System and method for determining mask recommendations

Abstract

The method includes receiving data associated with a user during a sleep session, analyzing the received data to determine whether the user is breathing through their nostrils for a selected time period during the sleep session, and communicating a mask recommendation based at least in part on a result of the analysis.

Description

[Technical Field]

[0001] The present disclosure relates generally to systems and methods for determining an appropriate mask for a user, and more particularly to systems and methods for using a sleep session to determine a user's breathing habits and recommending a mask based on the breathing habits. [Background technology]

[0002] Many people suffer from sleep-related and / or respiratory disorders, such as periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), apnea, Cheyne-Stokes respiration (CSR), respiratory failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disorders (NMD), and chest wall disorders. These disorders are often treated with respiratory therapy systems. However, for some users, such systems are uncomfortable, difficult to use, expensive, aesthetically unpleasing, and / or the benefits associated with using the system are not recognized. As a result, some users may choose not to use respiratory therapy systems diligently unless there is an indication of the severity of their symptoms without respiratory therapy treatment. Improving comfort can significantly contribute to increasing adherence to treatment. The present disclosure aims to solve these problems and others. Summary of the Invention [Means for solving the problem]

[0003] According to some implementations of the present disclosure, a method includes receiving data associated with a user during a sleep session, analyzing the received data to determine whether the user is breathing through their nostrils for a selected time period during the sleep session, and conveying a mask recommendation based at least in part on results of the analysis.

[0004] According to some implementations of the present disclosure, a system for determining a user's breathing habits includes a substrate coupled to the user's nostrils; a memory storing machine-readable instructions; and a control system including one or more processors configured to execute the machine-readable instructions to receive data associated with the user during a sleep session from the substrate; analyze the received data to determine whether the user is breathing through their nostrils for a selected time period; and communicate a mask recommendation to the user based at least in part on results of the analysis.

[0005] The above summary is not intended to describe each implementation or every aspect of the present disclosure. Additional features and benefits of the present disclosure will be apparent from the following detailed description and drawings. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a functional block diagram of a system according to some implementations of the present disclosure. [Figure 2] 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. FIG. [Figure 3] 1 illustrates an example timeline of a sleep session, according to some implementations of the present disclosure. [Figure 4] FIG. 1 is a process flow diagram of a method for providing mask recommendations according to some implementations of the present disclosure. [Figure 5] 1 illustrates an example of a substrate according to some implementations of the present disclosure. [Figure 6] 1 illustrates an example of another substrate according to some implementations of the present disclosure. [Figure 7] 1 illustrates an example of another substrate according to some implementations of the present disclosure. [Figure 8] 1 illustrates an example of another substrate according to some implementations of the present disclosure. [Figure 9A] 1 illustrates a front view of an exemplary substrate, according to some implementations of the present disclosure. [Figure 9B] 9B shows a side view of the substrate of FIG. 9A. [Figure 10A] 1 illustrates a side view of an exemplary substrate, according to some implementations of the present disclosure. [Figure 10B] 10B shows a front view of the substrate of FIG. 10A. [Figure 11A] 1 illustrates a side view of an exemplary substrate, according to some implementations of the present disclosure. [Figure 11B] 11B shows a front view of the substrate of FIG. 11A. [Figure 12A] 1 illustrates a side view of an exemplary substrate, according to some implementations of the present disclosure. [Figure 12B] 12B shows a front view of the substrate of FIG. 12A. DETAILED DESCRIPTION OF THE INVENTION

[0007] While the present disclosure is susceptible to various modifications and alternative forms, specific implementations and embodiments of the present disclosure have been shown by way of example in the drawings and are described in detail herein. It is to be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but rather, 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.

[0008] Many people suffer from sleep-related and / or breathing disorders, including periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), apnea, Cheyne-Stokes respiration (CSR), respiratory failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disorders (NMD), and chest wall disorders.

[0009] Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing (SDB) characterized by events such as upper airway obstruction or blockage during sleep due to an abnormally small upper airway combined with the normal loss of muscle tone in the tongue, soft palate, and posterior oropharynx. More generally, apnea generally refers to pauses in breathing (obstructive sleep apnea) or cessation of respiratory function (often referred to as central sleep apnea) caused by air blockage. Typically, individuals stop breathing for approximately 15 to 30 seconds during an obstructive sleep apnea event.

[0010] Other types of apnea include hypopnea, hyperpnea, and hypercapnia. Hypopnea is generally characterized by slow or shallow breathing caused by narrowing of the airway rather than airway obstruction. Hyperpnea is generally characterized by an increase in the depth and / or rate of breathing. Hypercapnia is generally characterized by a sudden or excessive amount of carbon dioxide in the bloodstream, usually resulting from insufficient breathing.

[0011] Cheyne-Stokes respiration (CSR) is another form of sleep-disordered breathing. CSR is a disturbance in a patient's respiratory control system that results in alternating periods of waxing and waning ventilation, known as the CSR cycle. CSR is characterized by repeated deoxygenation and reaeration of arterial blood.

[0012] Obesity hyperventilation syndrome (OHS) is defined as the combination of severe obesity and chronic awake hypercapnia in the absence of other causes of hypoventilation. Symptoms include dyspnea, morning headache, and excessive daytime sleepiness.

[0013] Chronic obstructive pulmonary disease (COPD) encompasses any of a group of lower respiratory tract disorders that share certain characteristics, such as increased resistance to air movement, a prolonged expiratory phase of breathing, and loss of normal lung elasticity.

[0014] Neuromuscular disorders (NMDs) encompass a number of diseases and illnesses that impair muscle function directly through intrinsic muscle pathology or indirectly through neuropathology. Chest wall disorders are a group of chest deformities that cause inefficient coupling between the respiratory muscles and the rib cage.

[0015] These and other disorders are characterized by specific events that occur during an individual's sleep (e.g., snoring, apnea, hypopnea, restless legs, sleep disturbances, choking, increased heart rate, difficulty breathing, asthma attacks, epileptic episodes, seizures, or any combination thereof).

[0016] 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 a user during a sleep session by the total number of hours of sleep in that sleep session. An event may be, for example, a pause in breathing lasting at least 10 seconds. An AHI of less than 5 is considered normal. An AHI of 5 to less than 15 is considered to indicate mild sleep apnea. An AHI of 15 to less than 30 is considered to indicate moderate sleep apnea. An AHI of 30 or greater is considered to indicate severe sleep apnea. In children, an AHI greater than 1 is considered abnormal. Sleep apnea can be considered "controlled" when the AHI is normal or when the AHI is normal or mild. The AHI can also be used in combination with oxygen saturation to indicate the severity of obstructive sleep apnea.

[0017] When a user or patient is prescribed a respiratory therapy system to relieve symptoms associated with any of the aforementioned respiratory disorders, the user may not know what type of user interface is most appropriate for them. Some implementations of the present disclosure provide systems and methods that use a user's breathing habits to determine an appropriate user interface. Outside of the context of using a respiratory therapy system, nasal breathing is associated with many health benefits. For example, the nostrils and sinuses filter, warm, and cool air as it enters the body. The sinuses produce nitric oxide, which, when breathed, fights harmful bacteria and viruses, regulates blood pressure, and boosts the immune system. Air inhaled through the nose passes through the nasal mucosa, stimulating reflexes that control breathing. Mouth breathing bypasses this pathway, which can lead to snoring, irregular breathing, and sleep apnea. Nasal breathing can reduce high blood pressure and stress by slowing breathing.

[0018] 1, a system 100 according to some implementations of the present disclosure is shown. The system 100 includes a control system 110, a memory device 114, an electronic interface 119, one or more sensors 130, one or more user devices 170, and a substrate 190. In some implementations, the system 100 optionally further includes a respiratory treatment system 120.

[0019] The control system 110 includes one or more processors 112 (hereinafter processors 112). The control system 110 is generally used to control (e.g., operate) various components of the system 100 and / or analyze data acquired and / or generated by the components of the system 100. The processor 112 may be a general-purpose or special-purpose processor or microprocessor. While one processor 112 is shown in FIG. 1 , the control system 110 may include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.), which may reside in a single housing or may be located remotely from one another. The control system 110 may be coupled to and / or located within, for example, the housing of the user device 170 and / or the housing of one or more of the sensors 130. The control system 110 may be centralized (within one such housing) or distributed (across two or more such physically separate housings). In such implementations that include two or more housings housing the control system 110, such housings may be located proximate to and / or remote from one another.

[0020] The memory device 114 stores machine-readable instructions executable by the processor 112 of the control system 110. The memory device 114 may be any suitable computer-readable storage device or medium, such as, for example, a random or serial access memory device, a hard drive, a solid-state drive, a flash memory device, etc. Although one memory device 114 is shown in FIG. 1 , the system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 114 may be coupled to and / or located within the housing of the respiratory treatment apparatus 122, the housing of the user device 170, the housing of one or more of the sensors 130, or any combination thereof. Like the control system 110, the memory device 114 may be centralized (within one such housing) or distributed (across two or more such physically distinct housings).

