Automated airflow modulation based on flow generator data

Abstract

According to some implementations of the present disclosure, a method includes accessing breath data for a respiratory therapy system, the breath data indicative of whether a user is wearing a user interface of the respiratory therapy system. The method also includes, in response to determining, based on the breath data, that the user is wearing the user interface, accessing adjustment data for the respiratory therapy system, the adjustment data indicative of whether the user is adjusting the user interface. Further, the methods includes, in response to determining, based on the adjustment data, that the user is not adjusting the user interface, modulating a flow of air via the user interface of the respiratory therapy system.

Description

AUTOMATED AIRFLOW MODULATION BASED ON FLOW GENERATOR DATACROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This Application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 624,440, filed on January 24, 2024, the entire contents of which are incorporated herein by reference.TECHNICAL FIELD

[0002] The present disclosure relates generally to systems and methods to provide automated modulation of airflow based on respiratory therapy flow generator data.BACKGROUND

[0003] Many individuals suffer from sleep-related and / or respiratory-related disorders such as, for example, Periodic Limb Movement Disorder (PLMD), Restless Leg Syndrome (RLS), Sleep-Disordered Breathing (SDB) such as Obstructive Sleep Apnea (OSA) and Central Sleep Apnea (CSA), Cheyne-Stokes Respiration (CSR), respiratory insufficiency, Obesity Hyperventilation Syndrome (OHS), Chronic Obstructive Pulmonary Disease (COPD), Neuromuscular Disease (NMD), and chest wall disorders. These disorders are often treated using respiratory therapy systems.

[0004] Each respiratory therapy system generally comprises a respiratory therapy device connected to a user interface (e.g., a mask) via a conduit and optionally a connector. The user wears the user interface during a therapy session and is supplied a flow of pressurized air from the respiratory therapy device via the conduit. The user interface generally is a specific category and type of user interface for the user, such as direct or indirect connections for the category of user interface, and full face mask, a partial face mask, nasal mask, or nasal pillows for the type of user interface. In addition to the specific category and type, the user interface generally is a specific model made by a specific manufacturer, e.g., AirFit™ F20 manufactured by ResMed.

[0005] In some cases, some users may find it difficult or uncomfortable to wear the user interface when the respiratory therapy device is not providing airflow. That is, if the user dons the mask before starting the airflow, the user may experience discomfort. Likewise, some users may find it difficult or uncomfortable to don the user interface if the respiratory therapy device is already providing airflow. That is, if the user dons the mask after starting the airflow, the user may experience discomfort. Also, some users may find it difficult or uncomfortable, orsimply inconvenient, to manually start a flow of air from the respiratory therapy device after donning the user interface.

[0006] The present disclosure is directed to solving these and other problems.SUMMARY

[0007] According to some implementations of the present disclosure, a method includes accessing breath data for a respiratory therapy system, the breath data indicative of whether a user is wearing a user interface of the respiratory therapy system; in response to determining, based on the breath data, that the user is wearing the user interface, accessing adjustment data for the respiratory therapy system, the adjustment data indicative of whether the user is adjusting the user interface; and in response to determining, based on the adjustment data, that the user is not adjusting the user interface, modulating a flow of air via the user interface of the respiratory therapy system.

[0008] According to some implementations of the present disclosure, a respiratory therapy system, includes a user interface, one or more breath sensors configured to detect breath data indicative of whether a user is wearing the user interface, one or more adjustment sensors configured to detect adjustment data indicative of whether a user is adjusting the user interface, and a flow generator configured to modulate a flow of air via the user interface based on the breath data and the adjustment data.

[0009] According to some implementations of the present disclosure, a system includes a control system and a memory. The control system includes one or more processors. The memory has stored thereon machine readable instructions. The control system is coupled to the memory, and any one of the methods disclosed herein is implemented when the machine executable instructions in the memory are executed by at least one of the one or more processors of the control system.

[0010] According to some implementations of the present disclosure, a system includes a control system configured to implement any one of the methods disclosed herein.

[0011] According to some implementations of the present disclosure, a computer program product includes instructions which, when executed by a computer, cause the computer to carry out any one of the methods disclosed herein.

[0012] The above summary is not intended to represent each implementation or every aspect of the present disclosure. Additional features and benefits of the present disclosure are apparent from the detailed description and figures set forth below.BRIEF DESCRIPTION OF THE DRAWINGS

[0013] FIG. 1 is a functional block diagram of a system, according to some implementations of the present disclosure;

[0014] FIG. 2 is a perspective view of at least a portion of the system of FIG. 1, a user, and a bed partner, according to some implementations of the present disclosure;

[0015] FIG. 3 is a rear perspective view of a respiratory therapy device of the system of FIG. 1, according to some implementations of the present disclosure.

[0016] FIG. 4 is a process flow diagram for a method for automatically modulating respiratory therapy airflow based on breath and adjustment data, according to some implementations of the present disclosure.

[0017] FIG. 5 is a process flow diagram for a method for generating and evaluating breath data features for pre-therapy operations, according to some implementations of the present disclosure.

[0018] FIG. 6 is a process flow diagram for a method for generating and evaluating adjustment data features, according to some implementations of the present disclosure.

[0019] FIG. 7 is a process flow diagram for a method for dynamically performing posttherapy airflow operations, according to some implementations of the present disclosure.

[0020] FIG. 8 is a process flow diagram for a method for generating and evaluating breath data features for post-therapy operations, according to some implementations of the present disclosure.

[0021] FIG. 9 is a process flow diagram for a method for training machine learning models to evaluate breath data and / or adjustment data, according to some implementations of the present disclosure.

[0022] FIG. 10 is a process flow diagram for a method for modulating airflow based on breath data and adjustment data, according to some implementations of the present disclosure.

[0023] While the present disclosure is susceptible to various modifications and alternative forms, specific implementations and embodiments thereof have been shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that it is not intended to limit the present disclosure to the particular forms disclosed, but on the contrary, the present disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.DETAILED DESCRIPTION

[0024] Many individuals suffer from sleep-related and / or respiratory disorders. Examples of sleep-related and / or respiratory disorders include Periodic Limb Movement Disorder (PLMD), Restless Leg Syndrome (RLS), Sleep-Disordered Breathing (SDB) such as Obstructive Sleep Apnea (OSA), Central Sleep Apnea (CSA), and other types of apneas (e.g., mixed apneas and hypopneas), Respiratory Effort Related Arousal (RERA), Cheyne-Stokes Respiration (CSR), respiratory insufficiency, Obesity Hyperventilation Syndrome (OHS), Chronic Obstructive Pulmonary Disease (COPD), Neuromuscular Disease (NMD), and chest wall disorders.

[0025] Obstructive Sleep Apnea (OSA) is a form of Sleep Disordered Breathing (SDB), and is characterized by events including occlusion or obstruction of the upper air passage during sleep resulting from a combination of an abnormally small upper airway and the normal loss of muscle tone in the region of the tongue, soft palate and posterior oropharyngeal wall. More generally, an apnea generally refers to the cessation of breathing caused by blockage of the air (Obstructive Sleep Apnea) or the stopping of the breathing function (often referred to as Central Sleep Apnea). Typically, the individual will stop breathing for between about 15 seconds and about 30 seconds during an obstructive sleep apnea event.

[0026] Other types of apneas include hypopnea, hyperpnea, and hypercapnia. Hypopnea is generally characterized by slow or shallow breathing caused by a narrowed airway, as opposed to a blocked airway. Hyperpnea is generally characterized by an increase depth and / or rate of breathing. Hypercapnia is generally characterized by elevated or excessive carbon dioxide in the bloodstream, typically caused by inadequate respiration.

[0027] A Respiratory Effort Related Arousal (RERA) event is typically characterized by an increased respiratory effort for 10 seconds or longer leading to arousal from sleep and which does not fulfill the criteria for an apnea or hypopnea event. In 1999, the American Academy of Sleep Medicine (AASM) Task Force defined RERAs as “a sequence of breaths characterized by increasing respiratory effort leading to an arousal from sleep, but which does not meet criteria for an apnea or hypopnea.” These events must fulfil both of the following criteria: 1. pattern of progressively more negative esophageal pressure, terminated by a sudden change in pressure to a less negative level and an arousal; 2. the event lasts 10 seconds or longer. In 2000, the study “Non-Invasive Detection of Respiratory Effort-Related Arousals (RERAs) by a Nasal Cannula / Pressure Transducer System” done at NYU School of Medicine and published in Sleep, vol. 23, No. 6, pp. 763-771, demonstrated that a Nasal Cannula / Pressure Transducer System was adequate and reliable in the detection of RERAs. A RERA detector may be basedon a real flow signal derived from a respiratory therapy (e.g., PAP) device. For example, a flow limitation measure may be determined based on a flow signal. A measure of arousal may then be derived as a function of the flow limitation measure and a measure of sudden increase in ventilation. Some such methods are described in WO 2008 / 138040, assigned to ResMed Ltd., the disclosure of which is hereby incorporated herein by reference in its entirety.

[0028] Cheyne-Stokes Respiration (CSR) is another form of sleep disordered breathing. CSR is a disorder of a patient’s respiratory controller in which there are rhythmic alternating periods of waxing and waning ventilation known as CSR cycles. CSR is characterized by repetitive de-oxygenation and re-oxygenation of the arterial blood.

[0029] Obesity Hyperventilation Syndrome (OHS) is defined as the combination of severe obesity and awake chronic hypercapnia, in the absence of other known causes for hypoventilation. Symptoms include dyspnea, morning headache and excessive daytime sleepiness.

[0030] Chronic Obstructive Pulmonary Disease (COPD) encompasses any of a group of lower airway diseases that have certain characteristics in common, such as increased resistance to air movement, extended expiratory phase of respiration, and loss of the normal elasticity of the lung.

[0031] Neuromuscular Disease (NMD) encompasses many diseases and ailments that impair the functioning of the muscles either directly via intrinsic muscle pathology, or indirectly via nerve pathology. Chest wall disorders are a group of thoracic deformities that result in inefficient coupling between the respiratory muscles and the thoracic cage.

[0032] These and other disorders are characterized by particular events (e.g., snoring, an apnea, a hypopnea, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, or any combination thereof) that occur when the individual is sleeping.

[0033] The Apnea-Hypopnea Index (AHI) is an index used to indicate the severity of sleep apnea during a sleep session. The AHI is calculated by dividing the number of apnea and / or hypopnea events experienced by the user during the sleep session by the total number of hours of sleep in the sleep session. The event can be, for example, a pause in breathing that lasts for at least 10 seconds. An AHI that is less than 5 is considered normal. An AHI that is greater than or equal to 5, but less than 15 is considered indicative of mild sleep apnea. An AHI that is greater than or equal to 15, but less than 30 is considered indicative of moderate sleep apnea. An AHI that is greater than or equal to 30 is considered indicative of severe sleep apnea. In children, an AHI that is 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 levels to indicate the severity of Obstructive Sleep Apnea.

[0034] Referring to FIG. 1, a system 100, according to some implementations of the present disclosure, is illustrated. The system 100 includes a control system 110, a memory device 114, an electronic interface 119, one or more sensors 130, and one or more user devices 170. In some implementations, the system 100 further optionally includes a respiratory therapy system 120, and an activity tracker 180.

[0035] The control system 110 includes one or more processors 112 (hereinafter, processor 112). The control system 110 is generally used to control (e.g., actuate) the various components of the system 100 and / or analyze data obtained and / or generated by the components of the system 100. The processor 112 can be a general or special purpose processor or microprocessor. While one processor 112 is illustrated in FIG. 1, the control system 110 can include any number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that can be in a single housing, or located remotely from each other. The control system 110 (or any other control system) or a portion of the control system 110 such as the processor 112 (or any other processor(s) or portion(s) of any other control system), can be used to carry out one or more steps of any of the methods described and / or claimed herein. The control system 110 can be coupled to and / or positioned within, for example, a housing of the user device 170, a portion (e.g., a housing) of the respiratory therapy system 120, and / or within a housing of one or more of the sensors 130. The control system 110 can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct). In such implementations including two or more housings containing the control system 110, such housings can be located proximately and / or remotely from each other.

[0036] The memory device 114 stores machine-readable instructions that are executable by the processor 112 of the control system 110. The memory device 114 can be any suitable computer readable storage device or media, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. While one memory device 114 is shown in FIG. 1, the system 100 can 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 positioned within a housing of a respiratory therapy device 122 of the respiratory therapy system 120, within a housing of the user device 170, within a housing of one or more of the sensors 130, or any combination thereof. Like the control system 110, the memory device 114 can be centralized(within one such housing) or decentralized (within two or more of such housings, which are physically distinct).

[0037] In some implementations, the memory device 114 stores a user profile associated with the user. The user profile can 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 earlier sleep sessions), or any combination thereof. The demographic information can include, for example, information indicative of an age of the user, a gender of the user, a race of the user, a geographic location of the user, a relationship status, a family history of insomnia or sleep apnea, an employment status of the user, an educational status of the user, a socioeconomic status of the user, or any combination thereof. The medical information can include, for example, information indicative of one or more medical conditions associated with the user, medication usage by the user, or both. The medical information data can further include a multiple sleep latency test (MSLT) result or score and / or a Pittsburgh Sleep Quality Index (PSQI) score or value. The self-reported user feedback can include information indicative of a self-reported subjective sleep score (e.g., poor, average, excellent), 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, a recent life event experienced by the user, or any combination thereof.

[0038] The electronic interface 119 is configured to receive data (e.g., physiological data and / or acoustic data) from the one or more sensors 130 such that the data can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The electronic interface 119 can communicate with the one or more sensors 130 using a wired connection or a wireless connection (e.g., using an RF communication protocol, a Wi-Fi communication protocol, a Bluetooth communication protocol, over a cellular network, etc.). The electronic interface 119 can 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 can also include one or more processors and / or one or more memory devices that are the same as, or similar to, the processor 112 and the memory device 114 described herein. In some implementations, the electronic interface 119 is coupled to or integrated in the user device 170. In other implementations, the electronic interface 119 is coupled to or integrated (e.g., in a housing) with the control system 110 and / or the memory device 114.

[0039] As noted above, in some implementations, the system 100 optionally includes a respiratory therapy system 120. The respiratory therapy system 120 can include a respiratorypressure therapy (RPT) device 122 (referred to herein as respiratory therapy device 122), a user interface 124, a conduit 126 (also referred to as a tube or an air circuit), a display device 128, a humidification tank 129, or any combination thereof. In some implementations, the control system 110, the memory device 114, the display device 128, one or more of the sensors 130, and the humidification tank 129 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to the application of a supply of air to an entrance to a user’s airways at a controlled target pressure that is nominally positive with respect to atmosphere throughout the user’s breathing cycle (e.g., in contrast to negative pressure therapies such as the tank ventilator or cuirass). The respiratory therapy system 120 is generally used to treat individuals suffering from one or more sleep-related respiratory disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0040] The respiratory therapy device 122 is generally used to generate pressurized air that is delivered to a user (e.g., using one or more motors that drive one or more compressors). In some implementations, the respiratory therapy device 122 generates continuous constant air pressure that is delivered 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 a variety of different air pressures within a predetermined range. For example, the respiratory therapy device 122 can deliver at least about 6 cmkhO, at least about 10 cmkhO, at least about 20 cmkhO, between about 6 cmlTO and about 10 cmlTO, between about 7 cmlTO and about 12 cml O, etc. 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 (relative to the ambient pressure).

[0041] The user interface 124 engages a portion of the user’s face and delivers pressurized air from the respiratory therapy device 122 to the user’s airway to aid in preventing the airway from narrowing and / or collapsing during sleep. This may also increase the user’s oxygen intake during sleep. Generally, the user interface 124 engages the user’s face such that the pressurized air is delivered to the user’s airway via the user’s mouth, the user’s nose, or both the user’s mouth and nose. Together, the respiratory therapy device 122, the user interface 124, and the conduit 126 form an air pathway fluidly coupled with an airway of the user. The pressurized air also increases the user’s oxygen intake during sleep. Depending upon the therapy to be applied, the user interface 124 may form a seal, for example, with a region or portion of the user’s face, to facilitate the delivery of gas at a pressure at sufficient variance with ambient pressure to effect therapy, for example, at a positive pressure of about 10 cml Orelative to ambient pressure. For other forms of therapy, such as the delivery of oxygen, the user interface may not include a seal sufficient to facilitate delivery to the airways of a supply of gas at a positive pressure of about 10 cmFhO. In some implementations, the user interface 124 may include a connector 127 and one or more vents 125. In some implementations, the connector 127 is distinct from, but couplable to, the user interface 124 (and / or conduit 126).

