System and method for monitoring driving period

Abstract

A method for determining a likelihood that an individual has or will develop a condition includes generating data associated with a plurality of driving periods during the plurality of driving periods. The individual is located within the vehicle during at least a portion of each of the plurality of driving periods. The method also includes analyzing the data and determining a risk factor of the individual associated with the condition based at least in part on the analyzed data. The data associated with the plurality of driving periods includes one or more parameters. Analyzing the data may include determining a value of each of the parameters, a change in the value of the parameter between two driving periods, and / or an average rate of change in the value of the parameter across multiple driving periods.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 357,550, filed on June 30, 2022, which is hereby incorporated by reference in its entirety. Technical Field

[0003] The present disclosure relates generally to systems and methods for monitoring driving sessions, and more particularly to systems and methods for predicting the likelihood that an individual has or will develop a condition based at least in part on data collected during one or more driving sessions. Background Art

[0004] Many individuals suffer from sleep-related and / or breathing-related disorders, such as, for example, sleep-disordered breathing (SDB), which may include obstructive sleep apnea (OSA), central sleep apnea (CSA), other types of apnea (such as mixed apnea and hypopnea), respiratory effort-related micro-arousal (RERA) and snoring. In some cases, these disorders may appear or appear more obviously when the individual is in a specific lying / sleeping posture. These individuals may also suffer from other health conditions (which may be referred to as comorbidities), such as insomnia (e.g., difficulty falling asleep, frequent or long awakenings after initially falling asleep, and / or early awakening and inability to fall asleep again), periodic limb movement disorder (PLMD), restless legs syndrome (RLS), Cheyne-Stokes respiration (CSR), respiratory insufficiency, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), rapid eye movement (REM) behavior disorder (also referred to as RBD), dream enactment behavior (DEB), hypertension, diabetes, stroke, chest wall disorder.

[0005] These disorders are often treated using respiratory therapy systems (e.g., continuous positive airway pressure (CPAP) systems) that deliver pressurized air to help prevent an individual's airway from narrowing or collapsing during sleep. It is sometimes difficult to determine whether an individual suffers from any of these conditions, or whether an individual is at risk of developing any of these conditions. Therefore, new systems and methods are needed to determine whether an individual suffers from and / or is at risk of developing any of these conditions. The present disclosure is intended to address these and other problems. Summary of the invention

[0006] According to some embodiments of the present disclosure, a method for determining the likelihood that an individual suffers from or will develop a disease includes generating data associated with the multiple driving periods during multiple driving periods. During at least a portion of each of the multiple driving periods, the individual is located in a vehicle. The method also includes analyzing the data. The method also includes determining the individual's risk factors associated with the disease based at least in part on the analyzed data. The risk factors may include the percentage likelihood that the individual will develop the disease, the estimated time period that the individual will develop the disease, the percentage likelihood that the individual currently suffers from the disease, or any combination thereof. The data associated with the multiple driving periods include one or more parameters. Analyzing the data may include determining the value of each of the parameters, the change in the value of the parameter between two driving periods, and / or the average rate of change of the value of the parameter across multiple driving periods.

[0007] According to some embodiments of the present disclosure, a system for determining the likelihood that an individual suffers from or will develop a condition includes an electronic interface, a control system, and a memory. The electronic interface is configured to receive data associated with a plurality of driving periods. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute machine-readable instructions to perform a method. The method includes analyzing the data, and determining an individual's risk factor associated with the condition based at least in part on the analyzed data. The risk factor may include a percentage likelihood that an individual will develop a condition, an estimated time period that an individual will develop a condition, a percentage likelihood that an individual currently suffers from a condition, or any combination thereof.

[0008] The above summary is not intended to represent each embodiment or every aspect of the present disclosure. Additional features and benefits of the present disclosure will become apparent from the detailed description and drawings set forth below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a functional block diagram of a system according to some embodiments of the present disclosure;

[0010] Figure 2 According to some embodiments of the present disclosure Figure 1 a perspective view of at least a portion of a system, a user, and a bed partner;

[0011] Figure 3 illustrates an exemplary timeline of a sleep session according to some embodiments of the present disclosure;

[0012] Figure 4 The present invention is illustrated in accordance with some embodiments of the present invention. Figure 3 An exemplary hypnogram associated with a sleep session of;

[0013] Figure 5Ais a perspective view of an individual operating a vehicle while fully awake according to some embodiments of the present disclosure;

[0014] Figure 5B is a perspective view of an individual operating a vehicle while showing signs of daytime fatigue according to some embodiments of the present disclosure;

[0015] Figure 5C is a perspective view of an individual operating a vehicle while exhibiting severe signs of daytime fatigue according to some embodiments of the present disclosure; and

[0016] Figure 6 is a flow chart of a method for determining the likelihood that an individual has or will develop a condition according to some embodiments of the present disclosure.