[0021] In some implementations, the memory device 114 (FIG. 1) stores a user profile associated with the user. The user profile may include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more previous sleep sessions), or any combination thereof. The demographic information may include, for example, information indicative of the user's age, the user's gender, the user's race, a family history of insomnia, the user's employment status, the user's education status, the user's socioeconomic status, or any combination thereof. The medical information may 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 may 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 may include information indicative of a self-reported subjective sleep score (poor, fair, good, etc.), the user's self-reported subjective stress level, the user's self-reported subjective fatigue level, the user's self-reported subjective health status, recent life events experienced by the user, or any combination thereof.

[0022] The electronic interface 119 is configured to receive data (e.g., physiological data and / or audio data) from one or more sensors 130, which 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 or wireless connection (e.g., using an RF communication protocol, a Wi-Fi communication protocol, a Bluetooth® communication protocol, a cellular network, etc.). The electronic interface 119 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 may also include another processor and / or another memory device that are the same as or similar to the processor 112 and memory device 114 described herein. In some implementations, the electronic interface 119 is coupled to or integrated with the user device 170 and / or the substrate 190. In other implementations, the electronic interface 119 is coupled to or integrated with (eg, within the housing of) the control system 110 and / or the storage device 114 .

[0023] The substrate 190 is an electronic device that can be coupled to the user's nose. The substrate 190 can be attached to the user's nose using adhesive. The substrate 190 can be clipped to the septum of the nose. The substrate 190 can be clipped to the bridge of the nose. The substrate 190 can be coupled to a strap and tied behind the user's head. The substrate 190 can include at least one of the one or more sensors 130. The substrate 190 can include a small battery and can monitor the user's breathing to determine whether the user is breathing through their nose. In some implementations, the substrate 190 can be used to determine whether the user is breathing through their mouth. The substrate 190 can communicate with the user device 170.

[0024] As mentioned above, in some implementations, system 100 optionally includes a respiratory treatment system 120. Respiratory treatment system 120 may include a respiratory pressure therapy device 122 (referred to herein as respiratory treatment apparatus 122), a user interface 124, a conduit 126 (also referred to as tubing or air circuit), a display device 128, a humidification tank 129, or any combination thereof. In some implementations, control system 110, memory device 114, display device 128, one or more of sensors 130, and humidification tank 129 are part of respiratory treatment apparatus 122. Respiratory pressure therapy refers to the application of supplying air to the entrance of a user's airways at a controlled target pressure that is nominally positive relative to the atmosphere throughout the user's respiratory cycle (as opposed to negative pressure therapy, such as an iron lung or chest pad, for example). Respiratory treatment system 120 is typically used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0025] Respiratory treatment device 122 is typically used to generate pressurized air delivered to a user (e.g., using one or more motors driving one or more compressors). In some implementations, respiratory treatment device 122 generates a continuous, constant air pressure delivered to a user. In other implementations, respiratory treatment device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In yet other implementations, respiratory treatment device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, respiratory treatment device 122 can deliver at least about 6 cm of H2O, at least about 10 cm of H2O, at least about 20 cm of H2O, between about 6 cm of H2O and about 10 cm of H2O, between about 7 cm of H2O and about 12 cm of H2O, etc. Furthermore, respiratory treatment device 122 can deliver pressurized air at a predetermined flow rate, e.g., between about -20 L / min and about 150 L / min, while maintaining a positive pressure (relative to ambient pressure).

[0026] The user interface 124 engages a portion of the user's face and delivers pressurized air from the respiratory treatment device 122 to the user's airways to help prevent the airways from narrowing and / or closing during sleep. This may also increase the user's oxygen intake while sleeping. Depending on the therapy being applied, the user interface 124 may, for example, form a seal with an area or portion of the user's face to facilitate delivery of gas at a pressure sufficiently different from ambient pressure to effect therapy, e.g., a positive pressure of approximately 10 cm of H2O above ambient pressure. In other forms of therapy, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate delivery of a gas supply to the airways at a positive pressure of approximately 10 cm of H2O.

[0027] As shown in FIG. 2 , in some implementations, the user interface 124 is a face mask that covers the user's nose and mouth. Alternatively, the user interface 124 may be a nasal mask that provides air to the user's nose or a nasal pillow mask that delivers air directly to the user's nostrils. The user interface 124 may include multiple straps (e.g., including hook-and-loop fasteners) for positioning and / or stabilizing the interface on a portion of the user (e.g., the face) and a conformable cushion (e.g., silicone, plastic, foam, etc.) that helps provide an airtight seal between the user interface 124 and the user. The user interface 124 may also include one or more vents to allow carbon dioxide and other gases exhaled by the user 210 to escape. In other implementations, the user interface 124 is a mouthpiece (e.g., a night guard mouthpiece shaped to fit the user's teeth, a mandibular repositioning device, etc.) that directs pressurized air into the user's mouth. The type of user interface 124 used may be more effective for a particular user. For example, a user who mostly breathes through their mouth would not benefit much from a user interface that only covers the nose, and would therefore be more beneficial to the user with a face mask that covers the nose and mouth.

[0028] A conduit 126 (also called an air circuit or tubing) allows air to flow between two components of the respiratory treatment system 120, such as the respiratory treatment device 122 and the user interface 124. In some implementations, there may be separate branches of this conduit for inhalation and exhalation. In other implementations, a single conduit is used for both inhalation and exhalation.

[0029] One or more of the substrate 190, respiratory treatment device 122, user interface 124, conduit 126, display device 128, and humidification tank 129 may include one or more sensors (e.g., a pressure sensor, a flow sensor, or more generally, any of the other sensors 130 described herein) that can be used, for example, to measure the air pressure and / or flow rate of the pressurized air supplied by respiratory treatment device 122.

[0030] The display device 128 is generally used to display image(s), including still images, video, or both, and / or information related to the respiratory treatment apparatus 122. For example, the display device 128 can provide information about the status of the respiratory treatment apparatus 122 (e.g., whether the respiratory treatment apparatus 122 is on / off, the pressure of the air being supplied by the respiratory treatment apparatus 122, the temperature of the air being supplied by the respiratory treatment apparatus 122, etc.) and / or other information (e.g., a sleep score, the current date / time, personal information of the user 210, etc.). In some implementations, the display device 128 functions as a human-machine interface (HMI) that includes, as an input interface, a graphical user interface (GUI) configured to display images. The display device 128 may be an LED display, an OLED display, an LCD display, or the like. The input interface may be, for example, a 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 treatment apparatus 122.

[0031] Humidification tank 129 is coupled to or integrated with respiratory treatment device 122 and includes a reservoir of water that can be used to humidify pressurized air delivered from respiratory treatment device 122. Respiratory treatment device 122 can include a heater that heats water in humidification tank 129 to humidify the pressurized air delivered to the user. Additionally, in some implementations, conduit 126 can also include a heating element (e.g., coupled to and / or embedded in conduit 126) that heats the pressurized air delivered to the user.

[0032] Respiratory treatment system 120 can be used, for example, as a positive airway pressure (PAP) system, a continuous positive airway pressure (CPAP) system, an automatic positive airway pressure system (APAP), a bilevel or variable positive airway pressure system (BPAP or VPAP), a ventilator, or any combination thereof. A CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to a user. An APAP system automatically varies the air pressure delivered to a user based, for example, on respiratory data associated with the user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure (e.g., expiratory positive airway pressure or EPAP) that is lower than the first predetermined pressure.

[0033] Referring to FIG. 2, a portion of system 100 (FIG. 1) according to some implementations is shown. A user 210 and bedmate 220 of respiratory treatment system 120 are positioned in bed 230 and lying on mattress 232. A user interface 124 (e.g., a full-face mask) may be worn by user 210 during a sleep session. User interface 124 is fluidly coupled and / or connected to respiratory treatment device 122 via conduit 126. Respiratory treatment device 122 delivers pressurized air to user 210 via conduit 126 and user interface 124 to increase air pressure in the user's 210 throat and help prevent airway obstruction and / or narrowing during sleep. Respiratory treatment device 122 may be positioned on a nightstand 240 directly adjacent to bed 230, as shown in FIG. 2, or more generally, on any surface or structure generally adjacent to bed 230 and / or user 210.

[0034] 1 , the one or more sensors 130 of the system 100 may include a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoplethysmogram (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, a LiDAR sensor 178, or any combination thereof. Generally, each of the one or more sensors 130 is configured to output sensor data received and stored by the storage device 114 or one or more other storage devices.