[0042] As shown in FIG. 2, in some implementations, the user interface 124 is a facial mask (e.g., a full face mask) that covers the nose and mouth of the user. Alternatively, the user interface 124 can be a nasal mask that provides air to the nose of the user or a nasal pillow mask that delivers air directly to the nostrils of the user. The user interface 124 can include a plurality of straps forming, for example, a headgear for aiding in positioning and / or stabilizing the user interface on a portion of the user (e.g., the face) and a conformal cushion (e.g., silicone, plastic, foam, etc.) that aids in providing an air-tight seal between the user interface 124 and the user. The user interface 124 can also include one or more vents for permitting the escape of carbon dioxide and other gases exhaled by the user 210. In other implementations, the user interface 124 includes a mouthpiece (e.g., a night guard mouthpiece molded to conform to the teeth of the user, a mandibular repositioning device, etc.).

[0043] Referring back to FIG. 1, the conduit 126 (also referred to as an air circuit or tube) allows the flow of air between two components of a respiratory therapy system 120, such as the respiratory therapy device 122 and the user interface 124. In some implementations, there can be separate limbs of the conduit for inhalation and exhalation. In other implementations, a single limb conduit is used for both inhalation and exhalation.

[0044] One or more of the respiratory therapy device 122, the user interface 124, the conduit 126, the display device 128, and the humidification tank 129 can contain one or more sensors (e.g., a pressure sensor, a flow rate 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 pressurized air supplied by the respiratory therapy device 122.

[0045] Referring briefly to FIG. 3, a perspective view of the back side of the respiratory therapy device 122 that includes a housing 123, an air inlet 186, and an air outlet 190. The air inlet 186 includes an inlet cover 182 movable between a closed position and an open position. The air inlet cover 182 includes one or more air inlet apertures 184 defined therein. The respiratory therapy device 122 includes a blower motor configured to draw air in through the one or more air inlet apertures 184 defined in the air inlet cover 182. The motor is further configured to cause pressurized air to flow through the humidification tank 129 and out of the air outlet 190. The conduit 126 can be fluidly coupled to the air outlet 190, such that the airflows from the air outlet 190 and into the conduit 126. The air outlet 190 is partially formed by an internal conduit 192 extending through the housing 123 from the interior of the respiratory therapy device 122. A seal 194 is positioned around the end of the internal conduit 192 to ensure that substantially all of the air that exits through the air outlet 190 flows into the conduit 126.

[0046] Referring back to FIG. 1, the display device 128 is generally used to display image(s) including still images, video images, or both and / or information regarding the respiratory therapy device 122. For example, the display device 128 (and / or the display device 172 of the user device 170) can provide information regarding the status of the respiratory therapy device 122 (e.g., whether the respiratory therapy device 122 is on / off, the pressure of the air being delivered by the respiratory therapy device 122, the temperature of the air being delivered by the respiratory therapy device 122, etc.) and / or other information (e.g., a question or questionnaire, or a sleep score and / or a therapy score, also referred to as a my Air™ score, such as described in WO 2016 / 061629, which is hereby incorporated by reference herein in its entirety; the current date / time; personal information for the user 210; etc.). In some implementations, the display device 128 acts as a human-machine interface (HMI) that includes a graphic user interface (GUI) configured to display the image(s) as an input interface. The display device 128 can be a light-emitting diode (LED) display, an organic light-emitting diode (OLED) display, an LCD display, or the like. The input interface can be, for example, a touchscreen or 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. Display device 172 of user device 170 may operate in the same or similar way to display device 128 and may be used with or instead of display device 128.

[0047] The humidification tank 129 is coupled to or integrated in the respiratory therapy device 122 and includes a reservoir of water that can be used to humidify the pressurized air delivered from the respiratory therapy device 122. The respiratory therapy device 122 can include a heater to heat the water in the humidification tank 129 in order to humidify the pressurized air provided to the user. Additionally, in some implementations, the conduit 126 can also include a heating element (e.g., coupled to and / or imbedded in the conduit 126) that heats the pressurized air delivered to the user. The humidification tank 129 can be fluidly coupled to a water vapor inlet of the air pathway and deliver water vapor into the air pathway via the water vapor inlet, or can be formed in-line with the air pathway as part of the air pathway itself.

[0048] 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 automatic positive airway pressure system (APAP), a bi-level or variable positive airway pressure system (BPAP or VPAP), or any combination thereof. The CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to the user. The APAP system automatically varies the air pressure delivered to the user based on, for example, respiration data associated with the user. The BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., an inspiratory positive airway pressure or IPAP) and a second predetermined pressure (e.g., an expiratory positive airway pressure or EPAP) that is lower than the first predetermined pressure.

[0049] Referring to FIG. 2, a portion of the system 100 (FIG. 1), according to some implementations, is illustrated. A user 210 of the respiratory therapy system 120 and a bed partner 220 are located in a bed 230 and are laying on a mattress 232. The user interface 124 (also referred to herein as a mask, e.g., a full facial 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. In turn, the respiratory therapy device 122 delivers pressurized air to the user 210 via the conduit 126 and the user interface 124 to increase the air pressure in the throat of the user 210 to aid in preventing the airway from closing and / or narrowing during sleep. The respiratory therapy device 122 can be positioned on a 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 generally adjacent to the bed 230 and / or the user 210.

[0050] Referring to back to FIG. 1, the one or more sensors 130 of the system 100 include a pressure sensor 132, a flow rate sensor 134, temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio-frequency (RF) receiver 146, a RF transmitter 148, a camera 150, an infrared sensor 152, a photoplethysmography (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalography (EEG) sensor 158, a capacitive sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyography (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, a EiDAR sensor 178, or any combination thereof. Generally, each of the one or more sensors 130 are configured to output sensor data that is received and stored in the memory device 114 or one or more other memory devices.

[0051] While the one or more sensors 130 are shown and described as including each of the pressure sensor 132, the flow rate sensor 134, the temperature sensor 136, the motion sensor 138, the microphone 140, the speaker 142, the RF receiver 146, the RF transmitter 148, thecamera 150, the infrared sensor 152, the photoplethysmography (PPG) sensor 154, the electrocardiogram (ECG) sensor 156, the electroencephalography (EEG) sensor 158, the capacitive sensor 160, the force sensor 162, the strain gauge sensor 164, the electromyography (EMG) sensor 166, the oxygen sensor 168, the analyte sensor 174, the moisture sensor 176, and the LiDAR sensor 178, more generally, the one or more sensors 130 can include any combination and any number of each of the sensors described and / or shown herein.

[0052] As described herein, the system 100 generally can be used to generate physiological data associated with a user (e.g., a user of the respiratory therapy system 120 shown in FIG. 2) during a sleep session. The physiological data can be analyzed to generate one or more sleep- related parameters, which can include any parameter, measurement, etc. related to the user during the sleep session. The one or more sleep-related parameters that can be determined for the user 210 during the sleep session include, for example, an Apnea-Hypopnea Index (AHI) score, a sleep score, a flow signal, a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, a stage, pressure settings of the respiratory therapy device 122, a heart rate, a heart rate variability, movement of the user 210, temperature, EEG activity, EMG activity, arousal, snoring, choking, coughing, whistling, wheezing, or any combination thereof.

[0053] The one or more sensors 130 can be used to generate, for example, physiological data, acoustic data, or both. Physiological data generated by one or more of the sensors 130 can be used by the control system 110 to determine a sleep-wake signal associated with the user 210 (FIG. 2) during the sleep session and one or more sleep-related parameters. The sleepwake signal can be indicative of one or more sleep states, including wakefulness, relaxed wakefulness, micro-awakenings, or distinct sleep stages such as, for example, a rapid eye movement (REM) stage, a first non-REM stage (often referred to as “Nl”), a second non-REM stage (often referred to as “N2”), a third non-REM stage (often referred to as “N3”), or any combination thereof. Methods for determining sleep states and / or sleep stages from physiological data generated by one or more sensors, such as the one or more sensors 130, are described in, for example, WO 2014 / 047310, US 2014 / 0088373, WO 2017 / 132726, WO 2019 / 122413, and WO 2019 / 122414, each of which is hereby incorporated by reference herein in its entirety.

[0054] In some implementations, the sleep-wake signal described herein can be timestamped to indicate a time that the user enters the bed, a time that the user exits the bed, a time that the user attempts to fall asleep, etc. The sleep-wake signal can be measured by the one or more sensorsl30 during the sleep session at a predetermined sampling rate, such as, forexample, one sample per second, one sample per 30 seconds, one sample per minute, etc. In some implementations, the sleep-wake signal can also be indicative of a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, pressure settings of the respiratory therapy device 122, or any combination thereof during the sleep session. The event(s) can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, a mask leak (e.g., from the user interface 124), a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, or any combination thereof. The one or more sleep-related parameters that can be determined for the user during the sleep session based on the sleep-wake signal include, for example, a total time in bed, a total sleep time, a sleep onset latency, a wake-after-sleep-onset parameter, a sleep efficiency, a fragmentation index, or any combination thereof. As described in further detail herein, the physiological data and / or the sleep-related parameters can be analyzed to determine one or more sleep-related scores.

[0055] Physiological data and / or acoustic data generated by the one or more sensors 130 can also be used to determine a respiration signal associated with a user during a sleep session. The respiration signal is generally indicative of respiration or breathing of the user during the sleep session. The respiration signal can be indicative of and / or analyzed to determine (e.g., using the control system 110) one or more sleep-related parameters, such as, for example, a respiration rate, a respiration rate variability, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, an occurrence of one or more events, a number of events per hour, a pattern of events, a sleep state, a sleet stage, an apnea-hypopnea index (AHI), pressure settings of the respiratory therapy device 122, or any combination thereof. The one or more events can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, a mask leak (e.g., from the user interface 124), a cough, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, increased blood pressure, or any combination thereof. Many of the described sleep-related parameters are physiological parameters, although some of the sleep- related parameters can be considered to be non-physiological parameters. Other types of physiological and / or non-physiological parameters can also be determined, either from the data from the one or more sensors 130, or from other types of data.

[0056] The pressure sensor 132 outputs pressure 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 pressure sensor 132 is an air pressure sensor (e.g., barometric pressuresensor) that generates sensor data indicative of the respiration (e.g., inhaling and / or exhaling) of the user of the respiratory therapy system 120, sensor data indicative of the pressure of the flow of air generated by the respiratory therapy device 122, sensor data indicative of the pressure of the flow of air delivered to the user interface from the respiratory therapy device 122, and / or ambient pressure. In such implementations, the pressure sensor 132 can be coupled to or integrated in the respiratory therapy device 122. The pressure sensor 132 can be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain-gauge sensor, an optical sensor, an acoustic sensor (e.g., microphone), a potentiometric sensor, or any combination thereof.

[0057] The flow rate sensor 134 outputs flow rate data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. Examples of flow rate sensors (such as, for example, the flow rate sensor 134) are described in WO 2012 / 012835, which is hereby incorporated by reference herein in its entirety. In some implementations, the flow rate sensor 134 is used to determine an air flow rate from the respiratory therapy device 122, an air flow rate through the conduit 126, an air flow rate through the user interface 124, or any combination thereof. In such implementations, the flow rate sensor 134 can be coupled to or integrated in the respiratory therapy device 122, the user interface 124, or the conduit 126. The flow rate sensor 134 can be a mass flow rate sensor such as, for example, a rotary flow meter (e.g., Hall effect flow meters), 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. In some implementations, the flow rate sensor 134 is configured to measure a vent flow (e.g., intentional “leak”), an unintentional leak (e.g., mouth leak and / or mask leak), a patient flow (e.g., air into and / or out of lungs), or any combination thereof. In some implementations, the flow rate data can be analyzed to determine cardiogenic oscillations of the user. In one example, the pressure sensor 132 can be used to determine a blood pressure of a user.

[0058] 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 temperatures data indicative of a core body temperature of the user 210 (FIG. 2), a skin temperature of the user 210, a temperature of the air flowing from the respiratory therapy device 122 and / or through the conduit 126, a temperature in the user interface 124, an ambient temperature, or any combination thereof. The temperature sensor 136 can be, for example, a thermocouple sensor, a thermistor sensor, a silicon band gap temperature sensor or semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0059] 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 movement of the user 210 during the sleep session, and / or detect movement of any of the components 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 accelerometers, gyroscopes, and magnetometers. In some implementations, the motion sensor 138 alternatively or additionally generates one or more signals representing bodily movement of the user, from which may be obtained a signal representing a sleep state of the user; for example, via a respiratory movement of the user. In some implementations, the motion data from the motion sensor 138 can be used in conjunction with additional data from another sensor 130 to determine the sleep state of the user.

[0060] The microphone 140 can be located at any location relative to the respiratory therapy system 120 and in acoustic communication with the airflow in the respiratory therapy system 120. For example, the respiratory therapy system 120 may include a microphone 140 (i) coupled externally to the conduit 126, (ii) positioned within, optionally at least partially within the respiratory therapy device 122, (iii) coupled externally to the user interface 124, (iv) coupled directly or indirectly to a headgear associated with the user interface 124, or in any other suitable location. In some implementations, the microphone 140 is coupled to a mobile device (for example, the user device 170 or a smart speaker(s) such as Google Nest Hub™, Google Home™, Amazon Echo™, Amazon Show™, Alexa™-enabled devices, etc.) that is communicatively coupled to the respiratory therapy system 120.

[0061] In some implementations, the microphone 140 is positioned on or at least partially outside of a housing of the respiratory therapy device 122. For example, the microphone 140 may be at least partially movable relative to the housing of the respiratory therapy device 122 to aid in being directed to the user 210 (FIG. 2). For example, the microphone 340 can be rotated between about 5° and about 355° towards the user 210.

[0062] In some implementations, the microphone 140 is configured to be in direct fluid communication with the airflow in the respiratory therapy system 120. In the same or alternative implementations, the microphone 140 is configured to be in acoustic communication with the airflow in the respiratory therapy system 120. For example, the microphone 140 may be (i) positioned at least partially within the conduit 126, (ii) positioned at least partially within the respiratory therapy device 122, optionally positioned at least partially within a component of the respiratory therapy device 122, which is in fluidcommunication with the conduit 126, or (iii) positioned at least partially within the user interface 124, the user interface 124 being in fluid communication with the conduit 126. Further, in some implementations, the microphone 140 is electrically connected with a circuit board (for example, connected physically, such as mounted on, the circuit board directly or indirectly) of the respiratory therapy device 122, which may be in acoustic communication (for example, via a small duct and / or a silicone window as in a stethoscope) or in fluid communication with the airflow in the respiratory therapy system 120.

[0063] The microphone 140 outputs sound and / or acoustic data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The acoustic data generated by the microphone 140 is reproducible as one or more sound(s) during a sleep session (e.g., sounds from the user 210). The acoustic data form the microphone 140 can also be used to identify (e.g., using the control system 110) an event experienced by the user during the sleep session, as described in further detail herein. The microphone 140 can be coupled to or integrated in the respiratory therapy device 122, the user interface 124, the conduit 126, or the user device 170. In some implementations, the system 100 includes a plurality of microphones (e.g., two or more microphones and / or an array of microphones with beamforming) such that sound data generated by each of the plurality of microphones can be used to discriminate the sound data generated by another of the plurality of microphones.

[0064] The speaker 142 outputs sound waves that are audible to a user of the system 100 (e.g., the user 210 of FIG. 2). The speaker 142 can be used, for example, as an alarm clock or to play an alert or message to the user 210 (e.g., in response to an event). In some implementations, the speaker 142 can be used to communicate the acoustic data generated by the microphone 140 to the user. The speaker 142 can be coupled to or integrated in the respiratory therapy device 122, the user interface 124, the conduit 126, or the user device 170.

[0065] 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 into an acoustic sensor 141 (e.g., a SONAR sensor), as described in, for example, WO 2018 / 050913 and WO 2020 / 104465, each of which is hereby incorporated by reference herein in its entirety. In such implementations, the speaker 142 generates or emits sound waves at a predetermined interval and the microphone 140 detects the reflections of the emitted sound waves from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency that is not audible to the human ear (e.g., below 20 Hz or above around 18 kHz) so as not to disturb the sleep of the user 210 or the bed partner 220 (FIG. 2). Based at least in part on the data from the microphone 140 and / or the speaker 142, the control system 110 can determine a locationof the user 210 (FIG. 2) and / or one or more of the sleep-related parameters described in herein such as, for example, a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, a sleep state, a sleep stage, pressure settings of the respiratory therapy device 122, or any combination thereof. In such a context, a SONAR sensor may be understood to concern an active acoustic sensing, such as by generating and / or transmitting ultrasound and / or low frequency ultrasound sensing signals (e.g., in a frequency range of about 17-23 kHz, 18-22 kHz, or 17-18 kHz, for example), through the air. Such a system may be considered in relation to WO 2018 / 050913 and WO 2020 / 104465 mentioned above, each of which is hereby incorporated by reference herein in its entirety.

