 14) Analyzing the generated acoustic data described above includes calculating the derivative of the cepstrum to determine the rate of change of the cepstrum signal, according to the method described in Appendix 12 or 13. (Appendix 15) Analyzing the generated acoustic data described above includes normalizing the generated acoustic data, and the method according to any one of Appendices 1 to 14. (Appendix 16) Normalizing the generated acoustic data described above includes subtracting the mean value from the generated acoustic data, dividing by the standard deviation of the generated acoustic data, or a combination thereof, and the method according to Appendix 15. (Appendix 17) Normalizing the generated acoustic data described above is the method according to Appendix 16 that explains the crosstalk conditions. (Appendix 18) The crosstalk conditions are caused by microphone gain, respiratory amplitude, treatment pressure, or a combination thereof, and the method according to Appendix 17. (Appendix 19) Characterizing the user interface described above includes inputting the analyzed acoustic data into a convolutional neural network to determine the form factor of the user interface, the model of the user interface, the size of one or more elements of the user interface, or a combination thereof, and the method according to any one of Appendices 1 to 18. (Appendix 20) The convolutional neural network includes one or more convolutional layers and one or more max-pooling layers, and the method according to Appendix 19. (Appendix 21) The convolutional neural network includes N features having max-pooling of M samples of N features, and the ratio of N to M is from 1:1 to 4:1, and the method according to Appendix 20. (Appendix 22) The method according to any one of Appendices 1 to 21 further includes emitting the acoustic signal to the conduit connected to the user interface via an acoustic transducer. (Appendix 23) The method according to any one of appendices 1 to 22, further comprising the step of emitting the acoustic signal into the conduit via a motor of the respiratory therapy device connected to the conduit. (Appendix 24) The method according to any one of appendices 1 to 23, wherein the acoustic signal is a sound audible to humans. (Appendix 25) The method according to any one of appendices 1 to 23, wherein the acoustic signal is a sound inaudible to humans. (Appendix 26) The method according to appendix 25, wherein whether the sound is audible is determined based on the frequency of the sound, the amplitude of the sound, or a combination thereof. (Appendix 27) The method according to appendix 26, wherein the acoustic signal is an ultrasonic wave. (Appendix 28) The method according to any one of appendices 1 to 27, wherein the user interface is not connected to the user during the generation of the acoustic data. (Appendix 29) The method according to any one of appendices 1 to 27, wherein the user interface is connected to the user during the generation of the acoustic data. (Appendix 30) The method according to any one of appendices 1 to 29, further comprising the step of providing a flow of pressurized air that enters the user interface through the conduit during the generation of the acoustic data. (Appendix 31) The method according to any one of appendices 1 to 29, further comprising the step of emitting the acoustic signal into the conduit while there is no flow of pressurized air entering the user interface through the conduit. (Appendix 32) The method according to any one of appendices 1 to 31, wherein the generated acoustic data is generated from a plurality of acoustic reflections from a plurality of acoustic signals. (Appendix 33) The method according to appendix 32, wherein the generated acoustic data is an average of the plurality of acoustic reflections from the plurality of acoustic signals. (Appendix 34) Analyzing the generated acoustic data described above includes identifying one or more signatures that correlate with the one or more features of the user interface, and the method further includes, at least in part, classifying the user interface based on the one or more signatures, the method according to any one of appendices 1 to 33. (Appendix 35) The user interface is characterized from a subset of the user interface determined at least in part based on the category of the user interface, the method according to appendix 34. (Appendix 36) The method according to appendix 34 or appendix 35 further includes verifying the characterized user interface based at least in part on the characterized user interface that satisfies the category of the user interface. (Appendix 37) The method further includes determining a confidence score that the user interface is correctly characterized, where verifying the characterized user interface described above is performed when the confidence score satisfies a confidence threshold, the method according to appendix 36. (Appendix 38) A control system including one or more processors, A system including a memory storing machine-readable instructions, wherein The control system is coupled to the memory, and when the machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system, the method according to any one of appendices 1 to 37 is implemented. (Appendix 39) A system for promoting a desired sleep stage of a user, comprising a control system configured to implement the method according to any one of appendices 1 to 37. (Appendix 40) When executed by a computer, any one of appendices 1 to 37 to the computer A computer program product including instructions for causing the method according to claim 1 to be performed. (Appendix 41) The computer program product is the computer program product according to Appendix 40, which is a