[0017] Although the present disclosure is susceptible to various modifications and alternative forms, specific embodiments and examples thereof have been shown by way of example in the drawings and will be described in detail herein. However, it should be understood that this is not intended to limit the present disclosure to the specific forms disclosed, but on the contrary, the present disclosure will cover all modifications, equivalents and alternatives falling within the spirit and scope of the present disclosure defined by the appended claims. DETAILED DESCRIPTION

[0018] The present disclosure is described with reference to the accompanying drawings, wherein the same reference numerals are used throughout the drawings to represent similar or equivalent elements. The drawings are not drawn to scale and are merely used to illustrate the present disclosure. Several aspects of the present disclosure are described below with reference to example applications for illustration.

[0019] Many individuals suffer from sleep-related and / or breathing disorders, such as sleep-disordered breathing (SDB), such as obstructive sleep apnea (OSA), central sleep apnea (CSA) and other types of apnea, respiratory effort-related arousals (RERA), snoring, Cheyne-Stokes respiration (CSR), respiratory insufficiency, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), periodic limb movement disorder (PLMD), restless legs syndrome (RLS), neuromuscular diseases (NMDs), and chest wall disorders.

[0020] Obstructive sleep apnea (OSA), a form of sleep-disordered breathing (SDB), is characterized by episodes of occlusion or obstruction of the upper airway during sleep caused by a combination of an abnormally small upper airway and loss of normal muscle tone in the tongue, soft palate, and posterior oropharyngeal wall areas. More generally, apnea generally refers to breathing cessation caused by air blockage (obstructive sleep apnea) or cessation of respiratory function (commonly known as central sleep apnea). CSA occurs when the brain temporarily stops sending signals to the muscles that control breathing. Typically, during an obstructive sleep apnea event, an individual will stop breathing for about 15 seconds to about 30 seconds.

[0021] Other types of apnea include hypopnea, hyperpnea, and hypercapnia. Hypopnea is usually characterized by slow or shallow breathing caused by narrow airways rather than airway obstruction. Hyperpnea is usually characterized by increased depth and / or rate of breathing. Hypercapnia is usually characterized by elevated or excessive carbon dioxide in the bloodstream, usually caused by inadequate breathing.

[0022] Respiratory effort-related arousals (RERA) events are typically characterized by increased respiratory effort lasting ten seconds or longer, resulting in arousals from sleep, and which do not meet the criteria for an apnea or hypopnea event. RERA is defined as a respiratory sequence characterized by increased respiratory effort, resulting in arousals from sleep, but which does not meet the criteria for an apnea or hypopnea event. These events meet the following criteria: (1) a pattern of gradually more negative esophageal pressures, terminated by an abrupt change in pressure to a lower negative level and arousal, and (2) the event lasts for 10 seconds or longer. In some embodiments, the nasal cannula / pressure transducer system is sufficient and reliable in the detection of RERA. The RERA detector can be based on an actual flow signal derived from a respiratory therapy device. For example, a flow limitation measure can be determined based on the flow signal. A microarousal measure can then be derived based on the flow limitation measure and the measure of the sudden increase in ventilation. One such method is described in WO 2008 / 138040 and US Pat. No. 9,358,353, assigned to ResMed Ltd., the disclosures of each of which are hereby incorporated by reference in their entirety.

[0023] Cheyne-Stokes respiration (CSR) is another form of sleep-disordered breathing. CSR is a disorder of the patient's respiratory controller in which there are rhythmic alternating cycles of boom-and-bust ventilation called CSR cycles. CSR is characterized by repeated deoxygenation and reoxygenation of arterial blood.

[0024] Obesity hyperventilation syndrome (OHS) is defined as the combination of severe obesity and chronic hypercapnia during wakefulness in the absence of other known causes of hypoventilation. Symptoms include dyspnea, morning headache, and excessive daytime sleepiness.