[0066] In some implementations, the sensors 130 include (i) a first microphone that is the same as, or similar to, the microphone 140, and is integrated in 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 that is integrated in the acoustic sensor 141.

[0067] The RF transmitter 148 generates and / or emits 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 signals, short wave signals, etc.). The RF receiver 146 detects the reflections of the radio waves emitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine a location of the user 210 (FIG. 2) and / or one or more of the sleep-related parameters described herein. An 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, the one or more sensors 130, the user device 170, or any combination thereof. While the RF receiver 146 and RF transmitter 148 are shown as being separate and distinct elements in FIG. 1, in some implementations, the RF receiver 146 and RF transmitter 148 are combined as a 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 format of the RF communication can be Wi-Fi, Bluetooth, or the like.

[0068] In some implementations, the RF sensor 147 is a part of a mesh system. One example of a mesh system is a Wi-Fi mesh system, which can include mesh nodes, mesh router(s), and mesh gateway(s), each of which can be mobile / movable or fixed. In such implementations, the Wi-Fi mesh system includes a Wi-Fi router and / or a Wi-Fi controller and one or more satellites (e.g., access points), each of which include an RF sensor that is the same as, or similar to, the RF sensor 147. The Wi-Fi router and satellites continuously communicatewith one another using Wi-Fi signals. The Wi-Fi mesh system can be used to generate motion data based on changes in the Wi-Fi signals (e.g., differences in received signal strength) between the router and the satellite(s) due to an object or person moving partially obstructing the signals. The motion data can be indicative of motion, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0069] The camera 150 outputs image data reproducible as one or more images (e.g., still images, video images, thermal images, or any combination thereof) that can be stored in the memory device 114. The image data from the camera 150 can be used by the control system 110 to determine one or more of the sleep-related parameters described herein, such as, for example, one or more events (e.g., periodic limb movement or restless leg syndrome), a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, a sleep state, a sleep stage, or any combination thereof. Further, the image data from the camera 150 can be used to, for example, identify a location of the user, to determine chest movement of the user 210 (FIG. 2), to determine air flow of the mouth and / or nose of the user 210, to determine a time when the user 210 enters the bed 230 (FIG. 2), and to determine a time when the user 210 exits the bed 230. In some implementations, the camera 150 includes a wide-angle lens or a fish eye lens.

[0070] The infrared (IR) sensor 152 outputs infrared image data reproducible as one or more infrared images (e.g., still images, video images, or both) that can be stored in the memory 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 a temperature of the user 210 and / or movement of the user 210. The IR sensor 152 can also be used in conjunction with the camera 150 when measuring the presence, location, and / or movement of the user 210. The IR sensor 152 can detect infrared light having a wavelength between about 700 nm and about 1 mm, for example, while the camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0071] The PPG sensor 154 outputs physiological data associated with the user 210 (FIG. 2) that can be used to determine one or more sleep-related parameters, such as, for example, a heart rate, a heart rate variability, a cardiac cycle, respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, estimated blood pressure parameter(s), or any combination thereof. The PPG sensor 154 can be worn by the user 210, embedded in clothing and / or fabric that is worn by the user 210, embedded in and / or coupled to the user interface 124 and / or its associated headgear (e.g., straps, etc.), etc.

[0072] In some implementations, a PAT (peripheral arterial tone) sensing device may make use of a fingertip mounted PPG probe, e.g., PPG sensor 154. The PPG probe operates with an optical technology that detects blood volume changes in the tissue’s microvascular bed. As noted above, PPG measurements are used to derive the arterial blood oxygen saturation (SpO2), pulse rate (PR), and changes in peripheral arterial tone, which are then used to detect respiratory events. Peripheral arterial tone refers to the tone of the peripheral arterial smooth muscle tissue. When the muscle tone of peripheral arteries increases, the arteries’ diameter decreases, resulting in a reduction of perfusion and thus a decrease in pulsatile blood volume in the peripheral tissue. The decrease in pulsatile blood volume in the peripheral tissue is picked up as a drop in the PPG signal swing between systole and diastole. The PAT signal may be derived from the PPG signal from the PPG sensor, such as by the method described in WO 2021 / 260190, the disclosure of which is incorporated by reference herein in its entirety. The PPG-derived signal, which may be derived by trending such pulsatile blood volume reductions, is referred to as the PAT signal.

[0073] The ECG sensor 156 outputs physiological data associated with electrical activity of the heart of the user 210. In some implementations, the ECG sensor 156 includes one or more electrodes that are positioned on or around a portion of the user 210 during the sleep session. The physiological data from the ECG sensor 156 can be used, for example, to determine one or more of the sleep-related parameters described herein.

[0074] The EEG sensor 158 outputs physiological data associated with electrical activity of the brain of the user 210. In some implementations, the EEG sensor 158 includes one or more electrodes that are positioned on or around the scalp of the user 210 during the sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine a sleep state and / or a sleep stage of the user 210 at any given time during the sleep session. In some implementations, the EEG sensor 158 can be integrated in the user interface 124 and / or the associated headgear (e.g., straps, etc.).

[0075] The capacitive sensor 160, the force sensor 162, and the strain gauge sensor 164 output data that can be stored in the memory device 114 and used by the control system 110 to determine one or more of the sleep-related parameters described herein. The EMG sensor 166 outputs physiological data associated with electrical activity produced by one or more muscles. The oxygen sensor 168 outputs oxygen data indicative of an oxygen concentration of gas (e.g., in the conduit 126 or at the user interface 124). The oxygen sensor 168 can be, for example, an ultrasonic oxygen sensor, an electrical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, a pulse oximeter (e.g., SpO2 sensor), or any combination thereof. In someimplementations, 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 sphygmomanometer sensor, an oximetry sensor, or any combination thereof.

[0076] The analyte sensor 174 can be used to detect the presence and / or quantity / concentration of an analyte, e.g., CO2, in the exhaled breath of the user 210. The data output by the analyte sensor 174 can be stored in the memory device 114 and used by the control system 110 to determine the identity and concentration of any analytes in the breath of the user 210. In some implementations, the analyte sensor 174 is positioned near a mouth of the user 210, such as in or on the user interface 124 and / or conduit 126, to detect analytes in breath exhaled from the user 210’ s mouth. For example, when the user interface 124 is a facial mask that covers the nose and mouth of the user 210, the analyte sensor 174 can be positioned within the facial mask to monitor the user 210’s mouth breathing. In other implementations, such as when the user interface 124 is a nasal mask or a nasal pillow mask, the analyte sensor 174 can be positioned near the nose of the user 210 to detect analytes in breath exhaled through the user’s nose. In still other implementations, the analyte sensor 174 can be positioned near the user 210’s mouth when the user interface 124 is a nasal mask or a nasal pillow mask. In this implementation, the analyte sensor 174 can be used to detect whether any air is inadvertently leaking from the user 210’s mouth. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds. In some implementations, the analyte sensor 174 can also be used to detect whether the user 210 is breathing through their nose or mouth. For example, if the data output by an analyte sensor 174 positioned near the mouth of the user 210 or within the facial mask (in implementations where the user interface 124 is a facial mask) detects the presence of an analyte, the control system 110 can use this data as an indication that the user 210 is breathing through their mouth.

[0077] 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 210’ 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 to or integrated in the user interface 124 or in 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 moisture levels need to be monitored. The moisture sensor 176 can alsobe used to monitor the humidity of the ambient environment surrounding the user 210, for example, the air inside the bedroom.

[0078] The Light Detection and Ranging (LiDAR) sensor 178 can be used for depth sensing. This type of optical sensor (e.g., laser sensor) can be used to detect objects and build three dimensional (3D) maps of the surroundings, such as of a living space. LiDAR can generally utilize a pulsed laser to make time of flight measurements. LiDAR is also referred to as 3D laser scanning. In an example of 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 extending 5 meters or more away from the sensor. The LiDAR data can be fused with point cloud data estimated by an electromagnetic RADAR sensor, for example. The LiDAR sensor(s) 178 can also use artificial intelligence (Al) to automatically geofence RADAR systems by detecting and classifying features in a space that might cause issues for RADAR systems, such a glass windows (which can be highly reflective to RADAR). LiDAR can also be used to provide an estimate of the height of a person, as well as changes in height when the person sits down, or falls down, for example. LiDAR may be used to form a 3D mesh representation of an environment. In a further use, for solid surfaces through which radio waves pass (e.g., radio- translucent materials), the LiDAR may reflect off such surfaces, thus allowing a classification of different type of obstacles.

[0079] 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 heart rate sensor (e.g., pulse sensor), a blood pressure sensor (e.g., sphygmomanometer sensor), an oximetry sensor, a SONAR sensor, a RADAR sensor, a blood glucose sensor, a camera (e.g., color sensor), a pH sensor, a tilt sensor (which measures the tilt in multiple axes of a reference plane), an orientation sensor (which measures the orientation of a device relative to an orthogonal coordinate frame), an alcohol sensor, or any combination thereof.

[0080] While shown separately in FIG. 1, any combination of the one or more sensors 130 can be integrated in and / or coupled to any one or more of the components of the system 100, including the respiratory therapy device 122, the user interface 124, the conduit 126, the humidification tank 129, the control system 110, the user device 170, the activity tracker 180, or any combination thereof. For example, the microphone 140 and the speaker 142 can be integrated in and / or coupled to the user device 170 and the pressure sensor 132 and / or flow rate sensor 134 are integrated in and / or coupled to the respiratory therapy device 122. In some implementations, at least one of the one or more sensors 130 is not coupled to the respiratory therapy device 122, the control system 110, or the user device 170, and is positioned generallyadjacent to the user 210 during the sleep session (e.g., positioned on or in contact with a portion of the user 210, worn by the user 210, coupled to or positioned on the nightstand, coupled to the mattress, coupled to the ceiling, etc.).

[0081] The data from the one or more sensors 130 can be analyzed to determine one or more sleep-related parameters, which can include a respiration signal, a respiration rate, a respiration pattern, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, an occurrence of one or more events, a number of events per hour, a pattern of events, a sleep state, an apnea-hypopnea index (AHI), or any combination thereof. The one or more events can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, a mask leak, a cough, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, increased blood pressure, or any combination thereof. Many of these sleep-related parameters are physiological parameters, although some of the sleep-related parameters can be considered to be non- physiological parameters. Other types of physiological and non-physiological parameters can also be determined, either from the data from the one or more sensors 130, or from other types of data.

[0082] The user device 170 (FIG. 1) includes a display device 172. The user device 170 can be, for example, a mobile device such as a smart phone, a tablet, a gaming console, a smart watch, a laptop, or the like. Alternatively, the user device 170 can be an external sensing system, a television (e.g., a smart television) or another smart home device (e.g., a smart speaker(s) such as Google Nest Hub™, Google Home™, Amazon Echo™, Amazon Show™, Alexa™-enabled devices, etc.). In some implementations, the user device is a wearable device (e.g., a smart watch). The display device 172 is generally used to display image(s) including still images, video images, or both. In some implementations, the display device 172 acts as a human-machine interface (HMI) that includes a graphic user interface (GUI) configured to display the image(s) and an input interface. The display device 172 can be an LED display, an OLED display, a liquid crystal display (LCD), or the like. The input interface can be, for example, a touchscreen or touch- sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the user device 170. In some implementations, one or more user devices can be used by and / or included in the system 100.

[0083] In some implementations, the system 100 also includes an activity tracker 180. The activity tracker 180 is generally used to aid in generating physiological data associated with the user. The activity tracker 180 can include one or more of the sensors 130 described herein, such as, for example, the motion sensor 138 (e.g., one or more accelerometers and / orgyroscopes), the PPG sensor 154, and / or the ECG sensor 156. The physiological data from the activity tracker 180 can be used to determine, for example, a number of steps, a distance traveled, a number of steps climbed, a duration of physical activity, a type of physical activity, an intensity of physical activity, time spent standing, a respiration rate, an average respiration rate, a resting respiration rate, a maximum respiration rate, a respiration rate variability, a heart rate, an average heart rate, a resting heart rate, a maximum heart rate, a heart rate variability, a number of calories burned, blood oxygen saturation, electrodermal activity (also known as skin conductance or galvanic skin response), or any combination thereof. In some implementations, the activity tracker 180 is coupled (e.g., electronically (such as wirelessly) or physically) to the user device 170.

[0084] In some implementations, the activity tracker 180 is a wearable device that can be worn by the user, such as a smartwatch, a wristband, a ring, or a patch. For example, referring to FIG. 2, the activity tracker 180 is worn on a wrist of the user 210. The activity tracker 180 can also be coupled to or integrated a garment or clothing that is worn by the user. Alternatively still, the activity tracker 180 can also be coupled to or integrated in (e.g., within the same housing) the user device 170. More generally, the activity tracker 180 can be communicatively coupled with, or physically integrated in (e.g., within a housing), the control system 110, the memory device 114, the respiratory therapy system 120, and / or the user device 170.

[0085] While the control system 110 and the memory device 114 are described and shown in FIG. 1 as being a separate and distinct component of the system 100, in some implementations, the control system 110 and / or the memory device 114 are integrated in the user device 170 and / or the respiratory therapy device 122. Alternatively, in some implementations, the control system 110 or a portion thereof (e.g., the processor 112) can be located in a cloud (e.g., integrated in a server, integrated in an Internet of Things (loT) device, connected to the cloud, be subject to edge cloud processing, etc.), located in one or more servers (e.g., remote servers, local servers, etc., or any combination thereof.

[0086] While system 100 is shown as including all of the components described above, more or fewer components can be included in a system according to implementations of the present disclosure. For example, a first alternative system includes the control system 110, the memory device 114, and at least one of the one or more sensors 130 and does not include the respiratory therapy system 120. As another example, a second alternative system includes the control system 110, the memory device 114, at least one of the one or more sensors 130, and the user device 170. As yet another example, a third alternative system includes the control system 110, the memory device 114, the respiratory therapy system 120, at least one of the oneor more sensors 130, and the user device 170. Thus, various systems can be formed using any portion or portions of the components shown and described herein and / or in combination with one or more other components.

[0087] As used herein, a sleep session can be defined in multiple ways. For example, a sleep session can be defined by an initial start time and an end time. In some implementations, a sleep session is a duration where the user is asleep, that is, the sleep session has a start time and an end time, and during the sleep session, the user does not wake until the end time. That is, any period of the user being awake is not included in a sleep session. From this first definition of sleep session, if the user wakes ups and falls asleep multiple times in the same night, each of the sleep intervals separated by an awake interval is a sleep session.

[0088] 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, so long as a continuous duration that the user is awake is below an awake duration threshold. The awake duration threshold can be defined as a percentage of a sleep session. The awake duration threshold can be, for example, about twenty percent of the sleep session, about fifteen percent of the sleep session duration, about ten percent of the sleep session duration, about five percent of the sleep session duration, about two percent of the sleep session duration, etc., or any other threshold percentage. In some implementations, the awake duration threshold is defined as a fixed amount of time, such as, for example, about one hour, about thirty minutes, about fifteen minutes, about ten minutes, about five minutes, about two minutes, etc., or any other amount of time.

[0089] In some implementations, a sleep session is defined as the entire time between the time in the evening at which the user first entered the bed, and the time the next morning when user last left the bed. Put another way, a sleep session can be defined as a period of time that begins on a first date (e.g., Monday, January 6, 2020) at a first time (e.g., 10:00 PM), that can be referred to as the current evening, when the user first enters a bed with the intention of going to sleep (e.g., not if the user intends to first watch television or play with a smart phone before going to sleep, etc.), and ends on a second date (e.g., Tuesday, January 7, 2020) at a second time (e.g., 7:00 AM), that can be referred to as the next morning, when the user first exits the bed with the intention of not going back to sleep that next morning.

[0090] In some implementations, the user can manually define the beginning of a sleep session and / or manually terminate a sleep session. For example, the user can select (e.g., by clicking or tapping) one or more user-selectable element that is displayed on the display device 172 of the user device 170 (FIG. 1) to manually initiate or terminate the sleep session.

[0091] Generally, the sleep session includes any point in time after the user 210 has laid or sat down in the bed 230 (or another area or object on which they intend to sleep), and has turned on the respiratory therapy device 122 and donned the user interface 124. The sleep session can thus include time periods (i) when the user 210 is using the CPAP system but before the user 210 attempts to fall asleep (for example when the user 210 lays in the bed 230 reading a book); (ii) when the user 210 begins trying to fall asleep but is still awake; (iii) when the user 210 is in a light sleep (also referred to as stage 1 and stage 2 of non-rapid eye movement (NREM) sleep); (iv) when the user 210 is in a deep sleep (also referred to as slow-wave sleep, SWS, or stage 3 of NREM sleep); (v) when the user 210 is in rapid eye movement (REM) sleep; (vi) when the user 210 is periodically awake between light sleep, deep sleep, or REM sleep; or (vii) when the user 210 wakes up and does not fall back asleep.