[0035] Although the one or more sensors 130 are illustrated and described as including each of a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, an RF receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoplethysmogram (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, and a LiDAR sensor 178, more generally, the one or more sensors 130 may include any combination and number of the sensors described and / or illustrated herein.

[0036] The one or more sensors 130 can be used to generate, for example, physiological data, audio data, or both. The 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 and one or more sleep-related parameters associated with the user during a sleep session. The sleep-wake signal can indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-arousal, rapid eye movement (REM) stage, first non-REM stage (often referred to as “N1”), second non-REM stage (often referred to as “N2”), third non-REM stage (often referred to as “N3”), or any combination thereof. The sleep-wake signal can be time-stamped to indicate the time the user got into bed, the time the user left bed, the time the user attempted to fall asleep, etc. The sleep-wake signal can be measured by the sensor(s) 130 during a sleep session at a predetermined sampling rate, such as one sample per second, one sample per 30 seconds, one sample per minute, etc. Examples of one or more sleep-related parameters that may be determined for a user during a sleep session based on the sleep-wake signal include total time in bed, total sleep time, sleep onset latency, wake parameters after sleep onset, sleep efficiency, fragmentation index, or any combination thereof.

[0037] The physiological and / or audio data generated by one or more sensors 130 can also be used to determine a respiratory signal associated with the user during a sleep session. The respiratory signal generally indicates the user's breathing or breathing during a sleep session. The respiratory signal may indicate, for example, respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, event patterns, pressure settings of the respiratory therapy device 122, or any combination thereof. The events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak (e.g., from the user interface 124), restless legs, sleep disorder, choking, increased heart rate, labored breathing, asthma attack, epileptic episode, seizure, or any combination thereof.

[0038] Pressure sensor 132 outputs pressure data that can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. In some implementations, pressure sensor 132 is an air pressure sensor (e.g., an atmospheric pressure sensor) that generates sensor data indicative of a user's breathing (e.g., inhalation and / or exhalation) and / or ambient pressure of respiratory treatment system 120. In such implementations, pressure sensor 132 can be coupled to or integrated with respiratory treatment device 122. Pressure sensor 132 can be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof.

[0039] The flow sensor 134 outputs flow 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 flow sensor 134 is used to determine the airflow rate from the respiratory treatment device 122, the airflow rate through the conduit 126, the airflow rate through the user interface 124, or any combination thereof. In such implementations, the flow sensor 134 can be coupled to or integrated with the respiratory treatment device 122, the user interface 124, or the conduit 126. The flow sensor 134 can be, for example, a mass flow sensor such as a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot wire sensor, a vortex sensor, a membrane sensor, or any combination thereof.

[0040] The temperature sensor 136 outputs temperature data that may be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the temperature sensor 136 generates temperature data indicative of the core body temperature of the user 210 ( FIG. 2 ), the skin temperature of the user 210, the temperature of the air flowing from the respiratory treatment device 122 and / or through the conduit 126, the temperature within the user interface 124, the ambient temperature, or any combination thereof. The temperature sensor 136 may be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap temperature sensor or semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0041] The microphone 140 outputs audio data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The audio data generated by the microphone 140 can be played back as one or more sounds (e.g., sounds from the user 210) during a sleep session. The audio data from the microphone 140 can also be used to identify events experienced by the user during a sleep session (e.g., using the control system 110), as described in further detail herein. The microphone 140 can be coupled to or integrated with the respiratory treatment apparatus 122, the user interface 124, the conduit 126, or the user device 170.

[0042] The speaker 142 outputs sound waves audible to a user (e.g., user 210 of FIG. 2 ) of the system 100. The speaker 142 can be used, for example, as an alarm clock or to play alerts or messages to the user 210 (e.g., in response to an event). In some implementations, the speaker 142 can be used to communicate audio data generated by the microphone 140 to the user. The speaker 142 can be coupled to or integrated with the respiratory treatment apparatus 122, the user interface 124, the conduit 126, or the user device 170.

[0043] The microphone 140 and the speaker 142 can be used as independent devices. In some implementations, the microphone 140 and the speaker 142 may be combined into an acoustic sensor 141, as described, for example, in International Publication No. WO 2018 / 050913 (which is incorporated herein by reference in its entirety). In such implementations, the speaker 142 generates or emits sound waves at predetermined intervals, and the microphone 140 detects reflections of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency inaudible to the human ear (e.g., below 20 Hz or above about 18 kHz) so as not to disturb the sleep of the user 210 or bedmate 220 ( FIG. 2 ). The control system 110 can determine the location of the user 210 ( FIG. 2 ) and / or one or more sleep-related parameters described herein based at least in part on the data from the microphone 140 and / or the speaker 142.

[0044] In some implementations, sensor 130 includes (i) a first microphone that is identical to or similar to microphone 140 and integrated into acoustic sensor 141, and (ii) a second microphone that is identical to or similar to microphone 140 but separate and distinct from the first microphone that is integrated into acoustic sensor 141.

[0045] 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, a long-wave signal, a short-wave signal, etc.). The RF receiver 146 detects reflections of the radio waves emitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the location of the user 210 ( FIG. 2 ) and / or one or more of the sleep-related parameters described herein. Additionally, the RF receiver (either the RF receiver 146 or the RF transmitter 148, or another RF pair) can be used for wireless communication between the control system 110, the respiratory treatment device 122, one or more sensors 130, the user device 170, or any combination thereof. While the RF receiver 146 and the RF transmitter 148 are shown in FIG. 1 as separate and distinct elements, in some implementations the RF receiver 146 and the RF transmitter 148 are combined as part of the RF sensor 147. In some such implementations, the RF sensor 147 includes control circuitry. The specific form of RF communication may be WiFi, Bluetooth, etc.

[0046] In some implementations, RF sensor 147 is part of a mesh system. An example of a mesh system is a WiFi mesh system, which may include mesh nodes, mesh router(s), and mesh gateway(s), each of which may be mobile / mobile or fixed. In such implementations, the WiFi mesh system includes a WiFi router and / or a WiFi controller, and one or more satellites (e.g., access points), each of which includes an RF sensor identical or similar to RF sensor 147. The WiFi router and satellites continuously communicate with each other using WiFi signals. The WiFi mesh system can be used to generate motion data based on changes in the WiFi signal between the router and the satellite (e.g., differences in received signal strength) due to the movement of an object or person partially obstructing the signal. This motion data may indicate movement, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0047] Camera 150 outputs image data that can be played back as one or more images (e.g., still images, motion images, thermal images, or a combination thereof) that can be stored in storage device 114. The image data from camera 150 can be used by control system 110 to determine one or more of the sleep-related parameters described herein. For example, image data from camera 150 can be used to identify a user's location, determine the time that user 210 enters bed 230 (FIG. 2), and determine the time that user 210 leaves bed 230.

[0048] The infrared (IR) sensor 152 outputs infrared image data that can be played back as one or more infrared images (e.g., still images, moving images, or both) that can be stored in the storage device 114. The infrared data from the IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including the temperature of the user 210 and / or the movement of the user 210. The IR sensor 152 can also be used in combination with the camera 150 in measuring the presence, location, and / or movement of the user 210. The IR sensor 152 can detect infrared light having a wavelength of, for example, about 700 nm to about 1 mm, while the camera 150 can detect visible light having a wavelength of about 380 nm to about 740 nm.

[0049] 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, heart rate, heart rate variability, cardiac cycle, respiratory rate, inspiration amplitude, expiration amplitude, inspiration-to-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 worn by the user 210, embedded in and / or coupled to the user interface 124 and / or its associated headgear (e.g., straps, etc.).

[0050] The ECG sensor 156 outputs physiological data associated with the electrical activity of the heart of the user 210. In some implementations, the ECG sensor 156 includes one or more electrodes positioned on or around a portion of the user 210 during a 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.

[0051] The EEG sensor 158 outputs physiological data associated with the electrical activity of the brain of the user 210. In some implementations, the EEG sensor 158 includes one or more electrodes positioned on or around the scalp of the user 210 during a sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine the sleep state of the user 210 at any given time during the sleep session. In some implementations, the EEG sensor 158 can be integrated into the user interface 124 and / or its associated headgear (e.g., straps, etc.).

[0052] The capacitance sensor 160, the force sensor 162, and the strain gauge sensor 164 output data that may 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 related to electrical activity produced by one or more muscles. The oxygen sensor 168 outputs oxygen data indicative of the oxygen concentration of a gas (e.g., in the conduit 126 or at the user interface 124). The oxygen sensor 168 may be, for example, an ultrasonic oxygen sensor, an electrical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some implementations, the one or more sensors 130 also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a sphygmomanometer sensor, an oximetry sensor, or any combination thereof.