non-transitory computer-readable medium. (Appendix 42) A memory storing machine-readable instructions, and A step of generating acoustic data associated with an acoustic reflection of an acoustic signal, wherein the acoustic reflection at least partially indicates one or more characteristics of a user interface coupled to a respiratory therapy device via a conduit, and A step of analyzing the generated acoustic data, including windowing the generated acoustic data based at least in part on at least one of the one or more characteristics of the user interface, and A control system including one or more processors configured to execute the machine-readable instructions to perform a step of characterizing the user interface based at least in part on the analyzed acoustic data. (Appendix 43) The windowing the generated acoustic data as described above includes determining a reference point within the generated acoustic data, for the system according to Appendix 42. (Appendix 44) The reference point is the minimum point within a predetermined section of the deconvolution of the generated acoustic data, for the system according to Appendix 43. (Appendix 45) The reference point is the maximum point within a predetermined section of the deconvolution of the generated acoustic data, for the system according to Appendix 43. (Appendix 46) The reference point corresponds to the position of the one or more characteristics along a path at least partially formed by the conduit and the user interface, for the system according to any one of Appendices 43 to 45. (Appendix 47) The system according to appendix 46, wherein the one or more features cause a change in acoustic impedance. (Appendix 48) The system according to appendix 47, wherein the change in acoustic impedance is at least partially based on a narrowing of the path at (i) the user interface, (ii) the junction of the conduit and the user interface, or (iii) a combination thereof. (Appendix 49) The system according to appendix 47, wherein the change in acoustic impedance is at least partially based on an expansion of the path at (i) the user interface, (ii) the junction of the conduit and the user interface, or (iii) a combination thereof. (Appendix 50) The system according to any one of appendices 42 to 49, wherein the window processing includes a first window processing of the generated acoustic data and a second window processing of the generated acoustic data. (Appendix 51) The system according to appendix 50, wherein the first window processing and the second window processing differ by an amount of the generated acoustic data selected before a reference point in the generated acoustic data, after the reference point in the generated acoustic data, or a combination thereof. (Appendix 52) Analyzing the generated acoustic data as described above is The system according to any one of appendices 42 to 51, including calculating a deconvolution of the generated acoustic data before the window processing of the generated acoustic data. (Appendix 53) The system according to appendix 52, wherein the deconvolution of the generated acoustic data includes calculating a cepstrum of the generated acoustic data. (Appendix 54) The system according to appendix 53, wherein the cepstrum identifies a distance indicating the position of one or more features of the user interface associated with acoustic reflections. (Appendix 55) Analyzing the generated acoustic data described above includes calculating the derivative of the cepstrum to determine the rate of change of the cepstrum signal, the system according to appended note 53 or 54. (Appended note 56) Analyzing the generated acoustic data described above includes normalizing the generated acoustic data, the system according to any one of appended notes 42 to 55. (Appended note 57) Normalizing the generated acoustic data described above includes subtracting the mean value from the generated acoustic data, dividing by the standard deviation of the generated acoustic data, or a combination thereof, the system according to appended note 56. (Appended note 58) Normalizing the generated acoustic data described above explains the crosstalk condition, the system according to appended note 57. (Appended note 59) The crosstalk condition is caused by microphone gain, respiratory amplitude, treatment pressure, or a combination thereof, the system according to appended note 58. (Appended note 60) Characterizing the user interface described above includes inputting the analyzed acoustic data into a convolutional neural network to determine the form factor of the user interface, the model of the user interface, the size of one or more elements of the user interface, or a combination thereof, the system according to any one of appended notes 42 to 59. (Appended note 61) The convolutional neural network includes one or more convolutional layers and one or more max pooling layers, the system according to appended note 60. (Appended note 62) The convolutional neural network includes N features having max pooling of M samples of N features, and the ratio of N to M is from 1:1 to 4:1, the system according to appended note 61. (Appended note 63) The one or more processors are further configured to execute the machine-readable instructions to emit the acoustic signal into the conduit connected to the user interface via an acoustic transducer, the system according to any one of appendices 42 to 62. (Appendix 64) The one or more processors are further configured to execute the machine-readable instructions to emit the acoustic signal into the conduit via a motor of the respiratory therapy device connected to the conduit, the system according to any one of appendices 42 to 63. (Appendix 65) The acoustic signal is a sound audible