[0092] The sleep session is generally defined as ending once the user 210 removes the user interface 124, turns off the respiratory therapy device 122, and gets out of bed 230. In some implementations, the sleep session can include additional periods of time, or can be limited to only some of the above-disclosed time periods. For example, the sleep session can be defined to encompass a period of time beginning when the respiratory therapy device 122 begins supplying the pressurized air to the airway or the user 210, ending when the respiratory therapy device 122 stops supplying the pressurized air to the airway of the user 210, and including some or all of the time points in between, when the user 210 is asleep or awake.

[0093] In some aspects, as discussed in more detail below, the respiratory therapy device 122 (or another component) may be configured to collect, measure, or otherwise access and analyze a variety of data to automatically modulate the flow of air that is provided via the user interface 124. For example, in response to determining that the user has donned the user interface 124 (e.g., the user is wearing and / or breathing through the user interface 124), the respiratory therapy device 122 may initiate, which may comprise increasing, the flow of air to begin therapy. In some embodiments, after determining that the user is wearing the user interface 124, the respiratory therapy device 122 may determine whether the user is adjusting the mask (e.g., shifting or rearranging the user interface 124 for comfort, improved seal, or ease of breathing). For example, the user may experience discomfort if the respiratory therapy device 122 initiates the flow of air before the user has finished adjusting the mask and is ready for therapy. In some embodiments, once the respiratory therapy device 122 determines that the user has stopped or completed adjusting the user interface 124, the respiratory therapy device 122 may initiate, which may comprise increasing, the flow of air to begin therapy.

[0094] In some embodiments, when the user ends therapy and doffs the user interface 124, the respiratory therapy device 122 may enter one or more post-therapy operations. In some embodiments, the post-therapy operation(s) may include providing a flow of air from the respiratory therapy device. For example, in some aspects, a flow of air may be provided to dry the various components (e.g., the conduit 126 and / or user interface 124). In some aspects, if the user is operating the respiratory therapy system 120 with climate settings enabled (e.g., with a heated conduit 126 and / or humidification via the humidification tank 129), the post-flow therapy operations may include providing a flow of air to cool these components down (e.g., to cool the conduit, the humidifier plate, and the like). In these aspects, the flow of air may be relatively small compared to the flow of air supplied during therapy. In some embodiments, if any components of the respiratory therapy system 120 are heated during operation, the posttherapy operations may generally include a cool-down process to cool any heated elements using airflow.

[0095] In some embodiments, this post-therapy operation may begin whenever the respiratory therapy device 122 determines that the user has doffed the user interface 124. However, if the user subsequently returns to continue or restart therapy during the post-therapy operations (e.g., after using the bathroom during the night), the user may experience discomfort in donning the user interface 124 while air associated with the post-therapy operation is flowing. In some embodiments, the respiratory therapy device 122 (or another component) may be configured to evaluate various data (e.g., breath data and / or adjustment data) to determine whether the post-therapy operations should be halted (e.g., because the user re- donned the user interface 124) or continued, as discussed below in more detail.

[0096] Referring to FIG. 4, a method 400 for automatically modulating respiratory therapy airflow based on breath and adjustment data, according to some implementations of the present disclosure. One or more steps of the method 400 can be implemented using any element or aspect of the system 100 (FIGS. 1-2) described herein. While the method 400 has been shown and described herein as occurring in a certain order, more generally, the steps of the method 400 can be performed in any suitable order. Similarly, each step may generally be optional, and may be omitted in some embodiments.

[0097] At block 410, a respiratory therapy system accesses breath data via one or more sensors. As used herein, “accessing” data may generally include receiving, requesting, retrieving, generating, collecting, measuring, obtaining, or otherwise gaining access to the data. For example, accessing breath data may include collecting or measuring various metrics using one or more sensors. Though referred to as “breath” data for conceptual clarity, in someembodiments, the data may or may not correspond to the actual breath of a user. That is, in some embodiments, the breath data may be indicative of whether a user’s breath is present (e.g., whether the user is wearing the user interface of the respiratory therapy system).

[0098] Generally, a wide variety of sensors may be used to generate the breath data. For example, in some aspects, the breath data comprises pressure data indicating air pressure in one or more components of the respiratory therapy system (e.g., in the user interface, in the conduit, in the flow generator, and the like). For example, the breath data may be collected or measured at least partially using a pressure sensor such as the pressure sensor 132 of FIG. 1. As another example, the breath data may comprise information about the flow rate of airflow (e.g., via a conduit, via the user interface, and the like). For example, the breath data may be collected or measured at least partially using a flow sensor, such as the flow rate sensor 134 of FIG. 1.

[0099] As another example, the breath data may include information about the concentration and / or presence of one or more analytes (such as carbon dioxide, humidity, and the like) that are emitted by users breathing. For example, the breath data may be collected or measured at least partially using a carbon dioxide sensor, a humidity sensor (or any other sensors for one or more other analytes), such as the analyte sensor 174 of FIG. 1. As another example, the breath data may include audio data captured by a microphone that is in acoustic communication with the airflow of the respiratory therapy system. For example, the breath data may be collected or measured at least partially using a microphone, such as the microphone 140 of FIG. 1.

[0100] Generally, the breath data may include a wide variety of information. In some embodiments, the breath data can comprise any information that may be indicative of whether a user is currently wearing the user interface of the respiratory therapy system (as of the time when the breath data is captured).

[0101] In some aspects, the breath data corresponds to information for a point in time (e.g., the pressure, flow rate, carbon dioxide concentration, and / or other data for a specific point in time). In some aspects, the breath data corresponds to a window of time. For example, the breath data may include continuous information (e.g., an audio waveform) and / or a sequence of data points (e.g., a data point every half second), where each data point indicates the relevant breath information at that point in time (e.g., the pressure, flow rate, audio data, and the like). In some embodiments, the length of the window may vary depending on the particular implementation. For example, in some embodiments, the breath data may correspond to a three second window, a four second window, a five second window, and the like. 1

[0102] In some embodiments, if the breath data corresponds to a window, the window may be a sliding or rolling window (e.g., where the respiratory therapy system evaluates breath data in overlapping windows, such as using four second long windows evaluated every half second). In some embodiments, the windows may be tumbling (e.g., non-overlapping), such as if the respiratory therapy system evaluates each four second window of time every four seconds. Such tumbling windows may be contiguous or non-contiguous.

[0103] At block 415, the respiratory therapy system determines, based on the breath data, whether a user is wearing the user interface of the respiratory therapy system. Generally, the respiratory therapy system may use a wide variety of operations and evaluations to determine whether a user is wearing the user interface, depending on the particular contents and format of the breath data (as well as depending on the particular implementation). For example, in some aspects, the respiratory therapy system may perform one or more preprocessing operations on the breath data (e.g., to determine the derivative(s) of the data, the average value(s) of the data, the variance(s) of the data, and the like).

[0104] In some embodiments, the respiratory therapy system may evaluate the (potentially preprocessed) breath data using one or more criteria to determine whether the user interface is currently being worn. If the breath data satisfy the one or more criteria, the respiratory therapy system may determine that a user is wearing the user interface. For example, the respiratory therapy system may compare the (potentially preprocessed) data against one or more thresholds. As one example, if the breath data comprises carbon dioxide information, the respiratory therapy system may determine whether the average concentration of carbon dioxide over the window meets or exceeds a threshold. As another example, the respiratory therapy system may process the (potentially preprocessed) breath data using one or more machine learning models to generate a prediction as to whether the user interface is being worn by a user. Additional detail for some example operations to evaluate the breath data are described and discussed below with reference to FIG. 5.

[0105] In some embodiments, at block 415, the respiratory therapy system may generate a set or plurality of predictions as to whether the user is wearing the user interface. In some embodiments, if the respiratory therapy system generates new predictions every second (based on a new window of time for each prediction), the respiratory therapy system may determine whether some number of predictions are in agreement that a user is wearing the user interface. For example, the respiratory therapy system may determine whether some number of sequential predictions (e.g., N predictions in a row) indicate that a user is wearing the user interface. As another example, the respiratory therapy system may determine whether a defined percentageof predictions (e.g., the majority (e.g., more than 50%) of predictions, from a set of N predictions) indicate that a user is wearing the user interface. Generally, the particular operations used to determine or predict whether the user is wearing the user interface may vary depending on the particular implementation.

[0106] If, at block 415, the respiratory therapy system determines that a user is not wearing the user interface, the method 400 continues to block 430. At block 430, the respiratory therapy system refrains from initiating a flow of air via the user interface. That is, the respiratory therapy system refrains from initiating or beginning respiratory therapy. In some embodiments, refraining from initiating the flow of air may correspond to refraining from modulating or changing a flow of air (e.g., if the respiratory therapy system is in a post-therapy cool-down operation). Similarly, in some embodiments, refraining from initiating the flow of air may include terminating an ongoing flow of air (e.g., if therapy was ongoing, but the respiratory therapy system has determined that no user is wearing the user interface). The method 400 then returns to block 410 to access and evaluate a new set of breath data (e.g., for the next window).

[0107] Returning to block 415, if the respiratory therapy system determines or infers, based on the breath data, that a user is wearing the user interface, the method 400 continues to block 420. At block 420, the respiratory therapy system accesses adjustment data via one or more sensors.

[0108] In some embodiments, the adjustment data may be indicative of whether a user adjusting or rearranging the user interface (e.g., sliding, shifting, pulling or pushing on, or otherwise interacting with the user interface). For example, the user may don the user interface and use one or both hands to move the user interface around until a comfortable seal is formed around their nose and / or mouth.

[0109] Generally, a wide variety of sensors may be used to generate the adjustment data. For example, in some aspects, the adjustment data comprises pressure data indicating air pressure in one or more components of the respiratory therapy system (e.g., in the user interface, in the conduit, in the flow generator, and the like). For example, the adjustment data may be collected or measured at least partially using a pressure sensor such as the pressure sensor 132 of FIG. 1. As another example, the adjustment data may comprise information about the volume or flow rate of airflow (e.g., via a conduit, via the user interface, and the like). For example, the adjustment data may be collected or measured at least partially using a flow sensor, such as the flow rate sensor 134 of FIG. 1.

[0110] As another example, the adjustment data may include information about movement of the user interface and / or conduit, in particular movement of the end of the conduit connected or proximal to the user interface (e.g., collected using one or more accelerometers embedded in or connected to the user interface and / or conduit). For example, the adjustment data may be collected or measured at least partially by a motion sensor, such as the motion sensor 138 of FIG. 1. As another example, the adjustment data may include audio data captured by a microphone that is in acoustic communication with the airflow of the respiratory therapy system. For example, the adjustment data may be collected or measured at least partially using a microphone, such as the microphone 140 of FIG. 1.

[0111] Generally, the adjustment data may include a wide variety of information (including a combination of data from multiple different sensors). In some embodiments, the adjustment data can comprise any information that may be indicative of whether a user is currently adjusting the user interface that they are wearing (as of the time when the adjustment data is captured).

[0112] In some aspects, the adjustment data corresponds to information for a point in time (e.g., the pressure, flow rate, accelerometer information, and / or other data for a specific point in time). In some aspects, the adjustment data corresponds to a window of time. For example, the adjustment data may include continuous information (e.g., an audio waveform) and / or a sequence of data points (e.g., a data point every half second), where each data point indicates the relevant adjustment information at that point in time (e.g., the pressure, flow rate, audio data, and the like). In some embodiments, the length of the window may vary depending on the particular implementation. For example, in some embodiments, the breath data may correspond to a three second window, a four second window, a five second window, and the like.

[0113] In some embodiments, if the adjustment data corresponds to a window, the window may be a sliding or rolling window (e.g., where the respiratory therapy system evaluates adjustment data in overlapping windows, such as using four second long windows evaluated every half second). In some embodiments, the windows may be tumbling (e.g., nonoverlapping), such as if the respiratory therapy system evaluates each four second window of time every four seconds. Such tumbling windows may be contiguous or non-contiguous.

[0114] In some embodiments, the adjustment data may be the same as (or may overlap with) the breath data. For example, if both the breath data and the adjustment data comprise information about the flow rate, the respiratory therapy system may evaluate the flow rate for a given window at block 415 (to determine whether a user is wearing the user interface), andmay then evaluate the same data for the given window at block 425 (to determine whether the user is adjusting the user interface). In some embodiments, the adjustment data may correspond to a subsequent window or point in time, as compared to the breath data. That is, the breath data may correspond to data collected during a first window of time (e.g., evaluated to determine whether the user is wearing the user interface) while the adjustment data may correspond to data collected during a second window of time subsequent to the first window (e.g., evaluated to determine whether the user is adjusting the user interface).

[0115] At block 425, the respiratory therapy system determines, based on the adjustment data, whether a user is adjusting the user interface of the respiratory therapy system (while wearing the user interface). Generally, the respiratory therapy system may use a wide variety of operations and evaluations to determine whether the user is adjusting the user interface, depending on the particular contents and format of the adjustment data (as well as depending on the particular implementation). For example, in some aspects, the respiratory therapy system may perform one or more preprocessing operations on the breath data (e.g., to determine the derivative(s) of the data, the average value(s) of the data, the variance(s) of the data, and the like). As another example, in some aspects, the respiratory therapy system may perform a variety of spectral analysis preprocessing operations (e.g., to preprocess acoustic data, accelerometer data, and the like).

[0116] In some embodiments, the respiratory therapy system may evaluate the (potentially preprocessed) adjustment data using one or more criteria to determine whether the user interface is currently being adjusted. If the adjustment data satisfy the one or more criteria, the respiratory therapy system may determine that a user is still adjusting the user interface. For example, the respiratory therapy system may compare the (potentially preprocessed) data against one or more thresholds. As another example, the respiratory therapy system may process the (potentially preprocessed) adjustment data using one or more machine learning models to generate a prediction as to whether the user interface is being adjusted by a user. Additional detail for some example operations to evaluate the adjustment data are described and discussed below with reference to FIG. 6.

[0117] In some embodiments, at block 425, the respiratory therapy system may generate a set or plurality of predictions as to whether the user is adjusting the user interface. In some embodiments, if the respiratory therapy system generates new predictions every second (based on a new window of time for each prediction), the respiratory therapy system may determine whether some number of predictions are in agreement that a user is adjusting the user interface. For example, the respiratory therapy system may determine whether some number of sequentialpredictions (e.g., N predictions in a row) indicate that a user is adjusting the user interface. As another example, the respiratory therapy system may determine whether a defined percentage of predictions (e.g., the majority of predictions, from a set of N predictions) indicate that a user is adjusting the user interface. In some embodiments, the respiratory therapy system determines that the user is not adjusting the user interface when a defined period or window of time with “clean breathing signal” has passed. For example, if the respiratory therapy system generates an adjustment prediction every second, block 425 may include tracking the time that has passed since the last time the prediction indicated that the user is adjusting the user interface. If the defined window (e.g., a four second window) has passed, the respiratory therapy system may conclude that the user has finished adjusting the user interface. Generally, the particular operations used to determine or predict whether the user is adjusting the user interface may vary depending on the particular implementation.

[0118] If, at block 425, the respiratory therapy system determines that a user is adjusting the user interface, the method 400 continues to block 430. As discussed above, at block 430, the respiratory therapy system refrains from initiating a flow of air via the user interface. In some embodiments, as discussed above, refraining from initiating the airflow may include refraining from initiating or beginning respiratory therapy, refraining from modulating or changing a flow of air, terminating an ongoing flow of air, and the like. The method 400 then returns to block 410 to access and evaluate a new set of breath data (e.g., for the next window).

[0119] Returning to block 425, if the respiratory therapy system determines or infers, based on the adjustment data, that a user is not adjusting, e.g., is no longer adjusting, the user interface, the method 400 continues to block 435. At block 435, the respiratory therapy system provides or initiates a flow of air via the user interface. That is, the respiratory therapy system may begin or initiate respiratory therapy. In some embodiments, providing the flow of air may include continuing to provide the airflow (e.g., continuing therapy), if therapy has already begun.

[0120] The method 400 then continues to block 440. At block 440, the respiratory therapy system determines whether the user has doffed the user interface. In some embodiments, for example, the respiratory therapy system may evaluate a variety of data (e.g., new breath and / or adjustment data), such as flow rate information for the airflow, to determine, predict, or infer whether the user has doffed the mask. In some embodiments, this information may be processed using one or more thresholds and / or machine learning models, as discussed above, to determine whether the user has doffed the mask. For example, if the pressure drops below a threshold, the respiratory therapy system may infer that the user has doffed the mask. Insome embodiments, at block 440, the respiratory therapy system may determine whether the user has manually indicated that they are finished (e.g., manually turning off the flow generator).