[0025] Chronic obstructive pulmonary disease (COPD) encompasses any of a group of lower airway diseases that have certain common features, such as increased resistance to the movement of air, prolonged expiratory phase of breathing, and loss of normal elasticity of the lungs. COPD encompasses a group of lower airway diseases that have certain common features, such as increased resistance to the movement of air, prolonged expiratory phase of breathing, and loss of normal elasticity of the lungs.

[0026] Neuromuscular diseases (NMDs) encompass a wide range of diseases and ailments that impair muscle function either directly through intrinsic muscle pathology or indirectly through neuropathology.Chest wall disorders are a group of thoracic deformities that result in inefficient connections between the respiratory muscles and the thorax.

[0027] These and other disorders are characterized by specific events that may occur while an individual is sleeping (e.g., snoring, apnea, hypopnea, restless legs, sleep disturbances, choking, increased heart rate, breathing difficulties, asthma attacks, epileptic seizures, convulsions, or any combination thereof).

[0028] The Apnea-Hypopnea Index (AHI) is an index used to indicate the severity of sleep apnea during a sleep session. The AHI is calculated by dividing the number of apnea and / or hypopnea events experienced by the user during a sleep session by the total number of hours slept in the sleep session. The event can be, for example, an apnea lasting at least 10 seconds. An AHI of less than 5 is considered normal. An AHI greater than or equal to 5 but less than 15 is considered an indication of mild sleep apnea. An AHI greater than or equal to 15 but less than 30 is considered an indication of moderate sleep apnea. An AHI greater than or equal to 30 is considered an indication of 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 conjunction with the oxygen desaturation level to indicate the severity of obstructive sleep apnea.

[0029] refer to Figure 1 , illustrates a system 10 according to some embodiments of the present disclosure. The system 10 may include a respiratory therapy system 100, a control system 200, a storage device 204, and one or more sensors 210. The system 10 may additionally or alternatively include a user device 260, an activity tracker 270, and a blood pressure device 280. The system 10 may be used to analyze data from multiple driving sessions of an individual in order to determine the likelihood that the individual has developed a condition (such as OSB or SDB) or will develop a condition (such as OSA or SDB).

[0030] The respiratory therapy system 100 includes a respiratory pressure therapy (RPT) device 110 (referred to herein as respiratory therapy device 110), a user interface 120 (also referred to as a mask or patient interface), a conduit 140 (also referred to as a tube or air circuit), a display device 150, and a humidifier 160. Respiratory pressure therapy refers to the application of air to the entrance of a user's airway at a controlled target pressure that is nominally positive relative to atmosphere (e.g., as opposed to negative pressure therapy such as a tank ventilator or chest plate) throughout the user's breathing cycle. The respiratory therapy system 100 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).

[0031] The respiratory therapy system 100 can be used as, for example, a ventilator or a positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an automatic positive airway pressure system (APAP), a bi-level or variable positive airway pressure system (BPAP or VPAP), or any combination thereof. The CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to the user. The APAP system automatically changes the air pressure delivered to the user based on, for example, respiratory data associated with the user. The 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) lower than the first predetermined pressure.

[0032] like Figure 2 As shown, respiratory therapy system 100 can be used to treat user 20. In this example, user 20 of respiratory therapy system 100 and bed partner 30 are located on bed 40 and lying on mattress 42. User interface 120 can be worn by user 20 during a sleep session. Respiratory therapy system 100 generally helps increase air pressure in the throat of user 20 to help prevent airway closure and / or narrowing during sleep. Respiratory therapy device 110 can be positioned in direct proximity to a user such as a bed. Figure 2 The bed 40 is shown on a nightstand 44 , or more generally, positioned on any surface or structure generally adjacent to the bed 40 and / or the user 20 .

[0033] Return to reference Figure 1, the respiratory therapy device 110 is generally used to generate pressurized air delivered to the user (e.g., using one or more motors driving one or more compressors). In some embodiments, the respiratory therapy device 110 generates a continuous constant air pressure delivered to the user. In other embodiments, the respiratory therapy device 110 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In other embodiments, the respiratory therapy device 110 generates various different air pressures within a predetermined range. For example, the respiratory therapy device 110 can deliver at least about 6cmH2O, at least about 10cmH2O, at least about 20cmH2O, about 6cmH2O to about 10cmH2O, about 7cmH2O to about 12cmH2O, etc. The respiratory therapy device 110 can also deliver pressurized air at a predetermined flow rate of, for example, about -20L / minute to about 150L / minute while maintaining a positive pressure (relative to ambient pressure).