to humans, the system according to any one of appendices 42 to 64. (Appendix 66) The acoustic single is a sound inaudible to humans, the system according to any one of appendices 42 to 64. (Appendix 67) Whether the sound is audible is determined based on the frequency of the sound, the amplitude of the sound, or a combination thereof, the system according to appendix 66. (Appendix 68) The acoustic signal is an ultrasonic sound, the system according to appendix 67. (Appendix 69) The user interface is not connected to the user during the generation of the acoustic data, the system according to any one of appendices 42 to 68. (Appendix 70) The user interface is connected to the user during the generation of the acoustic data, the system according to any one of appendices 42 to 68. (Appendix 71) The one or more processors are further configured to execute the machine-readable instructions to generate the acoustic data while providing a flow of pressurized air into the user interface via the conduit, the system according to any one of appendices 42 to 70. (Appendix 72) The one or more processors are further configured to execute the machine-readable instructions to emit the acoustic signal in the conduit while there is no flow of pressurized air entering the user interface through the conduit, the system according to any one of appendices 42 to 70. (Appendix 73) The generated acoustic data is generated from a plurality of acoustic reflections from a plurality of acoustic signals, the system according to any one of appendices 42 to 72. (Appendix 74) The generated acoustic data is an average of the plurality of acoustic reflections from the plurality of acoustic signals, the system according to appendix 73. (Appendix 75) Analyzing the generated acoustic data described above includes identifying one or more signatures that correlate with the one or more features of the user interface, and the method further includes, at least in part, classifying the user interface based on the one or more signatures, the system according to any one of appendices 42 to 74. (Appendix 76) The user interface is characterized from a subset of the user interface determined at least in part based on the category of the user interface, the system according to appendix 75. (Appendix 77) The method further includes verifying the characterized user interface at least in part based on the characterized user interface that satisfies the category of the user interface, the system according to appendix 75 or appendix 76. (Appendix 78) The method further includes determining a confidence score that the user interface is correctly characterized, wherein verifying the characterized user interface described above is performed when the confidence score meets a confidence threshold, the system according to appendix 77. (Appendix 79) A step of generating acoustic data associated with an acoustic reflection of an acoustic signal, wherein the acoustic reflection at least partially indicates one or more features of a user interface coupled to a respiratory therapy device via a conduit a step of analyzing the generated acoustic data to identify one or more signatures correlated with the one or more functions of the user interface; and a step of classifying the user interface based at least in part on the one or more signatures. A method comprising: (Appendix 80) The method according to Appendix 79, wherein the category of the user interface is associated with a direct connection between the conduit and the cushion of the user interface, between the conduit and the frame of the user interface, or between the conduit and both the cushion and the frame. (Appendix 81) The method according to Appendix 80, wherein the one or more features and the one or more signatures indicate the direct connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame. (Appendix 82) The method according to Appendix 79, wherein the category of the user interface is associated with an indirect connection between the conduit and the cushion of the user interface, between the conduit and the frame of the user interface, or between the conduit and both the cushion and the frame. (Appendix 83) The method according to Appendix 82, wherein the one or more features and the one or more signatures indicate the indirect connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame. (Appendix 84) The method according to Appendix 82 or 83, wherein the indirect connection is characterized by a further conduit arranged and configured to provide a fluid connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame. (Appendix 85) The further conduit is a headgear conduit, the headgear conduit forms part of a headgear for holding the user interface on the user's face, and the headgear conduit is configured to send pressurized air from the conduit to the cushion, from the conduit to the frame, or from the conduit to both the cushion and the frame, the method according to appended claim 84. (Appended claim 86) The further conduit is a user interface conduit that is (i) more flexible than the conduit, (ii) has a diameter smaller than the diameter of the conduit, or both (i) and (ii), and the user interface conduit is configured to send pressurized air from the conduit to the cushion, from the conduit to the frame, or from the conduit to both the cushion and the frame, the method according to appended claim 84. (Appended claim 87) Analyzing the generated acoustic data described above includes calculating the spectrum of the generated acoustic data, the method according to any one of appended claims 79 to 86. (Appended claim 88) Analyzing the generated acoustic data described above includes calculating the logarithm of the spectrum, the method according to appended claim 