[0121] If, at block 440, the respiratory therapy system determines that the user has not doffed the mask, the method 400 returns to block 435 to continue providing the airflow. If, at block 440, the respiratory therapy system determines that the user has doffed the user interface, the method 400 continues to block 445. At block 445, the respiratory therapy system optionally initiates one or more post-therapy operations. For example, as discussed above, the respiratory therapy system may provide post-therapy airflow (e.g., a relatively low flow and / or pressure of air) to cool down any heated components of the respiratory therapy system (e.g., a heated conduit, a humidifier plate, and the like). In some embodiments, these post-therapy operations may be performed until they complete (e.g., for a defined period of time, or until criteria are met, such as when the humidified plate reaches a target temperature). In some embodiments, the post-therapy operations may be interruptible, such as manually by the user (e.g., if a user terminates the cool-down) or automatically in response to determining that the user as re- donned the user interface. Additional detail for some example operations to perform (and potentially terminate) post-therapy operations are described and discussed below with reference to FIG. 7.

[0122] In these ways, using the method 400, the respiratory therapy system can collect and evaluate a variety of data to automatically modulate or control the flow of air that is provided via the user interface based on predictions such as whether a user has donned the mask, whether a user has finished adjusting the mask and is ready for therapy to begin, whether the user has doffed the mask, and the like. This automatic starting and stopping of therapy can substantially improve user comfort, as well as improving the operations of the respiratory therapy system itself (e.g., enabling additional functionality that operates accurately and reliably).

[0123] FIG. 5 is a process flow diagram for a method 500 for generating and evaluating breath data features for pre-therapy operations, according to some implementations of the present disclosure. One or more steps of the method 500 can be implemented using any element or aspect of the system 100 (FIGS. 1-2) described herein. While the method 500 has been shown and described herein as occurring in a certain order, more generally, the steps of the method 500 can be performed in any suitable order. Similarly, each step may generally be optional, and may be omitted in some embodiments. In some embodiments, the method 500 provides additional detail for blocks 410 and / or 415 of FIG. 4.

[0124] At block 505, the respiratory therapy system determines a flow rate (also referredto in some aspects simply as the “flow,” and / or the “volume over time”) within the respiratory therapy system (e.g., via the user interface) over a window of time. That is, the breath data (determined at block 410 of FIG. 4) may include or be derived from the flow rate of the airflow generated, or otherwise provided, over a period of time. For example, the respiratory therapy system may determine the flow rate of air via the user interface (e.g., through the conduit) for one or more points in time over the window (e.g., every half second over a four second window). In some embodiments, the respiratory therapy system may determine the flow rate as a continuous value (or may convert periodic or discrete values at multiple timestamps to a continuous function). As discussed above, in some aspects, the flow rate may be given, for example, as a number of liters per minute.

[0125] At block 510, the respiratory therapy system generates one or more breath features based on the flow rate of the airflow. Generally, as discussed above, the respiratory therapy system may apply a variety of operations (referred to in some aspects as preprocessing operations) to generate the breath features. For example, in some embodiments, the respiratory therapy system may determine or compute the average log value of the absolute value of the flow. That is, the respiratory therapy system may compute the absolute value of the flow rate over the window (e.g., at each time step), and compute the log of this absolute value. The respiratory therapy system may then compute the average value of these logs (for each time step) to determine the log of the flow data. In some aspects, this log value of the absolute value of the flow rate may be used as a breath feature to predict whether a user is wearing the user interface. For example, experimentation has shown that the log of a breath signal (e.g., when a user is breathing through the mask) differs substantially from the log of a non-breath signal (when a user is not wearing the mask). In some aspects, in addition to or instead of using the log of the absolute value, the respiratory therapy system may similarly use the absolute value of the flow rate directly (e.g., by comparing the absolute value to a threshold, or by using the absolute value as input to a machine learning model) as a breath feature to predict whether a user is wearing the user interface.

[0126] As another example, in some embodiments, the respiratory therapy system may determine or generate an exponential scaling of the flow data. In some aspects, the exponential scaling may be defined as the average value of e^lowover the window, where e is Euler’s number and flow is the flow rate at each given time step. In some aspects, other constants (other than Euler’s number) may be used in place of e to define the exponential scaling value. In some embodiments, this exponential scaling may be used as a breath feature to predictwhether a user is wearing the user interface.

[0127] As another example, in some embodiments, the respiratory therapy system may determine or generate a sigmoid of the flow data. For example, the respiratory therapy system apply a sigmoid function to the flow data at each time step, and compute the average sigmoid value over the window. In some embodiments, this sigmoid value may be used as a breath feature to predict whether a user is wearing the user interface.

[0128] As another example, in some embodiments, the respiratory therapy system may determine or generate a signal energy of the flow data. In some embodiments, the energy of the signal (e.g., the energy of the flow rate over the window) may be defined as the area under the squared magnitude of the flow rate, over the window. In some embodiments, this energy value may be used as a breath feature to predict whether a user is wearing the user interface.

[0129] As another example, in some embodiments, the respiratory therapy system may determine or generate the signal power of the flow data. In some embodiments, the power of the signal (e.g., the power of the flow rate) is defined as the average value of the squared flow rate for each data point or time step in the window. In some embodiments, this power value may be used as a breath feature to predict whether a user is wearing the user interface.

[0130] As another example, in some embodiments, the respiratory therapy system may determine or generate a variance of the flow data. Generally, the variance of the data over the window indicates the variability of the data from the average flow rate value. In some embodiments, this variance may be used as a breath feature to predict whether a user is wearing the user interface.

[0131] As another example, in some embodiments, the respiratory therapy system may determine or generate the standard deviation of the flow data. The standard deviation generally represents the average amount of variability in the data over the window. In some embodiments, this standard deviation may be used as a breath feature to predict whether a user is wearing the user interface.

[0132] Generally, depending on the particular implementation, the respiratory therapy system may use all or a subset of the above-discussed flow rate features, and may also use additional features not discussed above.

[0133] At block 515, the respiratory therapy system determines a concentration of one or more analytes in the breath data during the window. For example, the respiratory therapy system may determine the concentration of carbon dioxide in the airflow. In some embodiments, the respiratory therapy system may determine various aspects or features of the analyte concentration, such as the average concentration over the window, the maximum and / orminimum concentration during the window, the standard deviation and / or variance of the concentration, and the like. In some embodiments, determining the analyte concentration (at block 515) may be an optional step, which may or may not be performed as part of the method 500.

[0134] At block 520, the respiratory therapy system determines sound or audio data in the breath data. For example, the respiratory therapy system may evaluate various features of the audio data, such as the maximum and / or minimum amplitude, stochasticity of the amplitude, which frequencies are present, and the like. In some aspects, as discussed above, evaluating features of the audio data may include a variety spectral and / or cepstral analyses. For example, the spectral analysis may include (i) generation of a discrete Fourier transform (DFT), such as a fast Fourier transform (FFT), optionally with a sliding window; (ii) generation of a spectrogram; (iii) generation of a short time Fourier transform (STFT); (iv) a wavelet-based analysis; or (v) any combination thereof. Additionally, in some implementations, acoustic data may be removed from analysis regions where there is strong intensity acoustic interference (e.g., from speech), which can be done based on time domain variability.

[0135] In some embodiments, block 520 may further include, in addition to or as an alternative to spectral analysis, a cepstral analysis of the acoustic data. That is, the acoustic data may be evaluated based at least in part on using cepstral analysis. For example, the cepstral analysis may include generating a mel-frequency cepstrum from the received acoustic data, and determining one or more mel-frequency cepstral coefficients (MFCC) from the generated mel-frequency cepstrum. The acoustic signature then includes the one or more MFCCs. As another example, the cepstral analysis may include generating a linear-frequency cepstrum from the received acoustic data, and determining one or more linear-frequency cepstral coefficients (LFCC) from the generated linear-frequency cepstrum. The acoustic signature then includes the one or more LFCCs. In some implementations, the one or more MFCCs and / or LFCCs are examples of features that may be extracted from the cepstra. Similar steps may be performed, where mel-spectral coefficients and / or linear- spectral coefficients are examples of features that may be extracted from the spectra. In some embodiments, evaluating the acoustic data may include generating log acoustic spectra from the received acoustic data, where the acoustic signature may include principal components of the log acoustic spectra.

[0136] In some embodiments, determining the sound data (at block 520) may be an optional step, which may or may not be performed as part of the method 500.

[0137] At block 525, the respiratory therapy system then evaluates one or more breath features using one or more thresholds and / or one or more machine learning models in order topredict whether a user is wearing the user interface. For example, the airflow features may each be compared against one or more corresponding thresholds to determine whether the feature(s) meet, exceed, or fall below the one or more threshold(s) (e.g., whether the log of the flow rate is above a threshold). In the same or in another example, the analyte concentration features may be compared against various threshold(s). In the same or in another example, the audio features may be compared against one or more threshold(s). In the same or in yet another example, one or more of the airflow features, the analyte features, and / or the sound features may be processed using one or more machine learning models (e.g., logistic regression or other classification models) to generate a prediction as to whether a user is wearing the user interface (e.g., to generate a value between zero and one indicating the probability that a user is wearing the user interface).

[0138] Generally, depending on the particular implementation, the respiratory therapy system may use all or a subset of the above-discussed features, and may also use additional features not discussed above, using a wide variety of criteria (which may include thresholds, machine learning models, and the like) in order to predict or infer whether a user is wearing the user interface.

[0139] In some embodiments, as discussed above, the respiratory therapy system may generate a binary prediction as to whether a user is wearing the user interface. In some embodiments, the respiratory therapy system may additionally or alternatively generate a continuous value (e.g., between zero and one) indicating a probability or likelihood that a user is wearing the user interface. In some embodiments, the respiratory therapy system may evaluate multiple sets of breath data (e.g., multiple windows, which may or may not overlap) to generate multiple such predictions. This sequence of predictions may then be used, by the respiratory therapy system, to determine whether a user is wearing the user interface (e.g., at block 415 of FIG. 4).

[0140] FIG. 6 is a process flow diagram for a method 600 for generating and evaluating adjustment data features, according to some implementations of the present disclosure. One or more steps of the method 600 can be implemented using any element or aspect of the system 100 (FIGS. 1-2) described herein. While the method 600 has been shown and described herein as occurring in a certain order, more generally, the steps of the method 600 can be performed in any suitable order. Similarly, each step may generally be optional, and may be omitted in some embodiments. In some embodiments, the method 600 provides additional detail for blocks 420 and / or 425 of FIG. 4.

[0141] At block 605, the respiratory therapy system determines a flow rate within therespiratory therapy system (e.g., via the user interface) over a window of time. That is, the adjustment data (determined at block 420 of FIG. 4) may include or be derived from the flow rate of the airflow generated, or otherwise provided, over a period of time. For example, the respiratory therapy system may determine the flow rate of air via the user interface (e.g., through the conduit) for one or more points in time over the window (e.g., every half second over a four second window). In some embodiments, the respiratory therapy system may determine the flow rate as a continuous value (or may convert periodic or discrete values at multiple timestamps to a continuous function). As discussed above, in some aspects, the flow rate may be given, for example, as a number of liters per minute.

[0142] At block 610, the respiratory therapy system generates one or more adjustment features based on the flow rate of the airflow. Generally, as discussed above, the respiratory therapy system may apply a variety of operations (referred to in some aspects as preprocessing operations) to generate the adjustment features.

[0143] For example, in some embodiments, the respiratory therapy system may determine or compute the entropy of the flow data during the window. In some aspects, this entropy value may be used as an adjustment feature to predict whether a user is adjusting the user interface.

[0144] As another example, in some embodiments, the respiratory therapy system may determine or compute the number of turning points of the flow data during the window. In some embodiments, the number of turning points may refer to the number of (local) maxima and minima during the window (e.g., the number of points where the derivative of the flow data changes sign). In some aspects, this number of local minima and maxima may be used as an adjustment feature to predict whether a user is adjusting the user interface.

[0145] As another example, in some embodiments, the respiratory therapy system may determine or compute the fractal dimension (FD) of the flow data during the window. In some aspects, the fractal dimension may be used as a complexity measure of the flow data signal. In some aspects, this fractal dimension may be used as an adjustment feature to predict whether a user is adjusting the user interface.

[0146] As another example, in some embodiments, the respiratory therapy system may determine or compute a number of points in the flow data that go against a current direction of the flow data, during the window. In some embodiments, the number of points going against the current direction can be determined by first determining the direction of the flow rate over a small window (e.g., a subset of the window of airflow data) of data points (e.g., over five samples at five timestamps). For example, the respiratory therapy system may subtract the first sample value in the subset from the last sample value in the subset to determine the directionof the small window (e.g., positive or negative). The respiratory therapy system may then determine the direction of each consecutive sample in the subset (e.g., comparing each sample to its prior sample in sequence), and then determine the total number of points, in the subset, which have a direction opposite to the overall direction of the subset. In some embodiments, the respiratory therapy system performs this operation for each small window in the larger window (e.g., for each set of five data points in the four second window, with no overlap), and averages the number from each subset in order to determine an overall number of points, in the flow data, that go against the respective current direction of the flow data. In some aspects, this value may be used as an adjustment feature to predict whether a user is adjusting the user interface.

[0147] As another example, in some embodiments, the respiratory therapy system may determine or compute the number of zero crossings of the derivative of the flow data during the window. That is, the respiratory therapy system may compute the derivative of the flow data over the window (e.g., by using a finite impulse response (FIR) filter), and determine the number of times that the derivative crosses zero (e.g., goes from negative to positive, or vice versa). In some aspects, using the FIR filter to calculate the derivative may result in a derivative with a different frequency response, as compared to calculating the derivative by subtracting consecutive samples. In some aspects, this number of zero crossings for the derivative of the airflow flow rate may be used as an adjustment feature to predict whether a user is adjusting the user interface.

[0148] As another example, in some embodiments, the respiratory therapy system may determine or compute the ratio between an amplitude of the flow data and a derivative of the flow data during the window. That is, the respiratory therapy system may determine the ratio between the amplitude and derivative for one or more points during the window, and determine the average ratio for the window. In some aspects, this average ratio may be used as an adjustment feature to predict whether a user is adjusting the user interface.

[0149] As another example, in some embodiments, the respiratory therapy system may determine or compute the log of the derivative of the flow data during the window. Generally, the log may use a variety of base values, depending on the particular implementation. In some aspects, this log of the derivative of the flow data may be used as an adjustment feature to predict whether a user is adjusting the user interface.

[0150] As another example, in some embodiments, the respiratory therapy system may determine or compute number of zero crossings of the flow data during the window. That is, the respiratory therapy system may determine the number of times the flow rate goes fromnegative (e.g., flowing towards the flow generator) to positive (e.g., flowing towards the user interface) over the window. In some aspects, this value may be used as an adjustment feature to predict whether a user is adjusting the user interface.

[0151] As another example, in some embodiments, the respiratory therapy system may determine or compute amplitude of the derivative of the flow data during the window. For example, the respiratory therapy system may compute the average amplitude of the derivative. In some aspects, this value may be used as an adjustment feature to predict whether a user is adjusting the user interface.

[0152] Generally, depending on the particular implementation, the respiratory therapy system may use all or a subset of the above-discussed flow rate features, and may also use additional features not discussed above.

[0153] At block 615, the respiratory therapy system determines accelerometer data of the user interface during the window of time (as reflected in the adjustment data). For example, as discussed above, the user interface may include one or more accelerometers to measure acceleration of the user interface in one or more dimensions. In some embodiments, the respiratory therapy system determines the accelerometer data as a set of continuous values (e.g., acceleration in one or more dimensions over the window). In some embodiments, the respiratory therapy system may additionally or alternatively determine discrete accelerometer data, such as the maximum and / or minimum values, average values, standard deviation and / or variance, and the like. In some embodiments, determining the accelerometer data (at block 615) may be an optional step, which may or may not be performed as part of the method 600.

[0154] At block 620, the respiratory therapy system determines sound or audio data in the adjustment data. For example, the respiratory therapy system may evaluate various features of the audio data, such as the maximum and / or minimum amplitude, stochasticity of the amplitude, which frequencies are present, and the like. In some embodiments, determining the sound data (at block 620) may be an optional step, which may or may not be performed as part of the method 600.

[0155] At block 625, the respiratory therapy system then evaluates one or more adjustment features using one or more trained machine learning models in order to predict whether a user is adjusting the user interface. For example, one or more of the airflow features, the accelerometer afeatures, and / or the sound features may be processed using one or more machine learning models (e.g., regression models) to generate a prediction as to whether a user is adjusting the user interface (e.g., to generate a value between zero and one indicating the probability that a user is adjusting the user interface).