[0034] The respiratory therapy device 110 includes a housing 112, a blower motor 114, an air inlet 116, and an air outlet 118. The blower motor 114 is at least partially disposed or integrated within the housing 112. The blower motor 114 draws air from outside the housing 112 (e.g., the atmosphere) via the air inlet 116, and causes the pressurized air to flow through the humidifier 160 and through the air outlet 118. In some embodiments, the air inlet 116 and / or the air outlet 118 include a cover that can be moved between a closed position and an open position (e.g., to prevent or inhibit air from flowing through the air inlet 116 or the air outlet 118). The housing 112 may also include a vent to allow air to pass through the housing 112 to the air inlet 116. As described below, the conduit 140 is coupled to the air outlet 118 of the respiratory therapy device 110.

[0035] The user interface 120 engages a portion of the user's face, and pressurized air is delivered to the user's airway from the respiratory therapy device 110 to help prevent the airway from narrowing and / or collapsing during sleep. This can also increase the user's oxygen intake during sleep. Typically, the user interface 120 engages the user's face so that pressurized air is delivered to the user's airway via the user's mouth, the user's nose, or the user's mouth and nose. The respiratory therapy device 110, the user interface 120, and the conduit 140 together form an air passage connected to the user's airway fluid. Pressurized air also increases the user's oxygen intake during sleep. According to the therapy to be applied, the user interface 120 can, for example, form a seal with the area or part of the user's face, so that gas is delivered to achieve therapy at a pressure that is sufficiently different from the ambient pressure (e.g., at a positive pressure of about 10cmH2O relative to the ambient pressure) to achieve therapy. For other forms of therapy, such as the delivery of oxygen, the user interface may not include a seal that is sufficient to facilitate the delivery of gas supply to the airway at a positive pressure of about 10cmH2O.

[0036] The user interface 120 may include, for example, a cushion 122, a frame 124, a head cover 126, a connector 128, and one or more vents 130. The cushion 122 and the frame 124 define the volume of the space around the user's mouth and / or nose. When the respiratory therapy system 100 is in use, the volume space (e.g., via the conduit 140 from the respiratory therapy device 110) receives pressurized air to enter the user's airway. The head cover 126 is generally used to help position and / or stabilize the user interface 120 on a part of the user (e.g., face), and together with the cushion 122 (which may, for example, include silicone, plastic, foam, etc.), help provide a substantially airtight seal between the user interface 120 and the user 20. In some embodiments, the head cover 126 includes one or more bands (e.g., including hook-and-loop fasteners). The connector 128 is generally used to couple (e.g., connect and fluidly couple) the conduit 140 to the cushion 122 and / or frame 124. Alternatively, conduit 140 may be coupled directly to cushion 122 and / or frame 124 without connector 128. Vent 130 may be used to allow carbon dioxide and other gases exhaled by user 20 to escape. User interface 120 may generally include any suitable number of vents (e.g., one, two, five, ten, etc.).

[0037] like Figure 2 As shown, in some embodiments, the user interface 120 is a mask (e.g., a full-face mask) that covers at least a portion of the nose and mouth of the user 20. Alternatively, the user interface 120 may be a nasal mask that provides air to the user's nose or a nasal pillow mask that delivers air directly to the nostrils of the user 20. In other embodiments, the user interface 120 includes a mouthpiece (e.g., a night guard mouthpiece molded to conform to the user's teeth, a jaw repositioning device, etc.).

[0038] Return to reference Figure 1 , conduit 140 (also referred to as an air circuit or tube) allows air to flow between components of respiratory therapy system 100, such as between respiratory therapy device 110 and user interface 120. In some embodiments, there may be separate branches of the conduit for inhalation and exhalation. In other embodiments, a single branch conduit is used for inhalation and exhalation.