87. (Appended claim 89) Analyzing the generated acoustic data described above includes calculating the cepstrum of the logarithmic spectrum, the method according to appended claim 88. (Appended claim 90) Analyzing the generated acoustic data described above is selecting a segment of the log spectrum of the generated acoustic data, calculating the Fourier transform of the segment of the log spectrum, and associating the one or more features with the one or more signatures within the Fourier transform of the segment, the method according to any one of appended claims 79 to 89. (Appended claim 91) The one or more signatures include the maximum amplitude of the cepstrum, the minimum amplitude of the cepstrum, the standard deviation of the cepstrum, the skewness of the cepstrum, the kurtosis of the cepstrum, the median of the absolute value of the cepstrum, the sum of the absolute values of the cepstrum, the sum of the positive areas of the cepstrum, the sum of the negative areas of the cepstrum, the fundamental frequency, the energy corresponding to the fundamental frequency, the average energy, at least one resonance frequency of the combination of the conduit and the user interface, the change in the at least one resonance frequency, the number of peaks within a certain range, peak prominences, the distance between peaks, or a combination or variation thereof, according to the method of any one of Appendices 79 to 90. (Appendix 92) The generated acoustic data includes a primary reflection within the acoustic reflection of the acoustic signal, according to the method of any one of Appendices 79 to 91. (Appendix 93) The generated acoustic data includes a secondary reflection within the acoustic reflection of the acoustic signal, according to the method of any one of Appendices 79 to 92. (Appendix 94) The generated acoustic data includes a tertiary reflection within the acoustic reflection of the acoustic signal, according to the method of any one of Appendices 79 to 93. (Appendix 95) The generated acoustic data includes a primary reflection and a secondary reflection within the acoustic reflection of the acoustic signal, according to the method of any one of Appendices 79 to 91. (Appendix 96) The category of the user interface related to the direct connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame is classified based on one or more signatures having a maximum amplitude of the cepstrum greater than a threshold value, according to the method described in Appendix 80 or 81. (Appendix 97) The category of the user interface related to the indirect connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame via the user interface conduit is classified based on the one or more signatures that meet a threshold number of peaks, the method described in Appendix 86. (Appendix 98) The category of the user interface related to the indirect connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame via the user interface conduit is classified based on the one or more signatures having a maximum cepstrum amplitude below a threshold, the method described in Appendix 86. (Appendix 99) The category of the user interface related to the indirect connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame via the headgear conduit is classified based on the one or more signatures having an average cepstrum value below a threshold, the method described in Appendix 85. (Appendix 100) Classifying the user interface based at least in part on the one or more signatures described above includes comparing the one or more signatures with one or more known signatures of one or more user interfaces of known categories, Appendix 79 The method according to any one of claims 79 to 99. (Appendix 101) A control system including one or more processors, A memory storing machine-readable instructions, a system comprising: The control system is coupled to the memory, and when the machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system, the method according to any one of Appendices 79 to 100 is implemented. (Appendix 102) A system for promoting a desired sleep stage of a user, comprising a control system configured to implement the method according to any one of Appendices 79 to 100. (Appendix 103) A computer program product including instructions that, when executed by a computer, cause the computer to implement the method according to any one of Appendices 79 to 100. (Appendix 104) The computer program product according to Appendix 103, which is a non-transitory computer-readable medium. (Appendix 105) A memory storing machine-readable instructions, A step of generating acoustic data associated with an acoustic reflection of an acoustic signal, the acoustic reflection at least partially indicating one or more features of a user interface coupled to a respiratory therapy device via a conduit, A step of analyzing the generated acoustic data to identify one or more signatures correlated with the one or more features of the user interface, A control system having one or more processors configured to execute the machine-readable instructions to perform a step of classifying the user interface based at least in part on the one or more signatures. (Appendix 106) The system according to Appendix 105, wherein the category of the user interface is associated with a direct connection between the conduit and a cushion of the user interface, between the conduit and a frame of the user interface, or between the conduit and both the cushion and the frame. (Appendix 107) The system according to Appendix 106, wherein the one or more features and the one or more signatures indicate the direct connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame. (Appendix 108) The category of the user interface is the system