[0156] Generally, depending on the particular implementation, the respiratory therapy system may use all or a subset of the above-discussed features, and may also use additional features not discussed above, using a wide variety of criteria (which may include thresholds, machine learning models, and the like) in order to predict or infer whether a user is adjusting the user interface.

[0157] For example, in some embodiments, the respiratory therapy system may evaluate some or all of the above-discussed features (or others not discussed above) using one or more rules-based or threshold-based criteria. As one example, the respiratory therapy system could compare the entropy of the flow rate (generated at block 610) with a predetermined threshold, determining (or predicting) that the user is adjusting the interface if the entropy exceeds the threshold. Similar thresholds may be established for each other feature or attribute discussed above. In some embodiments, each feature may be evaluated against a corresponding predetermined threshold to generate a corresponding prediction as to whether the user is adjusting or, as the case may be, not adjusting the interface, and / or a rules-based model may be used to aggregate the threshold evaluations across multiple features to generate an overall prediction as to whether the user is adjusting, or not adjusting, the interface.

[0158] In some embodiments, as discussed above, the respiratory therapy system may generate a binary prediction as to whether a user is adjusting the user interface. In some embodiments, the respiratory therapy system may additionally or alternatively generate a continuous value (e.g., between zero and one) indicating a probability or likelihood that a user is adjusting the user interface. In some embodiments, the respiratory therapy system may evaluate multiple sets of adjustment data (e.g., multiple windows, which may or may not overlap) to generate multiple such predictions. This sequence of predictions may then be used, by the respiratory therapy system, to determine whether a user is adjusting the user interface (e.g., at block 425 of FIG. 4).

[0159] FIG. 7 is a process flow diagram for a method 700 for dynamically performing posttherapy airflow operations, according to some implementations of the present disclosure. One or more steps of the method 700 can be implemented using any element or aspect of the system 100 (FIGS. 1-2) described herein. While the method 700 has been shown and described herein as occurring in a certain order, more generally, the steps of the method 700 can be performed in any suitable order. Similarly, each step may generally be optional, and may be omitted in some embodiments. In some aspects, the method 700 is used to determine whether to continue or terminate post-therapy operations, such as discussed above with reference to block 445 of FIG. 4.

[0160] At block 705, the respiratory therapy system performs a post-therapy airflow operation. As discussed above, in some embodiments, the post-therapy airflow operation may generally include providing, using a flow generator, a flow of air via the user interface (e.g., through the conduit). In some embodiments, this post-therapy airflow operation may be triggered automatically (e.g., in response to determining or inferring that the user has doffed the user interface) and / or manually (e.g., in response to the user indicating that they are completing, or have completed, therapy). In some embodiments, as discussed above, the posttherapy airflow operation may be used for a variety of purposes, such as to facilitate drying of any condensation in the components (e.g., in the conduit and / or user interface) and / or to cool any heated components (e.g., a heated conduit and / or humidifier plate). In some embodiments, the post-therapy airflow operation generally comprises providing a relatively small volume and / or pressure of continuous airflow until one or more termination criteria are met (e.g., until a defined period of time has elapsed, until the heated components reach a target temperature, and the like).

[0161] At block 710, the respiratory therapy system accesses breath data via one or more sensors. As discussed above, in some embodiments, the breath data may be indicative of whether a user’s breath is present (e.g., whether the user is wearing the user interface of the respiratory therapy system).

[0162] Generally, a wide variety of sensors may be used to generate the breath data, as discussed above. For example, in some embodiments, the breath data may comprise the motor speed (e.g., in revolutions per minute (RPM)) of the blower motor used to provide the airflow via the user interface. In the same or in another example, in some aspects, the breath data comprises pressure data indicating air pressure in one or more components of the respiratory therapy system (e.g., in the user interface, in the conduit, in the flow generator, and the like). For example, the breath data may be collected or measured at least partially using a pressure sensor such as the pressure sensor 132 of FIG. 1. In the same or in another example, the breath data may comprise information about the flow rate of airflow (e.g., via a conduit, via the user interface, and the like). For example, the breath data may be collected or measured at least partially using a flow sensor, such as the flow rate sensor 134 of FIG. 1.

[0163] Generally, the breath data may include a wide variety of information. In some embodiments, the breath data can comprise any information that may be indicative of whether a user is currently wearing the user interface of the respiratory therapy system (as of the time when the breath data is captured). In some embodiments, the breath data collected during posttherapy operations may correspond to or include the same information as the breath data usedto dynamically initiate therapy (e.g., the breath data accessed at block 410 of FIG. 4). In other embodiments, the breath data used during post-therapy operations may differ from the breath data used prior to initiating therapy. For example, prior to initiating therapy, flow rate data may be used to detect whether the user is wearing the user interface, while during post-therapy airflow operations, motor speed data may be used to detect whether the user is wearing the user interface.

[0164] In some aspects, the breath data corresponds to information for a point in time (e.g., the motor speed, pressure, flow rate, and / or other data for a specific point in time). In some aspects, the breath data corresponds to a window of time. For example, the breath data may include continuous information (e.g., an audio waveform) and / or a sequence of data points (e.g., a point every half second), where each data point indicates the relevant breath information at that point in time (e.g., the pressure, flow rate, motor speed, and the like). In some embodiments, the length of the window may vary depending on the particular implementation. For example, in some embodiments, the breath data may correspond to a three second window, a four second window, a five second window, and the like.

[0165] In some embodiments, if the breath data corresponds to a window, the window may be a sliding or rolling window (e.g., where the respiratory therapy system evaluates breath data in overlapping windows, such as using four second long windows evaluated every half second). In some embodiments, the windows may be tumbling (e.g., non-overlapping), such as if the respiratory therapy system evaluates each four second window of time every four seconds. Such tumbling windows may be contiguous or non-contiguous.

[0166] At block 715, the respiratory therapy system determines or predicts, based on the breath data, whether a user is wearing the user interface. In some embodiments, as discussed above, the respiratory therapy system may evaluate the breath data using one or more criteria (e.g., by comparing the breath data to one or more thresholds). In some embodiments, the respiratory therapy system may process the breath data using one or more machine learning models to predict whether a user is wearing the user interface.

[0167] For example, in some embodiments, the respiratory therapy system may preprocess the breath data (e.g., to determine the standard deviation of the motor speed, the flow rate, and / or the pressure during the window of time), and compare the preprocessed data (e.g., the standard deviation, variance, or other value) to one or more thresholds. In one such embodiment, if the standard deviation (or other value) exceeds the threshold, the respiratory therapy system may determine (or infer) that a user is wearing the user interface.

[0168] In some embodiments, at block 715, the respiratory therapy system may generate a set or plurality of predictions as to whether the user is wearing the user interface. In some embodiments, if the respiratory therapy system generates new predictions every second or every half-second (based on a new window of time for each prediction), the respiratory therapy system may determine whether some number of predictions are in agreement that a user is wearing the user interface. For example, the respiratory therapy system may determine whether some number of sequential predictions (e.g., N predictions in a row) indicate that a user is wearing the user interface. As another example, the respiratory therapy system may determine whether a defined percentage of predictions (e.g., the majority of predictions, from a set of N predictions) indicate that a user is wearing the user interface. Generally, the particular operations used to determine or predict whether the user is wearing the user interface may vary depending on the particular implementation.

[0169] Generally, depending on the particular implementation, the respiratory therapy system may use all or a subset of the above-discussed breath features, and may also use additional features not discussed above, using a wide variety of criteria (which may include thresholds, machine learning models, and the like) in order to predict or infer whether a user is wearing the user interface. In some embodiments, as discussed above, the respiratory therapy system may generate a binary prediction as to whether a user is wearing the user interface. In some embodiments, the respiratory therapy system may additionally or alternatively generate a continuous value (e.g., between zero and one) indicating a probability or likelihood that a user is wearing the user interface.

[0170] If, at block 715, the respiratory therapy system determines that a user is wearing the user interface, the method 700 continues to block 720, where the respiratory therapy system stops, interrupts, or otherwise terminates the post-therapy airflow operation. That is, the respiratory therapy system may stop the flow of air associated with post-therapy airflow operation and, optionally, subsequently instigate a flow of air associated with respiratory therapy. In some embodiments, stopping the airflow may additionally or alternatively comprise modulating the airflow in other ways (e.g., reducing the pressure and / or flow rate, beginning therapeutic airflow, and the like). In some embodiments, after terminating the posttherapy airflow, the respiratory therapy system may use the method 400 to determine whether to auto-start therapy. For example, the respiratory therapy system may begin the method at block 410, or at block 420, to determine whether to initiate therapeutic airflow.

[0171] Returning to block 715, if the respiratory therapy system determines that a user is not wearing the user interface, the method 700 continues to block 725, where the respiratorytherapy system continues the post-therapy airflow operation. That is, the respiratory therapy system may refrain from stopping or otherwise modulating the airflow. As illustrated, the method 700 then returns to block 705. In this way, the respiratory therapy system can continue to monitor the breath data throughout the post-therapy operation in order to determine whether to interrupt the post-therapy airflow (e.g., to restart therapy).

[0172] Although the illustrated example depicts evaluating breath data to determine whether to interrupt the post-therapy airflow operation, in some embodiments, the respiratory therapy system may additionally or alternatively evaluate adjustment data (e.g., accelerometer data) to determine whether to interrupt post-therapy airflow. For example, based on accelerometer data, the respiratory therapy system may determine or infer whether the user is wearing or adjusting the user interface, or if the user has placed the user interface down (e.g., on the bed, or on a bedside table).

[0173] FIG. 8 is a process flow diagram for a method 800 for generating and evaluating breath data features for post-therapy operations, according to some implementations of the present disclosure. One or more steps of the method 800 can be implemented using any element or aspect of the system 100 (FIGS. 1-2) described herein. While the method 800 has been shown and described herein as occurring in a certain order, more generally, the steps of the method 800 can be performed in any suitable order. Similarly, each step may generally be optional, and may be omitted in some embodiments. In some embodiments, the method 800 provides additional detail for blocks 710 and / or 715 of FIG. 7.

[0174] At block 805, the respiratory therapy system determines a motor speed of a blower (e.g., the blower of a flow generator) over a window of time. For example, as discussed above, the respiratory therapy system may determine the RPMs of the motor at a sequence of discrete points in time (e.g., every half second) over a defined window (e.g., four seconds). In some aspects, as discussed above, the motor speed may be referred to as breath data during posttherapy operations (e.g., cool-down operations).

[0175] At block 810, the respiratory therapy system generates one or more features based on the motor speed information. For example, in some embodiments, the respiratory therapy system may perform one or more operations to determine features such as the standard deviation of the motor speed over the window, the variance of the motor speed, the average motor speed, the minimum and / or maximum motor speed, and the like. Generally, the particular motor features generated may vary depending on the particular implementation.

[0176] At block 815, the respiratory therapy system determines a flow rate of airflow within the respiratory therapy system (e.g., via the user interface) over a window of time. Forexample, the breath data may be collected or measured at least partially using a flow sensor, such as the flow rate sensor 134 of FIG. 1. That is, the breath data may include or be derived from the flow rate of the airflow generated, or otherwise provided, over a period of time. For example, the respiratory therapy system may determine the flow rate of air via the user interface (e.g., through the conduit) for one or more points in time over the window (e.g., every half second over a four second window). In some embodiments, the respiratory therapy system may determine the flow rate as a continuous value (or may convert periodic or discrete values at multiple timestamps to a continuous function). As discussed above, in some aspects, the flow rate of airflow may be given, for example, as a number of liters per minute. In some aspects, the respiratory therapy system may generate one or more flow rate features based on the airflow flow rate, as discussed above, such as computing the standard deviation, variance, average value, and the like.

[0177] At block 820, the respiratory therapy system determines pressure data indicating air pressure in one or more components of the respiratory therapy system (e.g., in the user interface, in the conduit, in the flow generator, and the like). For example, the breath data may be collected or measured at least partially using a pressure sensor such as the pressure sensor 132 of FIG. 1. In some aspects, the respiratory therapy system may generate one or more pressure features based on the airflow pressure, as discussed above, such as computing the standard deviation, variance, average value, and the like.

[0178] At block 825, the respiratory therapy system determines a concentration of one or more analytes in the breath data during the window. For example, the respiratory therapy system may determine the concentration of carbon dioxide in the airflow. In some embodiments, the respiratory therapy system may determine various aspects or features of the analyte concentration, such as the average concentration over the window, the maximum and / or minimum concentration during the window, the standard deviation and / or variance of the concentration, and the like.

[0179] At block 830, the respiratory therapy system then evaluates one or more of the breath features (e.g., the motor features, the flow rate features, the pressure features, the analyte features, and the like) using one or more thresholds and / or one or more machine learning models in order to predict whether a user is wearing the user interface. For example, as discussed above, the respiratory therapy system may compare the motor speed features (e.g., the standard deviation of the motor speed over the window) against one or more thresholds to determine whether the feature(s) satisfy one or more criteria. For example, if the standard deviation of the motor speed is above a threshold, the respiratory therapy system may determineor infer that a user is wearing the user interface.

[0180] Generally, depending on the particular implementation, the respiratory therapy system may use all or a subset of the above-discussed features, and may also use additional features not discussed above, using a wide variety of criteria (which may include thresholds, machine learning models, and the like) in order to predict or infer whether a user is wearing the user interface during the post-therapy airflow operations.

[0181] In some embodiments, as discussed above, the respiratory therapy system may generate a binary prediction as to whether a user is wearing the user interface. In some embodiments, the respiratory therapy system may additionally or alternatively generate a continuous value (e.g., between zero and one) indicating a probability or likelihood that a user is wearing the user interface. In some embodiments, the respiratory therapy system may evaluate multiple sets of breath data (e.g., multiple windows, which may or may not overlap) to generate multiple such predictions. This sequence of predictions may then be used, by the respiratory therapy system, to determine whether a user is wearing the user interface.

[0182] FIG. 9 is a process flow diagram for a method 900 for training machine learning models to evaluate breath data and / or adjustment data, according to some implementations of the present disclosure. One or more steps of the method 900 can be implemented using any element or aspect of the system 100 (FIGS. 1-2) described herein. In some embodiments, one or more steps of the method 900 may be implemented using one or more other systems (e.g., dedicated training systems). While the method 900 has been shown and described herein as occurring in a certain order, more generally, the steps of the method 900 can be performed in any suitable order. Similarly, each step may generally be optional, and may be omitted in some embodiments.

[0183] At block 905, the training system access breath data for one or more windows or points in time. As discussed above, the breath data may generally be captured via one or more sensors of one or more respiratory therapy systems. Generally, a wide variety of sensors may be used to generate the breath data. For example, in some aspects, the breath data comprises pressure data indicating air pressure in one or more components of one or more respiratory therapy systems, information about the flow rate of airflow (e.g., via a conduit, via the user interface, and the like) for one or more respiratory therapy systems, analyte concentration information from one or more respiratory therapy systems, audio data captured by one or more microphones in acoustic communication with the airflow of one or more respiratory therapy systems, and the like.

[0184] Generally, the breath data may include a wide variety of information that may be indicative of whether a user was wearing a user interface of a respiratory therapy system when the data was captured. For example, the breath data may be generated or collected by one or more respiratory therapy systems during various stages of operation. In some embodiments, at least a portion of the breath data is collected while no user is wearing the user interface of the respiratory therapy system and the system is not generating airflow (e.g., prior to the user donning the mask). In some embodiments, at least a portion of the breath data is collected while a user wears the user interface of the respiratory therapy system and the system is not generating airflow (e.g., prior to therapy). In some embodiments, at least a portion of the breath data is collected while a user wears the user interface of the respiratory therapy system and the system is generating therapeutic airflow (e.g., during therapy). In some embodiments, at least a portion of the breath data is collected while no user is wearing the user interface of the respiratory therapy system and the system is generating post-therapy airflow (e.g., during cooldown). In some embodiments, at least a portion of the breath data is collected while a user is wearing the user interface of the respiratory therapy system and the system is generating posttherapy airflow (e.g., if the user dons the mask during cool-down).

[0185] Generally, the breath data may correspond to or be collected during any phase or operation of the respiratory therapy system for which a machine learning model is desired. For example, if the training system is training a model to predict whether a user has donned the mask while the respiratory therapy system is not providing airflow, the breath data may comprise data collected while a user wears the mask with no airflow provided, as well as data collected while no user wears the mask with no airflow provided. In some embodiments, the breath data is collected or generated during a data collection phase, and is stored for subsequent use during a training phase. In some embodiments, the breath data is collected by users expressly for the purpose of training the machine learning model(s) (e.g., at the direction of data scientists). In some embodiments, the breath data is collected during ordinary operations for users of the respiratory therapy system(s) (e.g., while patients engage in the therapy normally). In some embodiments, the breath is anonymized prior to use to train the machine learning model(s).