[0053] The analyte sensor 174 can be used to detect the presence of an analyte in the breath of the user 210. Data output by the analyte sensor 174 can be stored in the memory device 114 and can be 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 the mouth of the user 210 to detect analytes in the breath exhaled from the user's 210 mouth. For example, if the user interface 124 is a face mask that covers the nose and mouth of the user 210, the analyte sensor 174 can be positioned within the face mask to monitor the user's 210 mouth breathing. In other implementations, if the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 can be positioned near the nose of the user 210 to detect analytes in the breath exhaled from the user's nose. In yet another implementation, when the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 can be positioned near the mouth of the user 210. In this implementation, the analyte sensor 174 can be used to detect whether air is inadvertently leaking from the mouth of the user 210. 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 the nose or mouth. For example, if the presence of an analyte is detected by data output by the analyte sensor 174 positioned near the mouth of the user 210 or within the face mask (in implementations where the user interface 124 is a face mask), the control system 110 can use this data as an indicator that the user 210 is breathing through their mouth.

[0054] 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 face of the user 210, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the respiratory treatment device 122, etc.). Thus, in some implementations, the moisture sensor 176 can be coupled to or integrated within the user interface 124 or the conduit 126 to monitor the humidity of the pressurized air from the respiratory treatment device 122. In other implementations, the moisture sensor 176 is located near any area where humidity levels need to be monitored. The moisture sensor 176 can also be used to monitor the humidity of the air in the ambient environment surrounding the user 210, for example, in the user's bedroom.

[0055] The light detection and ranging (LiDAR) sensor 178 can be used for depth sensing. This type of optical sensor (e.g., a laser sensor) can be used to detect objects and create a three-dimensional (3D) map of a surrounding environment, such as a living space. LiDAR generally utilizes a pulsed laser to measure time of flight. LiDAR is also referred to as 3D laser scanning. In one use case of such a sensor, a fixed or mobile device (such as a smartphone) with a LiDAR sensor 166 can measure and map an area more than five meters away from the sensor. LiDAR data can be fused with point cloud data estimated, for example, by an electromagnetic RADAR sensor. The LiDAR sensor 178 can also automatically create geofences for RADAR systems by using artificial intelligence (AI) to detect and classify features in a space that may pose a problem for the RADAR system, such as glass windows (which may be highly reflective to RADAR). LiDAR can also be used to estimate a person's height and changes in height that occur when a person sits or falls, for example. LiDAR can be used to create a 3D mesh representation of the environment. In a further application, for solid surfaces through which radio waves pass (e.g., radio-transparent materials), LiDAR allows classification of different types of obstacles due to reflections from such surfaces.

[0056] 1 , any combination of one or more sensors 130 may be integrated into and / or coupled to any one or more of the components of system 100, including respiratory treatment apparatus 122, user interface 124, conduit 126, humidification tank 129, control system 110, user device 170, substrate 190, or any combination thereof. For example, microphone 140 and speaker 142 are integrated into and / or coupled to user device 170, and pressure sensor 130, flow sensor 132, and / or temperature sensor 136 are integrated into and / or coupled to substrate 190. In some implementations, at least one of the one or more sensors 130 is not coupled to the respiratory treatment device 122, the control system 110, or the user device 170, but is positioned generally adjacent to the user 210 during a 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 a nightstand, coupled to a mattress, coupled to a ceiling, etc.).

[0057] User device 170 ( FIG. 1 ) includes a display device 172. User device 170 may be, for example, a mobile device such as a smartphone, tablet, or laptop. Alternatively, user device 170 may be an external sensing system, a television (e.g., a smart television), or another smart home device (e.g., smart speaker(s), such as Google Home, Amazon Echo, or Alexa). In some implementations, the user device is a wearable device (e.g., a smart watch). Display device 172 is generally used to display image(s), including still images, moving images, or both. In some implementations, display device 172 functions as a human-machine interface (HMI) that includes a graphic user interface (GUI) configured to display image(s) and an input interface. Display device 172 may be an LED display, an OLED display, an LCD display, or the like. The input interface may be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to detect inputs made by a human user interacting with user device 170. In some implementations, one or more user devices can be used by and / or included in the system 100.

[0058] 1 as separate and distinct components of system 100, in some implementations control system 110 and / or memory device 114 are integrated into user device 170, board 190, and / or respiratory treatment apparatus 122. Alternatively, in some implementations control system 110 or portions thereof (e.g., processor 112) can be located in the cloud (e.g., integrated into a server, integrated into an Internet of Things (IoT) device, connected to the cloud, subject to edge cloud processing, etc.), on one or more servers (e.g., remote servers, local servers, etc., or any combination thereof).

[0059] Although system 100 is shown as including all of the above components, in implementations of the present disclosure, the system may include more or fewer components for generating physiological data and determining recommended notifications or actions for the user. For example, a first alternative system includes control system 110, memory device 114, and at least one of one or more sensors 130. As another example, a second alternative system includes control system 110, memory device 114, at least one of one or more sensors 130, and user device 170. As yet another example, a third alternative system includes control system 110, memory device 114, at least one of one or more sensors 130, and substrate 190. As such, any portion(s) of the components shown and described herein can be used and / or combined with one or more other components to form a variety of systems.

[0060] As used herein, a sleep session may be defined in several ways, for example, based on an initial start time and an end time. Referring to FIG. 3, an example timeline 300 of a sleep session is shown. The timeline 300 includes a time from bedtime (t bed ) and sleep onset time (t GTS ) and initial sleep time (tsleep ), the first micro-awakening MA1, the second micro-awakening MA2, and the wake-up time (t wake ) and wake-up time (t rise ) and,

[0061] 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 the duration during which a user is asleep, i.e., the sleep session has a start time and an end time, and the user does not wake up during the sleep session until the end time. That is, the time the user is awake is not included in the sleep session. From this first definition of a sleep session, if a user wakes up and falls asleep multiple times in one night, each sleep period separated by a wake period constitutes a sleep session.

[0062] Alternatively, in some implementations, a sleep session has a start time and an end time, and during a sleep session, the user may wake up without the sleep session ending as long as the continuous duration the user is awake is below a wakefulness duration threshold. The wakefulness duration threshold may be defined as a percentage of the sleep session. The wakefulness duration threshold may be, for example, about 20 percent of the sleep session, about 15 percent of the sleep session duration, about 10 percent of the sleep session duration, about 5 percent of the sleep session duration, about 2 percent of the sleep session duration, or any other threshold percentage. In some implementations, the wakefulness duration threshold is defined as, for example, about 1 hour, about 30 minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 2 minutes, or any other amount of time.

[0063] In some implementations, a sleep session is defined as the total time from when a user first goes to bed that night to when the user last gets out of bed the next morning. In other words, a sleep session can be defined as the time starting at a first time (e.g., 10:00 PM) on a first date (e.g., Monday, January 6, 2020), which may be referred to as the current night, when the user first goes to bed with the intention to sleep (as opposed to when the user first intends to watch TV or play with their smartphone before going to sleep), and ending at a second time (e.g., 7:00 AM) on a second date (e.g., Tuesday, January 7, 2020), which may be referred to as the next morning, when the user first wakes up with the intention not to go back to sleep that next morning.

[0064] In some implementations, a user may manually define the start of a sleep session and / or manually end a sleep session. For example, a user may select (e.g., by clicking or tapping) one or more user-selectable elements displayed on display device 172 of user device 170 (FIG. 1) to manually start or end a sleep session.

[0065] Referring to Figure 3, an exemplary timeline 300 of a sleep session is shown. The timeline 300 begins with bedtime (t bed ) and sleep onset time (t GTS ) and initial sleep time (t sleep ), the first micro-awakening MA1, the second micro-awakening MA2, the awakening A, and the wake-up time (t wake ) and wake-up time (t rise ) and,

[0066] bedtime t bed is associated with the time when the user first goes to bed (e.g., bed 230 in FIG. 2) (e.g., when the user lies or sits in bed) before falling asleep. bedcan be identified based on a bedtime threshold duration to distinguish between times when a user goes to bed to sleep and times when a user goes to bed for other reasons (e.g., to watch television). For example, the bedtime threshold duration can be at least about 10 minutes, at least about 20 minutes, at least about 30 minutes, at least about 45 minutes, at least about 1 hour, at least about 2 hours, etc. bed is described herein in relation to a bed, but more generally, the time of bedtime t bed may refer to the time when a user first enters any location for sleeping (e.g., couch, chair, sleeping bag, etc.).

[0067] The time of sleep onset (GTS) is the time when the user goes to bed (t bed ) is associated with the time at which a user first attempts to fall asleep after getting into bed. For example, after getting into bed, a user may engage in one or more activities to relax before trying to fall asleep (e.g., reading, watching television, listening to music, using the user device 170, etc.). The initial sleep time (t sleep ) is the time when the user first falls asleep. For example, the initial sleep time (t sleep ) may be the time when the user first entered the first non-REM sleep stage.