[0039] The conduit 140 includes a first end coupled to the air outlet 118 of the respiratory therapy device 110. The first end can be coupled to the air outlet 118 of the respiratory therapy device 110 using various techniques (e.g., a press fit connection, a snap fit connection, a threaded connection, etc.). In some embodiments, the conduit 140 includes one or more heating elements that heat the pressurized air flowing through the conduit 140 (e.g., heating the air to a predetermined temperature or within a predetermined temperature range). Such heating elements can be coupled to the conduit 140 and / or embedded in the conduit 140. In such embodiments, the first end can include an electrical contact that is electrically coupled to the respiratory therapy device 110 to power the one or more heating elements of the conduit 140. For example, the electrical contact can be electrically coupled to the electrical contact of the air outlet 118 of the respiratory therapy device 110. In this example, the electrical contact of the conduit 140 can be a male connector, and the electrical contact of the air outlet 118 can be a female connector, or alternatively, the opposite configuration can be used.

[0040] Display device 150 is generally used to display images including still images, video images, or both, and / or information about respiratory therapy device 110. For example, display device 150 may provide information about the status of respiratory therapy device 110 (e.g., whether respiratory therapy device 110 is on / off, the pressure of air delivered by respiratory therapy device 110, the temperature of air delivered by respiratory therapy device 110, etc.) and / or other information (e.g., sleep score and / or therapy score (also referred to as myAir TM scores, such as described in WO 2016 / 061629 and U.S. Patent Publication No. 2017 / 0311879, which are hereby incorporated by reference in their entirety), current date / time, personal information of user 20, etc. In some embodiments, display device 150 acts as a human-machine interface (HMI) that includes a graphical user interface (GUI) configured to display an image as an input interface. Display device 150 can be an LED display, an OLED display, an LCD display, etc. The input interface can be, for example, a touch screen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with respiratory therapy device 110.

[0041] The humidifier 160 is coupled to or integrated in the respiratory therapy device 110 and includes a reservoir 162 for storing water that can be used to humidify the pressurized air delivered from the respiratory therapy device 110. The humidifier 160 includes one or more heating elements 164 to heat the water in the reservoir to generate water vapor. The humidifier 160 can be fluidly coupled to a water vapor inlet of the air passage between the blower motor 114 and the air outlet 118, or can be formed in line with the air passage between the blower motor 114 and the air outlet 118. For example, air flows from the air inlet 116 through the blower motor 114 and then through the humidifier 160 before leaving the respiratory therapy device 110 via the air outlet 118.

[0042] Although the respiratory therapy system 100 has been described herein as including each of the respiratory therapy device 110, the user interface 120, the conduit 140, the display device 150, and the humidifier 160, more or fewer components may be included in the respiratory therapy system according to embodiments of the present disclosure. For example, a first alternative respiratory therapy system includes the respiratory therapy device 110, the user interface 120, and the conduit 140. As another example, a second alternative system includes the respiratory therapy device 110, the user interface 120, the conduit 140, and the display device 150. Thus, various respiratory therapy systems may be formed using any one or more portions of the components shown and described herein and / or in combination with one or more other components.

[0043] The control system 200 includes one or more processors 202 (hereinafter referred to as processors 202). The control system 200 is generally used to control (e.g., actuate) various components of the system 10 and / or analyze data obtained and / or generated by the components of the system 10. The processor 202 can be a general-purpose processor or a special-purpose processor or a microprocessor. Figure 1200, but the control system 200 may include any number of processors (e.g., one processor, two processors, five processors, ten processors, etc.), which may be located in a single housing or remotely from each other. The control system 200 (or any other control system) or a portion of the control system 200 (such as the processor 202 (or any other processor or one or more portions of any other control system)) may be used to perform one or more steps of any of the methods described and / or claimed herein. The control system 200 may be coupled to the housing of the user device 260 and / or located, for example, within the housing of the user device 260, within a portion of the respiratory therapy system 100 (e.g., the respiratory therapy device 110), and / or within the housing of one or more of the sensors 210. The control system 200 may be centralized (within one such housing) or decentralized (within two or more such housings that are physically distinct). In such embodiments including two or more housings containing the control system 200, the housings may be located proximately and / or remotely from each other.

[0044] The storage device 204 stores machine-readable instructions that are executable by the processor 202 of the control system 200. The storage device 204 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 Figure 1 2 , but system 10 may include any suitable number of storage devices 204 (e.g., one storage device, two storage devices, five storage devices, ten storage devices, etc.). Storage device 204 may be coupled to and / or located within a housing of respiratory therapy device 110, a housing of user device 260, a housing of one or more of sensors 210, or any combination thereof, of respiratory therapy system 100. Similar to control system 200, storage device 204 may be centralized (within one such housing) or decentralized (within two or more such housings that are physically distinct).