[0186] At block 910, the training system accesses adjustment data. As discussed above, the adjustment data may generally be captured via one or more sensors of one or more respiratory therapy systems. Generally, a wide variety of sensors may be used to generate the adjustment data. For example, in some aspects, the adjustment data comprises pressure data indicating air pressure in one or more components of one or more respiratory therapy systems(e.g., in the user interface, in the conduit, in the flow generator, and the like). In the same or in another example, the adjustment data may comprise information about the flow rate of airflow (e.g., via a conduit, via the user interface, and the like). In the same or in additional examples, the adjustment data may include information about movement of the user interface (e.g., collected using one or more accelerometers embedded in or connected to the user interface), audio data captured by a microphone that is in acoustic communication with the airflow of the respiratory therapy system, and the like. Generally, the adjustment data may include a wide variety of information (including a combination of data from multiple different sensors). In some embodiments, the adjustment data can comprise any information that may be indicative of whether a user was wearing and adjusting the user interface when the adjustment data was captured.

[0187] Generally, the adjustment data may include a wide variety of information that may be indicative of whether a user was adjusting a user interface of a respiratory therapy system when the data was captured. For example, the adjustment data may be generated or collected by one or more respiratory therapy systems during various stages of operation. In some embodiments, at least a portion of the adjustment data is collected while no user is wearing the user interface of the respiratory therapy system and the system is not generating airflow (e.g., prior to the user donning the mask). In some embodiments, at least a portion of the adjustment data is collected while a user is wearing and adjusting the user interface of the respiratory therapy system and the system is not generating airflow (e.g., prior to therapy). In some embodiments, at least a portion of the adjustment data is collected while a user is wearing, but not adjusting, the user interface of the respiratory therapy system and the system is not generating airflow (e.g., prior to therapy). In some embodiments, at least a portion of the adjustment data is collected while a user is wearing and adjusting the user interface of the respiratory therapy system and the system is generating therapeutic airflow (e.g., during therapy). In some embodiments, at least a portion of the adjustment data is collected while a user is wearing, but not adjusting, the user interface of the respiratory therapy system and the system is generating therapeutic airflow (e.g., during therapy). In some embodiments, at least a portion of the adjustment data is collected while no user is wearing the user interface of the respiratory therapy system and the system is generating post-therapy airflow (e.g., during cooldown). In some embodiments, at least a portion of the adjustment data is collected while a user is wearing and adjusting the user interface of the respiratory therapy system and the system is generating post-therapy airflow (e.g., if the user dons the mask during cool-down). In some embodiments, at least a portion of the adjustment data is collected while a user is wearing, butnot adjusting, the user interface of the respiratory therapy system and the system is generating post-therapy airflow (e.g., if the user dons the mask during cool-down).

[0188] Generally, the adjustment data may correspond to or be collected during any phase or operation of the respiratory therapy system for which a machine learning model is desired. For example, if the training system is training a model to predict whether a user has finished adjusting the mask while the respiratory therapy system is not providing airflow, the adjustment data may comprise data collected while a user wears the mask and is not adjusting the mask while no airflow is provided, as well as data collected while the user wears the mask and is adjusting the mask while no airflow is provided. In some embodiments, the adjustment data is collected or generated during a data collection phase, and is stored for subsequent use during a training phase. In some embodiments, the adjustment data is collected by users expressly for the purpose of training the machine learning model(s) (e.g., at the direction of data scientists). In some embodiments, the adjustment data is collected during ordinary operations for users of the respiratory therapy system(s) (e.g., while patients engage in the therapy normally). In some embodiments, the adjustment is anonymized prior to use to train the machine learning model(s).

[0189] At block 915, the training system determines whether a user was wearing the user interface of a respiratory therapy system while the breath data was collected by the respiratory therapy system. For example, in some embodiments, the breath data may be labeled to indicate whether a user was wearing the user interface. In some embodiments, the label(s) may be manually generated (e.g., the user may manually indicate when they don the mask, or another user may observe and note when the user dons the mask). In some embodiments, the label(s) may be automatically generated (e.g., by processing video data depicting the user using one or more models or algorithms that detect whether the user is wearing the mask). In some embodiments, in addition to determining whether the user was wearing the user interface, the label(s) may further indicate what stage or operation the respiratory therapy system was in when the data was collected (e.g., pre-therapy, during therapy, post-therapy cool-down, and the like).

[0190] At block 920, the training system similarly determines whether a user was adjusting the user interface of a respiratory therapy system while the adjustment data was collected by the respiratory therapy system. For example, in some embodiments, the adjustment data may be labeled to indicate whether a user was adjustment the user interface. In some embodiments, the label(s) may be manually generated (e.g., the user may manually indicate when they have finished adjusting the mask, or another user may observe and note when the user finishes adjusting the mask). In some embodiments, the label(s) may be automatically generated (e.g.,by processing video data depicting the user using one or more models or algorithms that detect whether the user is adjustment the mask). In some embodiments, in addition to determining whether the user was adjustment the user interface, the label(s) may further indicate what stage or operation the respiratory therapy system was in when the data was collected (e.g., pretherapy, during therapy, post-therapy cool-down, and the like).

[0191] At block 925, the training system can then train one or more machine learning models based on the breath data and / or adjustment data. In some embodiments, the training system trains a different machine learning model for each stage of operation. For example, the training system may train a first model, based on the breath data, to predict whether a user has donned the user interface prior to therapy beginning. The training system may similarly train a second model, based on the adjustment data, to predict whether the user has completed adjusting the mask prior to therapy. Additionally, the training system may train a third model, based on the breath data and / or adjustment data, to predict whether the user has doffed the mask during therapy. Similarly, the training system may train a fourth model, based on the breath data and / or adjustment data, to predict whether the user has donned the mask during a post-therapy airflow operation.

[0192] Generally, training the machine learning model(s) may include a variety of operations, depending on the particular implementation and machine learning architecture being used. For example, if a logistic regression or neural network model is used, the training system may use gradient descent. In some embodiments, to train the model(s), the training system may process exemplar input data (e.g., the breath data and / or adjustment data for a given window of time) using the model to generate an output prediction (e.g., predicting whether a user was wearing and / or adjusting the mask during the given window of time). This prediction may then be compared against the ground-truth label (e.g., determined at block 915 and / or 920) for the exemplar in order to generate a loss based on the difference (e.g., a crossentropy loss). This loss may then be used to compute gradients indicating how to update the parameters of the model in order to make the output predictions more accurate.

[0193] Generally, this training process may be repeated any number of times using any number of training samples. In some embodiments, the training system repeats the training process until all exemplars have been used in one or more epochs. In some embodiments, the training system repeats this process until other terminating criteria are met, such as a defined amount of time or computational resources being spent, until a user interrupts the process, until the model(s) exhibit a desired minimum accuracy, and the like.

[0194] Once trained, the model(s) may generally be deployed for use on any suitable computing system. For example, the model(s) may be deployed to one or more respiratory therapy systems, where each respiratory therapy system uses the trained model(s) to provide automated starting and / or stopping of therapy, as discussed above.

[0195] In some embodiments, after being trained based on aggregated and anonymized data, one or more of the machine learning model(s) may be dynamically updated (e.g., finetuned) for specific users. For example, the respiratory therapy system may collect breath and / or adjustment data while the user uses the respiratory therapy system, and may fine-tune the pretrained model(s) in order to customize them for the specific user of the specific equipment. This can improve prediction accuracy in some aspects.

[0196] In some embodiments, in addition to training the machine learning model(s) based on objective measurements or determinations (e.g., based on breath data, based on whether the user was adjusting the user interface, and the like) the training system may further collect or evaluate one or more subjective measures (e.g., relating to comfort of the user(s)). For example, the system may train the model(s) to define the optimal starting time of therapy based further on subjective user comfort, rather than based solely on whether the user is actively adjusting the user interface. That is, in addition to objective adjustment data and / or objective labels indicating when the user stopped adjusting the user interface, the system may further user subjective user inputs to label the adjustment data (e.g., to indicate that adjustment is complete before or after the user actually stops adjusting the user interface) based on user preference.

[0197] In some embodiments, this period to start the therapy may also be a personalization option for the models. For example, some users may prefer therapy airflow to begin as soon as possible, while other users may prefer a delay before beginning. In some embodiments, therefore, these subjective user inputs may be used to refine or fine-tune the trained model(s) to personalize the model(s) for individual users.

[0198] FIG. 10 is a process flow diagram for a method 1000 for modulating airflow based on breath data and adjustment data, according to some implementations of the present disclosure. One or more steps of the method 1000 can be implemented using any element or aspect of the system 100 (FIGS. 1-2) described herein. While the method 1000 has been shown and described herein as occurring in a certain order, more generally, the steps of the method 1000 can be performed in any suitable order. Similarly, each step may generally be optional, and may be omitted in some embodiments.

[0199] At block 1005, breath data for a respiratory therapy system (e.g., the respiratorytherapy system 120 of FIG. 1) is accessed, the breath data indicative of whether a user is wearing a user interface (e.g., the user interface 124 of FIG. 1) of the respiratory therapy system.

[0200] At block 1010, in response to determining, based on the breath data, that the user is wearing the user interface, adjustment data for the respiratory therapy system is accessed, the adjustment data indicative of whether the user is adjusting the user interface.

[0201] At block 1015, in response to determining, based on the adjustment data, that the user is not adjusting the user interface, a flow of air via the user interface of the respiratory therapy system is modulated.Example Clauses

[0202] Clause 1: A method, comprising: accessing breath data for a respiratory therapy system, the breath data indicative of whether a user is wearing a user interface of the respiratory therapy system; in response to determining, based on the breath data, that the user is wearing the user interface, accessing adjustment data for the respiratory therapy system, the adjustment data indicative of whether the user is adjusting the user interface; and in response to determining, based on the adjustment data, that the user is not adjusting the user interface, modulating a flow of air via the user interface of the respiratory therapy system.

[0203] Clause 2: A method according to Clause 1, wherein the breath data comprises flow data indicating a flow rate of airflow of the respiratory therapy system over a window of time.

[0204] Clause 3: A method according to Clause 1 or 2, wherein determining, based on the breath data, that the user is wearing the user interface comprises: computing an average log value of an absolute value of the flow data over the window of time; and determining that the average log value satisfies one or more criteria.

[0205] Clause 4: A method according to any of Clauses 1-3, wherein determining, based on the breath data, that the user is wearing the user interface comprises at least one of: determining that an exponential scaling of the flow data satisfies one or more criteria; determining that a sigmoid of the flow data satisfies one or more criteria; determining that a signal energy of the flow data satisfies one or more criteria; determining that a signal power of the flow data satisfies one or more criteria; determining that a variance of the flow data satisfies one or more criteria; or determining that a standard deviation of the flow data satisfies one or more criteria.

[0206] Clause 5: A method according to any of Clauses 1-4, wherein the breath data comprises carbon dioxide data indicating a concentration of carbon dioxide in the respiratory therapy system.

[0207] Clause 6: A method according to any of Clauses 1-5, wherein the breath data comprises audio data captured by a microphone in acoustic communication with airflow of the respiratory therapy system.

[0208] Clause 7: A method according to any of Clauses 1-6, wherein the adjustment data comprises flow data indicating a flow rate of airflow of the respiratory therapy system over a window of time.

[0209] Clause 8: A method according to Clause 7, wherein determining, based on the adjustment data, that the user is not adjusting the user interface comprises computing an entropy of the flow data over the window of time.

[0210] Clause 9: A method according to any of Clauses 7-8, wherein determining, based on the adjustment data, that the user is not adjusting the user interface comprises determining a number of turning points of the flow data over the window of time; and optionally processing the number of turning points using a machine learning model.

[0211] Clause 10: A method according to any of Clauses 7-9, wherein determining, based on the adjustment data, that the user is not adjusting the user interface comprises computing a fractal dimension of the flow data over the window of time; and optionally processing the fractal dimension using a machine learning model.

[0212] Clause 11: A method according to any of Clauses 7-10, wherein determining, based on the adjustment data, that the user is not adjusting the user interface comprises at least one of: analyzing a number of points in the flow data that go against a current direction of the flow data, optionally including processing the number of points in the flow data that go against a current direction of the flow data using a machine learning model; analyzing a number of zero crossings of a derivative of the flow data, optionally including processing the number of zero crossings of a derivative of the flow data using a machine learning model; analyzing a ratio between an amplitude of the flow data and a derivative of the flow data, optionally including processing the ratio between an amplitude of the flow data and a derivative of the flow data using a machine learning model; analyzing a log of the derivative of the flow data, optionally including processing the log of the derivative of the flow data using a machine learning model; analyzing a number of zero crossings of the flow data, optionally including processing the number of zero crossings of the flow data using a machine learning model; or analyzing an amplitude of the derivative of the flow data, optionally including processing the amplitude of the derivative of the flow data using a machine learning model.

[0213] Clause 12: A method according to any of Clauses 1-11, wherein the adjustment data comprises accelerometer data indicating acceleration of the user interface.

[0214] Clause 13: A method according to any of Clauses 1-12, wherein the adjustment data comprises audio data captured by a microphone in acoustic communication with airflow of the respiratory therapy system.

[0215] Clause 14: A method according to any of Clauses 1-13, further comprising: accessing second breath data for the respiratory therapy system; and in response to determining, based on the breath data, that the user is not wearing the user interface refraining from modulating the flow of air via the user interface of the respiratory therapy system.

[0216] Clause 15: A method according to any of Clauses 1-14, further comprising: accessing second adjustment data for the respiratory therapy system; and in response to determining, based on the adjustment data, that the user is adjusting the user interface, refraining from modulating the flow of air via the user interface of the respiratory therapy system.

[0217] Clause 16: A method according to any of Clauses 1-15, further comprising: during a post-therapy airflow operation of the respiratory therapy system, accessing second breath data indicative of whether the user is wearing the user interface; and in response to determining, based on the second breath data, that the user is wearing the user interface, stopping the posttherapy airflow operation.

[0218] Clause 17: A method according to Clause 16, wherein the second breath data comprises a motor speed of a blower of the respiratory therapy system over a window of time.

[0219] Clause 18: A method according to Clause 17, wherein determining, based on the second breath data, that the user is wearing the user interface comprises: computing a standard deviation of the motor speed over the window of time; and determining that the standard deviation satisfies one or more criteria.

[0220] Clause 19: A method according to any of Clauses 16-18, wherein the second breath data comprises at least one of (i) flow data indicating a flow rate of airflow of the respiratory therapy system, (ii) pressure data indicating an amount of pressure of the airflow, or (iii) carbon dioxide data indicating a concentration of carbon dioxide in the respiratory therapy system.

[0221] Clause 20: A method according to any of Clauses 16-19, further comprising: during the post-therapy airflow operation of the respiratory therapy system, accessing third breath data indicative of whether the user is wearing the user interface; and in response to determining, based on the third breath data, that the user is not wearing the user interface, continuing the post-therapy airflow operation.

[0222] Clause 21: A method according to any of Clauses 1-20, further comprising: during a post-therapy airflow operation of the respiratory therapy system, accessing third adjustmentdata indicative of whether the user is adjusting the user interface; and in response to determining, based on the third adjustment data, that the user is not adjusting the user interface, continuing the post-therapy airflow operation. In some aspects, the post-therapy airflow operation of this Clause may be the same as the post-therapy airflow operation referred to above in Clause 16, or may be a different post-therapy airflow operation.

[0223] Clause 22: A respiratory therapy system, comprising: one or more breath sensors configured to detect breath data indicative of whether a user is wearing a user interface; one or more adjustment sensors configured to detect adjustment data indicative of whether a user is adjusting the user interface; and a flow generator configured to modulate a flow of air via the user interface based on the breath data and the adjustment data.

[0224] Clause 23, A respiratory therapy system according to Clause 22, wherein the one or more breath sensors comprise at least one of: (i) a flow rate sensor, (ii) a pressure sensor, (iii) an analyte sensor, or (iv) a microphone.

[0225] Clause 24: A respiratory therapy system according to any of Clauses 22-23, wherein the one or more adjustment sensors comprise at least one of: (i) a flow rate sensor, (ii) a pressure sensor, (iii) a motion sensor, or (iv) a microphone.

[0226] Clause 25: A respiratory therapy system according to any of Clauses 22-24, wherein the flow generator is configured to modulate the flow of air via the user interface based at least in part on determining, based on the breath data, that a user is wearing the user interface.

[0227] Clause 26: A respiratory therapy system according to any of Clauses 22-25, wherein the flow generator is configured to modulate the flow of air via the user interface based at least in part on determining, based on the adjustment data, that a user is not adjusting the user interface.