[0068] wake up time t wake is the time associated with the time the user wakes up without going back to sleep (as opposed to, for example, the user waking up in the middle of the night and going back to sleep). After the user initially falls asleep, they may experience one of the more involuntary micro-awakenings (e.g., micro-awakenings MA1 and MA2) that have short durations (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.). The user may wake up at a wake-up time t wake Unlike the wake-up time t , the user falls asleep again after each of the micro-awakenings MA1 and MA2. Similarly, after initially falling asleep, the user may have one or more conscious awakenings (e.g., Awakening A) (e.g., waking up to go to the bathroom, caring for a child or pet, sleepwalking, etc.). However, the user falls asleep again after Awakening A. Therefore, the wake-up time t wakemay be defined, for example, based on the wake-up threshold duration (eg, the user has been awake for 15 minutes or more, 20 minutes or more, 30 minutes or more, 1 hour or more, etc.).

[0069] Similarly, the wake-up time t rise is associated with the time when the user is out of bed with the intent of ending a sleep session (as opposed to, for example, the user getting up to go to the bathroom during the night, caring for a child or pet, sleepwalking, etc.). In other words, the wake-up time t rise is the time when the user last left bed without returning to bed until the next sleep session (e.g., the next night). Therefore, the wake-up time t rise may be defined, for example, based on a wake-up threshold duration (e.g., the user has left bed for 15 minutes or more, 20 minutes or more, 30 minutes or more, 1 hour or more, etc.). bed can also be defined based on wake threshold duration (eg, when the user has been out of bed for more than 4 hours, more than 6 hours, more than 8 hours, more than 12 hours, etc.).

[0070] As mentioned above, the user must first bed to the last t rise In some implementations, the patient may wake up and leave the bed one or more times during the night. wake and / or last wake-up time t rise The threshold duration may be identified or determined based on a predetermined threshold duration of time following an event (e.g., falling asleep or leaving bed). Such threshold duration may be customized for the user. For a typical user who goes to bed at night and wakes up and gets out of bed in the morning, the threshold duration may be any time between about 12 hours and about 18 hours (between the time the user wakes up (t)). wake ) or wake up (t rise ) and the user goes to bed (t bed ), falling asleep (t GTS ) or sleep (t sleep) can be used. A shorter threshold time (e.g., about 8 hours to about 14 hours) can be used for users who spend a lot of time in bed. The threshold time can be initially selected and / or later adjusted based on the system monitoring the user's sleep behavior.

[0071] Total time in bed (TIB) is the time from bedtime t bed From wake-up time t rise t. Total sleep time (TST) is the duration from the initial sleep time t to the wake-up time, excluding conscious or unconscious awakenings and / or microarousals in between. Typically, total sleep time (TST) will be shorter than total time in bed (TIB) (e.g., 1 minute shorter, 10 minutes shorter, 1 hour shorter, etc.). For example, referring to timeline 300 of FIG. 3, total sleep time (TST) is the duration from the initial sleep time t to the wake-up time, excluding conscious or unconscious awakenings and / or microarousals in between. sleep and alarm time t wake , but excluding the duration of the first micro-awakening MA1, the second micro-awakening MA2, and Awakening A. As shown, in this example, the total sleep time (TST) is less than the total time in bed (TIB).

[0072] In some implementations, total sleep time (TST) may be defined as total continuous sleep time (PTST). In such implementations, total continuous sleep time excludes a predetermined initial portion or period of the first non-REM stage (e.g., a light sleep stage). For example, this predetermined initial portion may be approximately 30 seconds to approximately 20 minutes, approximately 1 minute to approximately 10 minutes, approximately 3 minutes to approximately 5 minutes, etc. Total continuous sleep time is an indicator of continuous sleep and smooths the sleep-wake hypnogram. For example, when a user first falls asleep, the user may enter the first non-REM stage for a very short time (e.g., approximately 30 seconds), then return to a wakefulness stage for a short time (e.g., 1 minute), before returning to the first non-REM stage. In this example, total continuous sleep time excludes the first instance (e.g., approximately 30 seconds) of the first non-REM stage.

[0073] In some implementations, a sleep session begins at bedtime (t bed ) and wake-up time (t rise ), i.e., a sleep session is defined as the total time in bed (TIB). In some implementations, a sleep session is defined as ending at the initial sleep time (t sleep ) and wake up at the alarm time (t wake ) In some implementations, a sleep session is defined as total sleep time (TST). In some implementations, a sleep session is defined as a sleep session ending at sleep onset time (t GTS ) and wake up at the alarm time (t wake ) In some implementations, a sleep session is defined as ending at sleep onset time (t GTS ) and wake-up time (t rise ) In some implementations, a sleep session is defined as ending at bedtime (t bed ) and wake up at the alarm time (t wake ) In some implementations, a sleep session is defined as ending at an initial sleep time (t sleep ) and wake-up time (t rise ) is defined as ending in

[0074] In some implementations, one or more of the sensors 130 are used to measure bedtime (t bed ), sleep onset time (t GTS ), initial sleep time (t sleep ), one or more first micro-arousals (e.g., MA1 and MA2), wake-up time (t wake ), wake-up time (t rise ), or any combination thereof, can define a sleep session. bedmay be determined based on data generated by, for example, motion sensor 138, microphone 140, camera 150, or any combination thereof. For example, the time of sleep onset may be determined based on data from motion sensor 138 (e.g., data indicating that the user is not moving), data from camera 150 (e.g., data indicating that the user is not moving and / or data indicating that the user has turned off the lights), data from microphone 140 (e.g., data indicating that the television is turned off), data from user device 170 (e.g., data indicating that the user is no longer using user device 170), data from pressure sensor 132 and / or flow sensor 134, or any combination thereof.

[0075] 4, a method 400 for determining a mask recommendation for a user is shown, according to some implementations of the present disclosure. One or more steps of method 400 may be performed using any element or aspect of system 100 (FIGS. 1-2) described herein.

[0076] Step 402 of method 400 includes receiving and / or generating data associated with a user during a sleep session. The data may include, for example, respiratory data associated with the user, audio data associated with the user, or both respiratory and audio data. The respiratory data is data indicative of the user's breathing (e.g., respiratory rate, respiratory rate variability, ventilation, inspiratory amplitude, expiratory amplitude, and / or inspiratory-expiratory ratio) during at least a portion of the sleep session (e.g., 10% or more of the sleep session, 50% or more of the sleep session, 75% or more of the sleep session, 90% or more of the sleep session, etc.). The audio data is playable as one or more sounds (e.g., snoring, coughing, choking, breathing, pauses in breathing, labored breathing, etc.) recorded during the sleep session.

[0077] In some implementations, the respiratory data is generated by a first sensor of the one or more sensors 130, and the audio data is generated by a second sensor of the one or more sensors 130. For example, the respiratory data can be generated by the temperature sensor 136, the pressure sensor 130, and / or the flow sensor 132, and the audio data can be generated by the microphone 140. In this example, the pressure sensor 130 and / or the flow sensor 132 can be coupled to or integrated with any component or aspect of the substrate 190. The microphone 140 can be coupled to or integrated with the user device 170 and / or the substrate 190. In other implementations, the respiratory data and the audio data are generated by the same one or more sensors 130. In such implementations, the respiratory data and the audio data can be generated, for example, by the acoustic sensor 141. Data can be received from the one or more sensors 130, for example, by the electronic interface 119 and / or the user device 170 ( FIG. 1 ) described herein.

[0078] By time-stamping the respiratory and audio data, portions of the audio data can be associated with corresponding portions of the respiratory data associated with the same time interval. The moisture sensor 176 can be used to sense the humidity in the air breathed by the user. The motion sensor 138 can be used to determine whether the user is awake and / or can be used to determine the user's sleeping position.

[0079] Step 404 of method 400 includes determining, for a selected time period during the sleep session, whether the user is breathing through their nostrils based at least in part on the data received during step 402. Detecting whether the user is breathing through their nose or mouth for the selected time period may be determined based at least in part on respiratory data, audio data, moisture data, etc. For example, control system 110 may analyze data received during step 402 (e.g., data stored in memory device 114) to determine a respiratory signal associated with the user during the sleep session. Information associated with and / or describing the determined respiratory signal may be stored, for example, in memory device 114 (FIG. 1).

[0080] The selected time frame can be a percentage of the sleep session (e.g., 90% of the sleep session, 80% of the sleep session, 60% of the sleep session, etc.). The selected time frame can be the total period the user is asleep during the sleep session. That is, the selected time frame can be the TST defined in FIG. 3. In some implementations, the selected time frame is adjusted to exclude periods in which the user experienced apnea events. In some implementations, the user has a calculated AHI value, and the selected time frame is adjusted based on the AHI value. The adjustment based on the AHI value can include estimating the duration of the apnea events and multiplying this number by the AHI value to obtain an hourly correction to be made to the selected time frame. The hourly correction can be prorated based on the duration of the selected time frame. For example, if the selected time frame is two hours, the hourly correction is multiplied by two to obtain the prorated correction. If the selected time frame is 6.5 hours, multiply the hourly correction by 6.5 to get the prorated correction. If the selected time frame is 45 minutes (or 0.75 hours), multiply the hourly correction by 0.75 to get the prorated correction.