[0045] In some embodiments, the storage device 204 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 earlier sleep sessions), or any combination thereof. The demographic information may include, for example, information indicating the user's age, the user's gender, the user's race, the user's geographic location, relationship status, family history of insomnia or sleep apnea, the user's employment status, the user's educational status, the user's socioeconomic status, or any combination thereof. The medical information may include, for example, information indicating one or more medical conditions associated with the user, the user's medication use, or both. The medical information data may also include multiple sleep latency test (MSLT) results or scores and / or Pittsburgh Sleep Quality Index (PSQI) scores or values. The self-reported user feedback may include information indicating a self-reported subjective sleep score (e.g., poor, average, excellent), the user's self-reported subjective stress level, the user's self-reported subjective fatigue level, the user's self-reported subjective health status, the user's recent life events, or any combination thereof.

[0046] As described herein, the processor 202 and / or the storage device 204 can receive data (e.g., physiological data and / or audio data) from one or more sensors 210, such that the data is stored in the storage device 204 and / or analyzed by the processor 202. The processor 202 and / or the storage device 204 can communicate with the one or more sensors 210 using a wired connection or a wireless connection (e.g., using an RF communication protocol, a Wi-Fi communication protocol, a Bluetooth communication protocol, via a cellular network, etc.). In some embodiments, the system 10 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. Such components may be coupled to or integrated into a housing of the control system 200 (e.g., in the same housing as the processor 202 and / or the storage device 204) or the user device 260.

[0047] The one or more sensors 210 include a pressure sensor 212, a flow sensor 214, a temperature sensor 216, a motion sensor 218, a microphone 220, a speaker 222, a radio frequency (RF) receiver 226, an RF transmitter 228, a camera 232, an infrared (IR) sensor 234, a photoplethysmogram (PPG) sensor 236, an electrocardiogram (ECG) sensor 238, an electroencephalogram (EEG) sensor 240, a capacitive sensor 242, a force sensor 244, a strain gauge sensor 246, an electromyogram (EMG) sensor 248, an oxygen sensor 250, an analyte sensor 252, a humidity sensor 254, a light detection and ranging (LiDAR) sensor 256, or any combination thereof. In general, each of the one or more sensors 210 is configured to output sensor data, which is received and stored in the storage device 204 or one or more other storage devices.

[0048] Although the one or more sensors 210 are shown and described as including each of the pressure sensor 212, the flow sensor 214, the temperature sensor 216, the motion sensor 218, the microphone 220, the speaker 222, the RF receiver 226, the RF transmitter 228, the camera 232, the IR sensor 234, the PPG sensor 236, the ECG sensor 238, the EEG sensor 240, the capacitive sensor 242, the force sensor 244, the strain gauge sensor 246, the EMG sensor 248, the oxygen sensor 250, the analyte sensor 252, the humidity sensor 254, and the LiDAR sensor 256, more generally, the one or more sensors 210 may include any combination and any number of each of the sensors described and / or shown herein.

[0049] As described herein, the system 10 may generally be used to generate physiological data associated with a user (e.g., a user of the respiratory therapy system 100) during a sleep session. The physiological data may be analyzed to generate one or more sleep-related parameters, which may include any parameters, measurements, etc. associated with the user during the sleep session. The one or more sleep-related parameters that may be determined for the user 20 during the sleep session include, for example, an apnea-hypopnea index (AHI) score, a sleep score, a flow signal, a breathing signal, a breathing rate, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, a number of events per hour, an event pattern, a stage, a pressure setting of the respiratory therapy device 110, a heart rate, a heart rate variability, movement of the user 20, a temperature, an EEG activity, an EMG activity, micro-arousals, snoring, choking, coughing, whistling, gasping, or any combination thereof.