[0228] Clause 27: A respiratory therapy system according to any of Clauses 22-26, wherein the flow generator is configured to modulate the flow of air via the user interface based at least in part on determining, based on the adjustment data, that a user is not adjusting the user interface.

[0229] Clause 28: A system, comprising: a control system comprising one or more processors; and a memory having stored thereon machine readable instructions; wherein the control system is coupled to the memory, and the machine readable instructions in the memory, when executed by at least one of the one or more processors of the control system, cause the system to perform an operation comprising: accessing breath data for a respiratory therapy system, the breath data indicative of whether a user is wearing a user interface of the respiratory therapy system; in response to determining, based on the breath data, that the user is wearingthe user interface, accessing adjustment data for the respiratory therapy system, the adjustment data indicative of whether the user is adjusting the user interface; and in response to determining, based on the adjustment data, that the user is not adjusting the user interface, modulating a flow of air via the user interface of the respiratory therapy system.

[0230] Clause 29: A system according to Clause 28, wherein the breath data comprises flow data indicating a flow rate of airflow of the respiratory therapy system over a window of time.

[0231] Clause 30: A system according to Clause 29, wherein determining, based on the breath data, that the user is wearing the user interface comprises: computing an average log value of an absolute value of the flow data over the window of time; and determining that the average log value satisfies one or more criteria.

[0232] Clause 31: A system according to any of Clauses 28-30, wherein determining, based on the breath data, that the user is wearing the user interface comprises at least one of: determining that an exponential scaling of the flow data satisfies one or more criteria; determining that a sigmoid of the flow data satisfies one or more criteria; determining that a signal energy of the flow data satisfies one or more criteria; determining that a signal power of the flow data satisfies one or more criteria; determining that a variance of the flow data satisfies one or more criteria; or determining that a standard deviation of the flow data satisfies one or more criteria.

[0233] Clause 32: A system according to any of Clauses 28-31, wherein the breath data comprises carbon dioxide data indicating a concentration of carbon dioxide in the respiratory therapy system.

[0234] Clause 33: A system according to any of Clauses 28-32, wherein the breath data comprises audio data captured by a microphone in acoustic communication with airflow of the respiratory therapy system.

[0235] Clause 34: A system according to any of Clauses 28-33, wherein the adjustment data comprises flow data indicating a flow rate of airflow of the respiratory therapy system over a window of time.

[0236] Clause 35: A system according to Clause 34, wherein determining, based on the adjustment data, that the user is not adjusting the user interface comprises computing an entropy of the flow data over the window of time.

[0237] Clause 36: A system according to any of Clauses 34-35, wherein determining, based on the adjustment data, that the user is not adjusting the user interface comprises determining a number of turning points of the flow data over the window of time; and optionally processingthe number of turning points using a machine learning model.

[0238] Clause 37: A system according to any of Clauses 34-36, wherein determining, based on the adjustment data, that the user is not adjusting the user interface comprises computing a fractal dimension of the flow data over the window of time; and optionally processing the fractal dimension using a machine learning model.

[0239] Clause 38: A system according to any of Clauses 34-37, wherein determining, based on the adjustment data, that the user is not adjusting the user interface comprises at least one of: analyzing a number of points in the flow data that go against a current direction of the flow data, optionally including processing the number of points in the flow data that go against a current direction of the flow data using a machine learning model; analyzing a number of zero crossings of a derivative of the flow data, optionally including processing the number of zero crossings of a derivative of the flow data using a machine learning model; analyzing a ratio between an amplitude of the flow data and a derivative of the flow data, optionally including processing the ratio between an amplitude of the flow data and a derivative of the flow data using a machine learning model; analyzing a log of the derivative of the flow data, optionally including processing the log of the derivative of the flow data using a machine learning model; analyzing a number of zero crossings of the flow data, optionally including processing the number of zero crossings of the flow data using a machine learning model; or analyzing an amplitude of the derivative of the flow data, optionally including processing the amplitude of the derivative of the flow data using a machine learning model.

[0240] Clause 39: A system according to any of Clauses 28-38, wherein the adjustment data comprises accelerometer data indicating acceleration of the user interface.

[0241] Clause 40: A system according to any of Clauses 28-39, wherein the adjustment data comprises audio data captured by a microphone in acoustic communication with airflow of the respiratory therapy system.

[0242] Clause 41: A system according to any of Clauses 28-40, further comprising: accessing second breath data for the respiratory therapy system; and in response to determining, based on the breath data, that the user is not wearing the user interface refraining from modulating the flow of air via the user interface of the respiratory therapy system.

[0243] Clause 42: A system according to any of Clauses 28-41, further comprising: accessing second adjustment data for the respiratory therapy system; and in response to determining, based on the adjustment data, that the user is adjusting the user interface, refraining from modulating the flow of air via the user interface of the respiratory therapy system.

[0244] Clause 43: A system according to any of Clauses 28-42, further comprising: during a post-therapy airflow operation of the respiratory therapy system, accessing second breath data indicative of whether the user is wearing the user interface; and in response to determining, based on the second breath data, that the user is wearing the user interface, stopping the posttherapy airflow operation.

[0245] Clause 44: A system according to any of Clauses 28-43, wherein the second breath data comprises a motor speed of a blower of the respiratory therapy system over a window of time.

[0246] Clause 45: A system according to any of Clauses 28-44, wherein determining, based on the second breath data, that the user is wearing the user interface comprises: computing a standard deviation of the motor speed over the window of time; and determining that the standard deviation satisfies one or more criteria.

[0247] Clause 46: A system according to any of Clauses 28-45, wherein the second breath data comprises at least one of (i) flow data indicating a flow rate of airflow of the respiratory therapy system, (ii) pressure data indicating an amount of pressure of the airflow, or (iii) carbon dioxide data indicating a concentration of carbon dioxide in the respiratory therapy system.

[0248] Clause 47: A system according to any of Clauses 28-46, further comprising: during the post-therapy airflow operation of the respiratory therapy system, accessing third breath data indicative of whether the user is wearing the user interface; and in response to determining, based on the third breath data, that the user is not wearing the user interface, continuing the post-therapy airflow operation.

[0249] Clause 48: A system according to any of Clauses 28-47 during a post-therapy airflow operation of the respiratory therapy system, accessing third adjustment data indicative of whether the user is adjusting the user interface; and in response to determining, based on the third adjustment data, that the user is not adjusting the user interface, continuing the posttherapy airflow operation.

[0250] Clause 49: A system according to Clause 28, further comprising a respiratory therapy system according to any of Clauses 22-27.

[0251] Clause 50: A system for respiratory therapy, the system comprising a control system configured to implement the method of any one of Clauses 1 to 21.

[0252] Clause 51: A computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method of any one of Clauses 1 to 21.

[0253] Clause 52: A computer program product according to Clause 51, wherein thecomputer program product is a non-transitory computer readable medium.Additional Considerations

[0254] One or more elements or aspects or steps, or any portion(s) thereof, from one or more of any of claims below can be combined with one or more elements or aspects or steps, or any portion(s) thereof, from one or more of any of the other claims below or combinations thereof, to form one or more additional implementations and / or claims of the present disclosure.

[0255] While the present disclosure has been described with reference to one or more particular embodiments or implementations, those skilled in the art will recognize that many changes may be made thereto without departing from the spirit and scope of the present disclosure. Each of these implementations and obvious variations thereof is contemplated as falling within the spirit and scope of the present disclosure. It is also contemplated that additional implementations according to aspects of the present disclosure may combine any number of features from any of the implementations described herein.

[0256] The preceding description is provided to enable any person skilled in the art to practice the various embodiments described herein. The examples discussed herein are not limiting of the scope, applicability, or embodiments set forth in the claims. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments. For example, changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For instance, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Also, features described with respect to some examples may be combined in some other examples. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth herein. In addition, the scope of the disclosure is intended to cover such an apparatus or method that is practiced using other structure, functionality, or structure and functionality in addition to, or other than, the various aspects of the disclosure set forth herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of a claim.

[0257] As used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects.

[0258] As used herein, a phrase referring to “at least one of’ a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a c c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

[0259] As used herein, the term “determining” encompasses a wide variety of actions. For example, “determining” may include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, “determining” may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Also, “determining” may include resolving, selecting, choosing, establishing and the like.

[0260] The methods disclosed herein comprise one or more steps or actions for achieving the methods. The method steps and / or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and / or use of specific steps and / or actions may be modified without departing from the scope of the claims. Further, the various operations of methods described above may be performed by any suitable means capable of performing the corresponding functions. The means may include various hardware and / or software component(s) and / or module(s), including, but not limited to a circuit, an application specific integrated circuit (ASIC), or processor. Generally, where there are operations illustrated in figures, those operations may have corresponding counterpart means-plus-function components with similar numbering.

[0261] Embodiments of the invention may be provided to end users through a cloud computing infrastructure. Cloud computing generally refers to the provision of scalable computing resources as a service over a network. More formally, cloud computing may be defined as a computing capability that provides an abstraction between the computing resource and its underlying technical architecture (e.g., servers, storage, networks), enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Thus, cloud computing allows a user to access virtual computing resources (e.g., storage, data, applications, and even complete virtualized computing systems) in “the cloud,” without regard for the underlying physical systems (or locations of those systems) used to provide the computing resources.

[0262] Typically, cloud computing resources are provided to a user on a pay-per-use basis, where users are charged only for the computing resources actually used (e.g., an amount of storage space consumed by a user or a number of virtualized systems instantiated by the user). A user can access any of the resources that reside in the cloud at any time, and from anywhere across the Internet. In context of the present invention, a user may access applications or systems (e.g., the software and / or hardware used to provide automated therapy airflow modulation) or related data available in the cloud. For example, the evaluations could be executed on a computing system in the cloud and the resulting determinations can be used, by respiratory therapy systems, to modulate the flow of air. Doing so allows a user to access this information from any computing system attached to a network connected to the cloud (e.g., the Internet).

[0263] The following claims are not intended to be limited to the embodiments shown herein, but are to be accorded the full scope consistent with the language of the claims. Within a claim, reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. No claim element is to be construed under the provisions of 35 U.S.C. § 112(f) unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.” All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.

Claims

CLAIMSWHAT IS CLAIMED IS:

1. A method, comprising: accessing breath data for a respiratory therapy system, the breath data indicative of whether a user is wearing a user interface of the respiratory therapy system; in response to determining, based on the breath data, that the user is wearing the user interface, accessing adjustment data for the respiratory therapy system, the adjustment data indicative of whether the user is adjusting the user interface; and in response to determining, based on the adjustment data, that the user is not adjusting the user interface, modulating a flow of air via the user interface of the respiratory therapy system.

2. The method of claim 1, wherein the breath data comprises flow data indicating a flow rate of airflow of the respiratory therapy system over a window of time.

3. The method of claim 2, wherein determining, based on the breath data, that the user is wearing the user interface comprises: computing an average log value of an absolute value of the flow data over the window of time; and determining that the average log value satisfies one or more criteria.

4. The method of any of claims 1-3, wherein determining, based on the breath data, that the user is wearing the user interface comprises at least one of: determining that an exponential scaling of the flow data satisfies one or more criteria; determining that a sigmoid of the flow data satisfies one or more criteria; determining that a signal energy of the flow data satisfies one or more criteria; determining that a signal power of the flow data satisfies one or more criteria; determining that a variance of the flow data satisfies one or more criteria; or determining that a standard deviation of the flow data satisfies one or more criteria.

5. The method of any of claims 1-4, wherein the breath data comprises carbon dioxide data indicating a concentration of carbon dioxide in the respiratory therapy system.

6. The method of any of claims 1-5, wherein the breath data comprises audio data captured by a microphone in acoustic communication with airflow of the respiratory therapy system.

7. The method of any of claims 1-6, wherein the adjustment data comprises flow data indicating a flow rate of airflow of the respiratory therapy system over a window of time.

8. The method of claim 7, wherein determining, based on the adjustment data, that the user is not adjusting the user interface comprises computing an entropy of the flow data over the window of time.

9. The method of any of claims 7-8, wherein determining, based on the adjustment data, that the user is not adjusting the user interface comprises determining a number of turning points of the flow data over the window of time.

10. The method of any of claims 7-9, wherein determining, based on the adjustment data, that the user is not adjusting the user interface comprises computing a fractal dimension of the flow data over the window of time.

11. The method of any of claims 7-10, wherein determining, based on the adjustment data, that the user is not adjusting the user interface comprises at least one of: analyzing a number of points in the flow data that go against a current direction of the flow data; analyzing a number of zero crossings of a derivative of the flow data; analyzing a ratio between an amplitude of the flow data and a derivative of the flow data; analyzing a log of the derivative of the flow data; analyzing a number of zero crossings of the flow data; or analyzing an amplitude of the derivative of the flow data.

12. The method of any of claims 1-11, wherein the adjustment data comprises accelerometer data indicating acceleration of the user interface.

13. The method of any of claims 1-12, wherein the adjustment data comprises audio data captured by a microphone in acoustic communication with airflow of the respiratory therapy system.

14. The method of any of claims 1-13, further comprising: accessing second breath data for the respiratory therapy system; and in response to determining, based on the breath data, that the user is not wearing the user interface refraining from modulating the flow of air via the user interface of the respiratory therapy system.

15. The method of any of claims 1-14, further comprising: accessing second adjustment data for the respiratory therapy system; and in response to determining, based on the adjustment data, that the user is adjusting the user interface, refraining from modulating the flow of air via the user interface of the respiratory therapy system.

16. The method of any of claims 1-15, further comprising: during a post-therapy airflow operation of the respiratory therapy system, accessing second breath data indicative of whether the user is wearing the user interface; and in response to determining, based on the second breath data, that the user is wearing the user interface, stopping the post-therapy airflow operation.

17. The method of claim 16, wherein the second breath data comprises a motor speed of a blower of the respiratory therapy system over a window of time.

18. The method of claim 17, wherein determining, based on the second breath data, that the user is wearing the user interface comprises: computing a standard deviation of the motor speed over the window of time; and determining that the standard deviation satisfies one or more criteria.

19. The method of any of claims 16-18, wherein the second breath data comprises at least one of (i) flow data indicating a flow rate of airflow of the respiratory therapy system, (ii) pressure data indicating an amount of pressure of the airflow, or (iii) carbon dioxide data indicating a concentration of carbon dioxide in the respiratory therapy system.

20. The method of any of claims 16-19, further comprising: during the post-therapy airflow operation of the respiratory therapy system, accessing third breath data indicative of whether the user is wearing the user interface; and in response to determining, based on the third breath data, that the user is not wearing the user interface, continuing the post-therapy airflow operation.

21. The method of any of claims 1-20, further comprising: during a post-therapy airflow operation of the respiratory therapy system, accessing third adjustment data indicative of whether the user is adjusting the user interface; and in response to determining, based on the third adjustment data, that the user is not adjusting the user interface, continuing the post-therapy airflow operation.

22. A respiratory therapy system, comprising: one or more breath sensors configured to detect breath data indicative of whether a user is wearing a user interface; one or more adjustment sensors configured to detect adjustment data indicative of whether a user is adjusting the user interface; and a flow generator configured to modulate a flow of air via the user interface based on the breath data and the adjustment data.

23. The respiratory therapy system of claim 22, wherein the one or more breath sensors comprise at least one of: (i) a flow rate sensor, (ii) a pressure sensor, (iii) an analyte sensor, or (iv) a microphone.

24. The respiratory therapy system of any of claims 22-23, wherein the one or more adjustment sensors comprise at least one of: (i) a flow rate sensor, (ii) a pressure sensor, (iii) a motion sensor, or (iv) a microphone.

25. The respiratory therapy system of any of claims 22-24, wherein the flow generator is configured to modulate the flow of air via the user interface based at least in part on determining, based on the breath data, that a user is wearing the user interface.

26. The respiratory therapy system of any of claims 22-25, wherein the flow generator is configured to modulate the flow of air via the user interface based at least in part on determining, based on the adjustment data, that a user is not adjusting the user interface.

27. The respiratory therapy system of any of claims 22-26, wherein the respiratory therapy system further comprises the user interface and a conduit connecting the flow generator and the user interface.

28. A system comprising: a control system comprising one or more processors; and a memory having stored thereon machine readable instructions; wherein the control system is coupled to the memory, and the method of any one of claims 1 to 21 is implemented when the machine readable instructions in the memory are executed by at least one of the one or more processors of the control system.

29. A system for respiratory therapy, the system comprising a control system configured to implement the method of any one of claims 1 to 21.

30. A computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method of any one of claims 1 to 21.

31. The computer program product of claim 30, wherein the computer program product is a non-transitory computer readable medium.

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