[0081] In some implementations, the user does not breathe through the nose for the entire selected time period, or the user alternates between breathing through the nose and mouth. Alternatively, in some implementations, the user breathes through the mouth for the entire selected time period. Thus, a nose breathing percentage can be determined for the selected time period. The nose breathing percentage is the percentage of the selected time period during which the user breathes through the nose. If the nose breathing percentage is greater than a threshold, the user is determined to be a nose breather. In some implementations, the threshold is 50%, 60%, 70%, 90%, etc. If the nose breathing percentage is equal to or less than the threshold, the user is determined to be a mouth breather. A nose breather is an individual who has a breathing habit of breathing through the nose, and a mouth breather is an individual who has a breathing habit of breathing through the mouth.

[0082] In some implementations, the threshold value can be adjusted based on the duration of the selected time frame. For example, if the selected time frame is on the order of minutes, the threshold value can be high (e.g., on the order of 75%, 80%, or 90%). If the selected time frame is on the order of hours, the threshold value can be low (e.g., on the order of 50%, 60%, 70%). In some implementations, the threshold value is higher when the selected time frame is on the order of hours. A higher threshold value when the selected time frame is on the order of hours indicates that a larger portion of the sleep session is captured and is therefore more representative of the user's sleep habits compared to a shorter time frame on the order of minutes.

[0083] Determining the difference between nose and mouth breathing can be based on location. For example, the substrate 190 can be positioned close to the nose to monitor airflow from the nose. If no airflow from the nose is observed within a certain period of time, mouth breathing can be inferred. The substrate 190 can monitor the moisture level around the user's nostrils. In this case, in some implementations, exhalation can be detected because exhaled air may have higher humidity than inhaled air. In some implementations, the flow sensor 134 and / or pressure sensor 132 are flexible membranes that deflect when airflow is detected. When the membrane is positioned close to the nose, the deflection of the membrane can indicate nasal breathing.

[0084] In some implementations, the membrane is a piezoelectric layer whose resistance changes based on deflection. Therefore, deflection can be used to further distinguish whether a user is breathing in and out through their nose or both their nose and mouth. For example, a membrane in a resting position exhibits a first resistance level, and a deflected membrane causes the membrane's resistance to decrease from the first resistance level. The decrease in resistance can indicate breathing through the nose. A second resistance level lower than the first resistance level can indicate the user is breathing only through the nostrils. If the membrane changes from the first resistance level to a resistance value between the first resistance level and a second resistance level, the user is determined to be breathing through both their nose and mouth. If the membrane changes from the first resistance level to a resistance value equal to or lower than the second resistance level, the user is determined to be breathing only through the nostrils.

[0085] In some implementations, the user's breathing pattern can be determined based on the data received in step 402. The user's breathing pattern can include (a) inhaling through the nostrils, inhaling through the mouth, or both, and (b) exhaling through the nostrils, exhaling through the mouth, or both. In some implementations, the breathing pattern is determined based on changes in the direction of airflow through the nostrils. In some implementations, the breathing pattern is determined based on changes in humidity. For example, sensing humidity above a humidity threshold and then below the humidity threshold within a period of time can indicate an inhale followed by an exhale. In some implementations, sensing consecutive exhalations or exhalations through the nostrils without an inhale through the nostrils can indicate an inhale through the mouth. Successive inhalations through the nostrils without an exhalation through the nostrils can indicate an exhale through the mouth.

[0086] In some implementations, the pressure levels can be used to determine breathing patterns. For example, the substrate 190 can determine an ambient pressure level between breaths. The ambient pressure level indicates a level at which the user is neither exhaling nor inhaling. During exhalation, the substrate 190 can sense a first pressure level above the ambient pressure level and determine that the user is exhaling. During inhalation, the substrate 190 can sense a second pressure level below the ambient pressure level and determine that the user is inhaling. In some implementations, the substrate 190 can sense a third pressure level between the first pressure level and the ambient pressure level and determine that the user is partially exhaling through the nostrils. The substrate 190 can sense a fourth pressure level between the ambient pressure level and the second pressure level and determine that the user is partially inhaling through the nostrils. Partial exhalation and partial inhalation can be interpreted as the user exhaling through both the nose and mouth and the user inhaling through both the nose and mouth, respectively.

[0087] In some implementations, the motion sensor 138 and / or the force sensor 162 can be used to determine one or more sleeping postures of the user. Sleeping postures include supine sleeping posture, prone sleeping posture, left sleeping posture, right sleeping posture, sleeping posture with the user's head elevated, or any combination thereof. A user's breathing habits may differ in different sleeping postures, and thus time stamping can be used to correlate the user's sleeping posture with their breathing pattern during a selected time frame. In some implementations, the user is a nose breather in certain sleeping postures and a mouth breather in other sleeping postures. In some implementations, the force sensor 162 is coupled to a gyroscope and / or accelerometer on the substrate 190, and thus a determination of the substrate 190's position in space is used to approximate the user's sleeping posture. Because the substrate 190 is attached to the user's nose, the position of the substrate 190 in space is translated into the position of the user's head. Thus, the force sensor 162 and / or the motion sensor 138 can be used to detect sleep posture as well as head and / or neck posture based on the posture of the substrate 190 .

[0088] In some implementations, the ultrasonic pulse data, the laser light data, or both can be used to determine a user's nasal congestion level. The ultrasonic pulse data, the laser light data, or both can be used to image the user's nasal cavity and determine whether there is a blockage in the nasal cavity. If the nasal congestion level is greater than a nasal congestion threshold, the received data for the sleep session is deemed unreliable. The nasal congestion threshold can be defined as the distance the ultrasonic pulse data can travel before reaching a boundary line. In some implementations, the user is determined to have a respiratory illness (e.g., the user is determined to have a cold). The user can be notified to provide data from a subsequent sleep session when their nasal congestion is relieved.

[0089] In some implementations, mask size recommendations can be determined using ultrasound pulse data, laser light data, or both. Mask size is typically based on the height and width of the user's nose. The ultrasound pulse data, laser light data, or both can be used to image the user's nasal cavity. The control system 110 can use the image of the nasal cavity to reconstruct a potential image of the user's nose shape to approximate the height and width of the user's nose. Based on the approximated height and width of the user's nose, a lookup table or mask size chart can be used to determine mask size recommendations. In one example, a nasal mask having an extra small size is recommended for a nose approximately 1.5 inches high and 1.5 inches wide, a nasal mask having a small size is recommended for a nose approximately 1.75 inches high and 1.5 inches wide, a nasal mask having a medium-small size is recommended for a nose approximately 2 inches high and 1.5 inches wide, and a nasal mask having a large size is recommended for a nose approximately 2.25 inches high and 2 inches wide.

[0090] In some implementations, mask size recommendations can be determined using a user device 170. A camera 150 coupled to the user device 170 can be used to capture image data of the user's face. In one example, the user device 170 is a user's smartphone, and the camera 150 includes at least two cameras such that a defined width between the two cameras represents a unit length for determining the size of an object in the image captured by the camera 150. The control system 110 can use an object detection algorithm to identify the user's nose and mouth in the user's facial image data. Based on the height and width of the user's nose, a lookup table can be used, as described above in connection with nasal masks, to determine a mask size recommendation. In some implementations, a full-face mask can be recommended by determining the dimensions of the user's combined nose-mouth height and mouth width. A small-sized full-face mask can be recommended if it is 3.25 inches tall and 2.75 inches wide. A medium-sized full-face mask can be recommended if it is 3.5 inches tall and 3.25 inches wide. A large full face mask is recommended at 4.25 inches in height and 3.25 inches in width.

[0091] Step 406 of method 400 includes communicating a mask recommendation. The mask recommendation can be communicated to the user's physician, the user's caregiver, the user's partner, or any combination thereof. The mask recommendation can be provided by the user device 170 based on data generated by the substrate 190. The mask recommendation can be indicated as a visual signal including a light provided on the substrate 190. For example, an LED can be provided on the substrate 190 such that a full face mask recommendation is a red light and a nasal mask recommendation is a green light. In some implementations, the LED is lit only for a nasal mask or only for a full face mask. While nasal and full face masks are used as examples, nasal pillows can also be provided.

[0092] In some implementations, the mask recommendation may include an auditory signal, a tactile signal, etc. The auditory signal may be provided by a speaker 142. The speaker 142 may be provided on a user device 172. The user device 172 may be a laptop computer, a smartphone, a smart speaker, etc.