according to Appendix 105, which is associated with an indirect connection between the conduit and the cushion of the user interface, between the conduit and the frame of the user interface, or between the conduit and both the cushion and the frame. (Appendix 109) The one or more features and the one or more signatures are the system according to Appendix 108, which indicate the indirect connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame. (Appendix 110) The indirect connection is the system according to Appendix 108 or 109, which is characterized by a further conduit arranged and configured to provide a fluid connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame. (Appendix 111) The further conduit is a headgear conduit, the headgear conduit forms part of a headgear for holding the user interface on the user's face, and the headgear conduit is configured to send pressurized air from the conduit to the cushion, from the conduit to the frame, or from the conduit to both the cushion and the frame. This is the system according to Appendix 110. (Appendix 112) The further conduit is a user interface conduit that is (i) more flexible than the conduit, (ii) has a diameter smaller than the diameter of the conduit, or has both (i) and (ii). The user interface conduit is configured to send pressurized air from the conduit to the cushion, from the conduit to the frame, or from the conduit to both the cushion and the frame. This is the system according to Appendix 110. (Appendix 113) Analyzing the generated acoustic data described above includes calculating the spectrum of the generated acoustic data. This is the system according to any one of Appendices 105 to 112. (Appendix 114) Analyzing the generated acoustic data described above includes calculating the logarithm of the spectrum, and the system according to Appendix 113. (Appendix 115) Analyzing the generated acoustic data described above includes calculating the cepstrum of the logarithmic spectrum, and the system according to Appendix 114. (Appendix 116) Analyzing the generated acoustic data described above includes selecting a segment of the log spectrum of the generated acoustic data, calculating the Fourier transform of the segment of the log spectrum, and associating the one or more features with the one or more signatures within the Fourier transform of the segment, and the method according to any one of Appendices 105 to 115. (Appendix 117) The one or more signatures include the maximum amplitude of the cepstrum, the minimum amplitude of the cepstrum, the standard deviation of the cepstrum, the skewness of the cepstrum, the kurtosis of the cepstrum, the median of the absolute value of the cepstrum, the sum of the absolute values of the cepstrum, the sum of the positive areas of the cepstrum, the sum of the negative areas of the cepstrum, the fundamental frequency, the energy corresponding to the fundamental frequency, the average energy, at least one resonance frequency of the combination of the conduit and the user interface, the change of the at least one resonance frequency, the number of peaks within a certain range, peak protrusions, the distance between peaks, or a combination or change thereof, and the system according to any one of Appendices 105 to 116. (Appendix 118) The generated acoustic data includes a primary reflection within the acoustic reflection of the acoustic signal, and the system according to any one of Appendices 105 to 117. (Appendix 119) The generated acoustic data includes a secondary reflection within the acoustic reflection of the acoustic signal, and the system according to any one of Appendices 108 to 118. (Appendix 120) The generated acoustic data includes third-order reflections within the acoustic reflections of the acoustic signal, and the system according to any one of Appendices 105 to 119. (Appendix 121) The generated acoustic data includes first-order and second-order reflections within the acoustic reflections of the acoustic signal, and the system according to any one of Appendices 105 to 117. (Appendix 122) The category of the user interface related to the direct connection between the conduit and the cushion, between the conduit and the frame of the user interface, or between the conduit and both the cushion and the frame is classified based on the one or more signatures having a maximum cepstrum amplitude greater than a threshold value, and the system according to Appendix 105 or 121. (Appendix 123) The category of the user interface related to the indirect connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame via the user interface conduit is classified based on the one or more signatures satisfying a threshold number of peaks, and the system according to Appendix 112. (Appendix 124) The category of the user interface related to the indirect connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame via the user interface conduit is classified based on the one or more signatures having a maximum cepstrum amplitude less than a threshold value, and the system according to Appendix 112. (Appendix 125) The category of the user interface related to the indirect connection between the conduit and the cushion, between the conduit and the frame, or between the conduit and both the cushion and the frame via the headgear conduit is classified based on the one or more signatures having an average cepstrum value below a threshold value, and the system according to Appendix 111. (Appendix 126) Classifying the user interface based at least in part on the one or more signatures described above includes comparing the one or more signatures to one or more known signatures of one or more user interfaces of a known category, the system according to any one of appendices 105 to 125.