[0093] In some implementations, the mask recommendation includes a breathing pattern of the user. In some implementations, the mask recommendation includes a breathing habit of the user. In some implementations, the mask recommendation includes a sleep position of the user. In some implementations, the mask recommendation includes a recommended sleep position of the user. The recommended sleep position of the user can be a position in which the user's breathing habit is a nasal breathing habit. In some implementations, the mask recommendation includes a mask size recommendation for the user.

[0094] In some implementations, the substrate 190 uses a motion sensor 138 to determine when a user has coupled the substrate 190 to a nostril. The substrate 190 can be turned on based on an increase in the activity of the motion sensor 138. In some implementations, the substrate 190 includes a membrane with a resistivity that changes based on whether the membrane is exposed to a humid environment. When a user exhales, the air from the nostril is more humid than the typical sealed environment of an unused substrate 190. Therefore, changes in humidity can be used to determine when the substrate 190 is coupled to the user's nostril. In some implementations, the substrate 190 includes a temperature sensor 136 for determining when the substrate 190 is coupled to the user's nostril. The temperature of the substrate's package can be lower than the temperature of the human body.

[0095] Once it is determined that the substrate 190 is coupled to the user's nostrils using any of the motion sensor 138, moisture sensor 176, temperature sensor 136, and / or one or more sensors 130, electronics on the substrate 190 can be turned on to a monitoring state so that physiological data related to the user's breathing can be collected. In some implementations, a tab is removed from the substrate 190, and thus the substrate 190 goes into a monitoring state when the tab is removed. In some implementations, the substrate 190 includes an on / off button for putting the substrate 190 into a monitoring state.

[0096] In some implementations, a machine learning algorithm trained using the received respiratory data (step 402) (e.g., using supervised or unsupervised learning) determines whether the user is breathing through their nostrils (step 404). The received respiratory data can be calibrated using instructions provided to the user. For example, the substrate 190 can be calibrated before the user uses the substrate 190 during a sleep session. The user device 170 can provide instructions to the user to perform at least one inhale-exhale pair. An inhale-exhale pair includes the user breathing in through a nostril and breathing out through a nostril. The substrate 190 can generate data associated with each inhale-exhale pair to determine the user's nasal breathing baseline. The nasal breathing baseline can include a baseline inspiratory flow rate, a baseline expiratory flow rate, a baseline hydration level, a baseline breathing volume, or any combination thereof. Thus, during step 404, the nasal breathing baseline can be compared to the respiratory data of step 402 during the sleep session to determine whether the respiratory data deviates from the nasal breathing baseline. For example, if the breathing data deviates from nasal breathing data, it can be determined that the user is breathing partially through the nose or not at all through the nose.

[0097] In some implementations, the substrate 190 can use red, yellow, or green visual alerts to indicate whether the user is a nose breather. For example, nose breathers can be indicated by a green LED, with green indicating that the user should be recommended a nasal or pillow mask. Yellow can indicate that the user is sometimes a nose breather and sometimes a mouth breather. That is, if the proportion of nose breathers is similar in magnitude to the proportion of mouth breathers during a selected time frame, yellow can indicate that the user can learn to use a nasal or pillow mask, but should be closely monitored. Red can indicate that the user is not a nose breather and should start with a full face mask. Note that these colors are used by way of example only. In some implementations, more than three colors or other color schemes can be used. For example, color schemes or themes for color-blind individuals can be provided.

[0098] In some implementations, the substrate 190 can be used in conjunction with the user interface 124 during treatment. The substrate 190 can monitor during treatment whether the user has switched from a mouth breather to a nose breather. Thus, if a user has been wearing a full face mask for several weeks and is becoming a nose breather, a nasal or pillow mask can be recommended. Recommending a smaller mask can be useful for users who may feel claustrophobic when using a full face mask. This can improve adherence to treatment and improve compliance.

[0099] FIG. 5 illustrates an example of a substrate 502 according to some implementations of the present disclosure. The substrate 502 may be similar to or the same as the substrate 190 of FIG. 1. The substrate 502 includes a cone-shaped portion 506 that is inserted into the nostrils, as shown in FIG. 5. The substrate 502 is held in place by a strap 504 so that the substrate 502 remains attached to the nose while the user sleeps. The strap 504 is depicted in FIG. 5 as looping around the user's head. In some implementations, the strap 504 may include a chin strap, with a portion of the strap 504 also looping around the user's chin. One or more sensors 130 may be provided on the substrate 502. For example, a membrane may be provided for pressure measurement, flow measurement, etc. A battery (e.g., a button cell battery) may be used to power the electronics included in the substrate 502.

[0100] 6 illustrates an example of a substrate 602 according to some implementations of the present disclosure. The substrate 602 may be similar to or the same as the substrate 190 of FIG. 1. The substrate 602 may be attached to the nose using an adhesive. A portion 604 of the substrate 602 may include an adhesive to maintain the sensing portion 606 of the substrate 602 across both nostrils of the user.

[0101] FIG. 7 illustrates an example of a substrate 702 according to some implementations of the present disclosure. The substrate 702 may be similar to or the same as the substrate 190 of FIG. 1 . Like the substrate 602, the substrate 702 is attached to the user using an adhesive at portion 704. The substrate 702 includes two sensing portions 706 and 708. The sensing portion 706 can be used to determine nose breathing, and the sensing portion 708 can be used to monitor lip movement to improve accuracy in determining whether the user is mouth breathing. In some implementations, the sensing portions 706 and 708 include microphones so that breathing sounds from the nose can be distinguished from breathing sounds from the mouth. For example, one microphone can be located near the nose in the sensing portion 706, and a second microphone can be located near the mouth in the sensing portion 708. The difference in loudness from both microphones can be used to determine whether the user is nose breathing or mouth breathing.

[0102] FIG. 8 shows an example of a substrate 802 according to some implementations of the present disclosure. The substrate 802 is similar to or the same as the substrate 190 of FIG. 1 and the substrate 602 of FIG. 6. In FIG. 8, the substrate 802 is designed to be attached downward (i.e., adjacent to the nose). The substrate 802 includes a portion 804 having an adhesive for attaching the substrate 802 to a user. The substrate 802 includes a sensing portion 806 for including one or more of the electronics and / or sensors 130 for capturing respiratory data.

[0103] FIG. 9A shows a front view of an exemplary substrate 902 according to some implementations of the present disclosure. FIG. 9B shows a side view of the substrate 902. The substrate 902 is similar to or the same as the substrate 190 of FIG. 1 . The substrate 902 includes a clip 904 that attaches to the septum. The clip 904 holds the substrate 902 in place while the user sleeps. The substrate 902 includes a sensing portion 906 that includes one or more of the electronics and / or sensors 130 for capturing respiratory data. In some implementations, the clip 904 includes a wire connecting both sensing portions 906 so that power from a battery can be shared between the electronics on the sensing portions 906.

[0104] 10A shows a side view of a substrate 1000 according to some implementations of the present disclosure, where the substrate 1000 is similar to or the same as the substrate 190. FIG. 10B shows a front view of the substrate 1000. The substrate 1000 includes a frame 1002 that clips around the bridge of the user's nose to hold the substrate 1000 in place.

[0105] 11A shows a side view of a substrate 1100 according to some implementations of the present disclosure, where the substrate 1100 is similar to or the same as the substrate 190. FIG. 11B shows a front view of the substrate 1100. The substrate 1100 includes a frame 1102 that surrounds the user's nose to maintain a sensing portion 1104 near the user's nostrils.

[0106] 12A shows a side view of a substrate 1200 according to some implementations of the present disclosure, where the substrate 1200 is similar to or the same as the substrate 190. FIG. 12B shows a front view of the substrate 1200. The substrate 1200 includes a cage 1202 that encloses the user's nose. The cage 1202 can have a portion 1204 that separates the nostrils. The cage 1202 can hold a membrane and / or other electronics for capturing respiratory data as described above.

[0107] 5-12B are provided as examples of how the substrate 190 (FIG. 1) can be coupled to the user 210 (FIG. 2) for data collection purposes. In some implementations, the substrate 190 can be incorporated into a different item worn away from the user's nose. For example, the substrate 190 can be incorporated into a head-mounted display (HMD). In another example, the substrate 190 can be incorporated into a sleep mask. The sleep mask is used to block light from the environment while the user of the sleep mask is trying to fall asleep or while the user is sleeping. The sleep mask can have a strap that covers the user's eyes and goes around the user's head to hold the sleep mask in place. The sleep mask can incorporate the substrate 190 and one or more sensors 130 to acquire data while the user is sleeping. In some implementations, the sleep mask includes a light and a speaker to help the user sleep.