Claims

1. receiving generated acoustic data associated with acoustic reflections of the acoustic signal; the acoustic reflections at least partially indicative of one or more characteristics of at least one of a user interface and a conduit; the user interface is coupled to a respiratory treatment device via the conduit; Analyzing the generated acoustic data; The analyzing includes windowing the generated acoustic data based at least in part on at least one characteristic of the one or more characteristics of at least one of the user interface and the conduit; the at least one characteristic includes a known length of the conduit; characterizing the user interface based at least in part on the windowed and analyzed acoustic data; method.

2. and windowing the generated acoustic data includes determining a reference point within the generated acoustic data. The method of claim 1.

3. The reference point is a minimum point within a predetermined interval of deconvolution of the generated acoustic data. The method of claim 2.

4. The reference point is a maximum point within a predetermined interval of deconvolution of the generated acoustic data. The method of claim 2.

5. the reference points correspond to positions of the one or more features along a path defined at least in part by the conduit and the user interface. The method according to any one of claims 2 to 4.

6. the one or more features cause a change in acoustic impedance; The method according to claim 5.

7. the change in acoustic impedance is due, at least in part, to a narrowing of the pathway at (i) the user interface, (ii) a junction of the conduit and the user interface, or (iii) a combination thereof; or the change in acoustic impedance is due, at least in part, to a widening of the path through (i) the user interface, (ii) a junction of the conduit and the user interface, or (iii) a combination thereof; The method according to claim 6.

8. the windowing includes a first windowing of the generated acoustic data and a second windowing of the generated acoustic data; the first windowing and the second windowing vary depending on an amount of the generated acoustic data selected before a reference point in the generated acoustic data, after the reference point in the generated acoustic data, or a combination thereof. The method according to any one of claims 1 to 7.

9. Analysing the generated acoustic data includes computing a deconvolution of the generated acoustic data prior to the windowing of the generated acoustic data; and wherein the deconvolving of the generated acoustic data includes calculating a cepstrum of the generated acoustic data. The method according to any one of claims 1 to 8.

10. The cepstrum identifies a distance associated with the acoustic reflection; the distance being indicative of a location of the one or more features of the user interface.

10. The method of claim 9.

11. analyzing the generated acoustic data includes calculating a derivative of the cepstrum to determine a rate of change of a cepstral signal.

10. The method of claim 9.

12. and analyzing the generated acoustic data includes normalizing the generated acoustic data. The method according to any one of claims 1 to 11.

13. Normalizing the generated acoustic data includes subtracting a mean from the generated acoustic data, dividing by a standard deviation of the generated acoustic data, or a combination thereof. The method of claim 12.

14. The method of claim 13 , wherein normalizing the generated acoustic data accounts for confounding terms.

15. The confounding conditions may be due to microphone gain, breathing amplitude, treatment pressure, or a combination thereof. The method of claim 14.

16. characterizing the user interface includes inputting the analyzed acoustic data into a convolutional neural network to determine a form factor of the user interface, a model of the user interface, a size of one or more elements of the user interface, or a combination thereof. The method according to any one of claims 1 to 15.

17. the acoustic signal is emitted to the conduit connected to the user interface via an acoustic transducer or via a motor of the respiratory treatment device connected to the conduit; The method according to any one of claims 1 to 16.

18. The method of any one of claims 1 to 17, further comprising emitting the acoustic signal to the conduit via a motor of the respiratory treatment device connected to the conduit.

19. the acoustic signal is a non-audible sound based on the frequency of the sound, the amplitude of the sound, or a combination thereof; or the acoustic signal is an ultrasonic sound; 20. The method of claim 18.

20. the user interface is not connected to a user during generation of the acoustic data; The method according to any one of claims 1 to 19.

21. the user interface is connected to a user during generation of the acoustic data. The method according to any one of claims 1 to 19.

22. and providing a flow of pressurized air through the conduit and into the user interface during generation of the acoustic data. The method according to any one of claims 1 to 21.

23. and emitting the acoustic signal within the conduit during an absence of a flow of pressurized air through the conduit into the user interface. A method according to any one of claims 1 to 22.

24. the generated acoustic data is generated from a plurality of acoustic reflections from a plurality of acoustic signals; or the generated acoustic data is an average of the acoustic reflections from the acoustic signals. The method according to any one of claims 1 to 23.

25. analyzing the generated acoustic data includes identifying one or more signatures that correlate to the one or more features of the user interface; The method further includes classifying the user interface based at least in part on the one or more signatures. A method according to any one of claims 1 to 24.

26. the user interface is characterized from a subset of user interfaces determined based at least in part on a category of the user interface; 26. The method of claim 25.

27. and validating the characterized user interfaces based at least in part on the characterized user interfaces satisfying the user interface category.

26. The method of claim 25.

28. determining a confidence score that the user interface is correctly characterized; Validating the characterized user interface occurs when the confidence score meets a confidence threshold.

28. The method of claim 27.

29. receiving generated acoustic data associated with acoustic reflections of the acoustic signal; the acoustic reflections at least partially indicative of one or more characteristics of at least one of a user interface and a conduit; the user interface is coupled to a respiratory treatment device via the conduit; and analyzing the generated acoustic data, The analyzing includes windowing the generated acoustic data based at least in part on at least one characteristic of the one or more characteristics of at least one of the user interface and the conduit; analyzing the at least one characteristic including a known length of the conduit; 1. A computer program product comprising a non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to: characterizing the user interface based at least in part on the windowed and analyzed acoustic data; Computer program products.

30. a control system including one or more processors; a memory storing machine-readable instructions; Including, the control system is coupled to the memory; Execution of the machine-readable instructions in the memory causes the method of any one of claims 1 to 28 to be performed by the one or more processors of the control system. system.

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