[0108] In another example, the substrate 190 can be incorporated into earphones and / or earplugs. According to some implementations of the present disclosure, a user can sleep wearing the earphones, and sensors on the substrate 190 can collect sleep data. For example, the substrate 190 can use the microphone 140 ( FIG. 1 ) to listen for sounds to detect nasal congestion, breathing sounds, breathing rate, apnea, snoring, mouth breathing, nose breathing, head position, and the like. In some implementations, when two earphones are used, muting the microphone of one earphone indicates that the user's head is turned to the side, making head position easily detected. When neither earphone is muted, the back of the user's head is pressed against the bed.

[0109] One or more elements, aspects, steps or portions thereof from any one or more of claims 1-44 below may be combined with one or more elements, aspects, steps or portions thereof from any one or more other claims 1-44 or combinations thereof to form one or more further implementations and / or claims of the present disclosure.

[0110] 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 modifications are possible 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 further implementations according to various aspects of the present disclosure may combine any number of features from any of the implementations described herein.

[0111] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 072467, filed August 31, 2020, which is incorporated herein by reference in its entirety.

Claims

1. a control system including one or more processors receiving respiratory data from a temperature sensor, a pressure sensor, and / or a flow sensor and / or receiving audio data from a microphone, wherein any of the temperature sensor, the pressure sensor, and / or the flow sensor and / or the microphone are adjacent to or coupled to the user's nostrils during a sleep session; the one or more processors analyze the received data to determine a nasal breathing percentage for a selected time period during the sleep session, the nasal breathing percentage indicating a percentage of the selected time period during which the user breathes through the nostrils; the one or more processors determine that the user is a nasal breather based at least in part on the percentage of nasal breathing for the selected time period exceeding a threshold; A mask recommendation is communicated via a user device associated with the user based at least in part on results of the analysis.

2. The method of claim 1 , wherein the selected time period is at least 90% of the sleep session.

3. The method of claim 1 or claim 2, wherein the selected time period is the total time the user is asleep during the sleep session.

4. The method of claim 1 , wherein the duration of the selected time period is adjusted based at least in part on the value of the user's Apnea Hypopnea Index (AHI).

5. The method of claim 1 , wherein the mask recommendation is indicative of the user's breathing habits.

6. The method of claim 5 , wherein the breathing habits of the user are mouth breathing habits or nose breathing habits.

7. 10. The method of claim 1, further comprising: at least one of the one or more processors determining that the user is a mouth breather based at least in part on the percentage of time the nose breathing rate being below the threshold.

8. 8. The method of claim 1, further comprising: from the received data, at least one of the one or more processors determining a breathing pattern of the user, the breathing pattern including: (a) inhaling through the nostrils, inhaling through the user's mouth, or both; and (b) exhaling through the nostrils, exhaling through the mouth, or both.

9. 9. The method of claim 8, wherein the breathing pattern is determined to include inhaling through the nostril followed by exhaling through the nostril based at least in part on the received data indicative of a change in direction of airflow through the nostril.

10. 10. The method of claim 8 or claim 9, wherein, based at least in part on determining successive inhalations without exhalations through the nostrils or successive exhalations without inhalations through the nostrils, the breathing pattern is determined to include (i) inhaling through the nostrils followed by exhaling through the mouth, (ii) exhaling through the nostrils followed by inhaling through the mouth, (iii) inhaling through the mouth followed by exhaling through the nostrils, or (iv) exhaling through the mouth followed by inhaling through the nostrils.

11. 11. The method of claim 8, wherein the breathing pattern is determined to include inhaling through both the nostrils and the mouth, or exhaling through both the nostrils and the mouth, based at least in part on a pressure level surrounding the nostril being below a threshold.

12. the control system receiving additional data from a motion or force sensor; from the received additional data, at least one of the one or more processors determines one or more sleeping positions of the user during the sleep session, the sleeping positions including a supine sleeping position, a prone sleeping position, a left-sided sleeping position, a right-sided sleeping position, a sleeping position in which the user's head is elevated, or any combination thereof; The method of any one of claims 1 to 11, further comprising, for each sleep posture, at least one of the one or more processors determining a corresponding breathing habit of the user.

13. The method of claim 12 , wherein the mask recommendation further indicates a recommended sleep position for the user as any of the sleep positions for which the corresponding breathing habits of the user are nasal breathing habits.

14. at least one of the one or more processors instructing the user to perform at least one inhalation-exhalation pair, the inhalation-exhalation pair including the user inhaling through the nostril and exhaling through the nostril; and receiving data associated with the at least one inhale-exhale pair from the control system; 13. The method of claim 1, further comprising: at least one of the one or more processors determining a nasal breathing baseline for the user based at least in part on the data associated with the at least one inhale-exhale breath pair.

15. 15. The method of claim 14, wherein the mask recommendation is further based at least in part on the user's nasal breathing baseline.

16. 16. The method of claim 14 or claim 15, wherein the nasal breathing baseline of the user comprises a baseline inspiratory flow rate, a baseline expiratory flow rate, a baseline hydration level, a baseline breath volume, or any combination thereof.

17. the control system receiving imaging data including ultrasound pulse data, laser light data, or both; 17. The method of claim 1, further comprising: at least one of the one or more processors determining a nasal congestion level of the user from the imaging data; and the mask recommendation further indicating that data from a subsequent sleep session is required based at least in part on the nasal congestion level being above a nasal congestion threshold.

18. The method of any preceding claim, wherein the data received by the one or more processors further comprises moisture data.

19. The method of any one of claims 1 to 18, wherein the mask recommendation further indicates a type of user interface including a full face mask, nasal pillows, nasal mask, or any combination thereof.

20. The method of any one of claims 1 to 19, wherein the mask recommendation comprises a visual signal, an auditory signal, a tactile signal, or any combination thereof.

21. The method of claim 20 , wherein the visual signal, the auditory signal, the tactile signal, or any combination thereof is provided on the user device associated with the user.

22. 22. The method of claim 21, wherein the user device is a mobile phone associated with the user, a smart speaker associated with the user, a desktop computer associated with the user, a laptop computer associated with the user, or any combination thereof.

23. the data is received from a substrate coupled to the nostrils of the user, and the mask recommendation further includes a visual signal including a light provided on the substrate; The visual signal includes turning the light on or off and / or turning the light on in a particular color. The method according to any one of claims 1 to 22.

24. The method of any one of claims 1 to 23, wherein the control system communicates the mask recommendation to the user, the user's physician, the user's caregiver, the user's partner, or any combination thereof.

25. 25. The method of any one of claims 1 to 24, further comprising: at least one of the one or more processors determining that the user experienced an apnea event during a portion of the selected time period and experienced normal breathing during a portion of the selected time period, wherein the mask recommendation is applicable to the portion of the selected time period during which the user experienced normal breathing.

26. a control system including one or more processors; a memory storing machine-readable instructions, The control system is coupled to the memory, and when the machine-readable instructions in the memory are executed by at least one of the one or more processors of the control system, the method of any one of claims 1 to 25 is performed.

27. A system for identifying mask recommendations, the system comprising a control system configured to implement the method of any one of claims 1 to 25.

28. A computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 25.

29. 30. The computer program product of claim 28, wherein the computer program product is a non-transitory computer-readable medium.

30. 1. A system for determining a user's breathing habits, comprising: a substrate coupled to the user's nostrils, the substrate including a membrane covering at least one of the user's nostrils, the membrane deflecting based on the user's breathing; a memory storing machine-readable instructions; Executing the machine-readable instructions, receiving flow data associated with at least one of the nostrils of the user during a sleep session from the substrate; analyzing the received data to determine a nasal breathing percentage for a selected time period during the sleep session, the nasal breathing percentage indicating a percentage of the selected time period during which the user breathes through the at least one of the nostrils; determining that the user is a nasal breather based at least in part on the percentage of nasal breathing for the selected time period exceeding a threshold; and a control system including one or more processors configured to communicate a mask recommendation to the user via a visual signal including a light provided to the substrate based at least in part on the results of the analysis, the visual signal including turning the light on or off and / or turning the light on a particular color.

31. 31. The system of claim 30, wherein the membrane is configured to measure pressure associated with the user's breathing.

32. 32. A system according to claim 30 or claim 31, wherein the membrane is configured to determine whether the user is breathing through both nostrils or one nostril.

33. A system according to any one of claims 30 to 32, wherein the membrane is configured to measure the flow of air through the nostrils of the user.

34. 34. The system of any one of claims 30 to 33, wherein the substrate includes one or more sensors including a pressure sensor, a flow sensor, a temperature sensor, an acoustic sensor, an infrared sensor, a camera, a force sensor, a capacitance sensor, a piezoresistive sensor, a moisture sensor, an oxygen sensor, or any combination thereof.

35. 35. The system of any one of claims 30 to 34, wherein the substrate is coupled to an external device including a mobile phone associated with the user, a smart speaker associated with the user, a desktop computer associated with the user, a laptop computer associated with the user, or any combination thereof.

Images