[0050] One or more sensors 210 may be used to generate, for example, physiological data, audio data, or both. The control system 200 may use the physiological data generated by one or more of the sensors 210 to determine a sleep-wake signal and one or more sleep-related parameters associated with the user 20 during a sleep session. The sleep-wake signal may indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-awakening, or different sleep stages, such as, for example, a rapid eye movement (REM) stage, a first non-REM stage (commonly referred to as "N1"), a second non-REM stage (commonly referred to as "N2"), a third non-REM stage (commonly referred to as "N3"), or any combination thereof. Methods for determining sleep states and / or sleep stages based on physiological data generated by one or more sensors (such as one or more sensors 210) are described in, for example, WO 2014 / 047310, U.S. Patent Publication No. 2014 / 0088373, WO 2017 / 132726, WO 2019 / 122413, WO 2019 / 122414, and U.S. Patent Publication No. 2020 / 0383580, each of which is hereby incorporated by reference in its entirety.

[0051] In some embodiments, the sleep-wake signal described herein may be time stamped to indicate the time when the user entered the bed, the time when the user left the bed, the time when the user attempted to fall asleep, etc. The sleep-wake signal may be measured by one or more sensors 210 at a predetermined sampling rate (e.g., one sample per second, one sample per 30 seconds, one sample per minute, etc.) during a sleep session. In some embodiments, the sleep-wake signal may also indicate a respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, pattern of events, pressure setting of the respiratory therapy device 110, or any combination thereof during a sleep session. Events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak (e.g., from the user interface 120), restless legs, sleep disturbance, choking, increased heart rate, difficulty breathing, asthma attack, epileptic seizure, convulsion, or any combination thereof. One or more sleep-related parameters that can be determined for a user based on the sleep-wake signal during a sleep session include, for example, total time in bed, total sleep time, sleep onset latency, wake parameters after sleep onset, sleep efficiency, segmentation index, or any combination thereof. As described in further detail herein, physiological data and / or sleep-related parameters can be analyzed to determine one or more sleep-related scores.

[0052] The physiological data and / or audio data generated by one or more sensors 210 can also be used to determine a breathing signal associated with the user during a sleep session. The breathing signal generally indicates the user's respiration / breathing during a sleep session. The breathing signal can indicate and / or be analyzed to determine (e.g., using the control system 200) one or more sleep-related parameters, such as, for example, respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, the occurrence of one or more events, the number of events per hour, the pattern of events, sleep state, sleep stage, apnea-hypopnea index (AHI), the pressure setting of the respiratory therapy device 110, or any combination thereof. The one or more events can include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak (e.g., from the user interface 120), coughing, restless legs, sleep disorders, suffocation, increased heart rate, dyspnea, asthma attack, epileptic seizure, convulsion, increased blood pressure, or any combination thereof. Many of the sleep-related parameters described are physiological parameters, although some of the sleep-related parameters can be considered non-physiological parameters. Other types of physiological parameters and / or non-physiological parameters may also be determined based on data from one or more sensors 210 or based on other types of data.

[0053] The pressure sensor 212 outputs pressure data that can be stored in the storage device 204 and / or analyzed by the processor 202 of the control system 200. In some embodiments, the pressure sensor 212 is an air pressure sensor (e.g., an atmospheric pressure sensor) that generates sensor data indicative of the respiration (e.g., inhalation and / or exhalation) and / or ambient pressure of a user of the respiratory therapy system 100. In such embodiments, the pressure sensor 212 can be coupled to or integrated in the respiratory therapy device 110. The pressure sensor 212 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.

[0054] The flow sensor 214 outputs flow data, which can be stored in the storage device 204 and / or analyzed by the processor 202 of the control system 200. Examples of flow sensors (such as, for example, flow sensors 214) are described in International Publication No. WO 2012 / 012835 and U.S. Patent No. 10,328,219, both of which are hereby incorporated by reference in their entirety. In some embodiments, the flow sensor 214 is used to determine the air flow from the respiratory therapy device 110, the air flow through the conduit 140, the air flow through the user interface 120, or any combination thereof. In such embodiments, the flow sensor 214 can be coupled to the respiratory therapy device 110, the user interface 120, or the conduit 140 or integrated in the respiratory therapy device 110, the user interface 120, or the conduit 140. The flow sensor 214 can be a mass flow sensor, such as, for example, 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. In some embodiments, the flow sensor 214 is configured to measure ventilation flow (e.g., intentional "leak"), unintentional leak (e.g., mouth leak and / or mask leak), patient flow (e.g., air entering and / or leaving the lungs), or any combination thereof. In some embodiments, the flow data can be analyzed to determine the user's cardiogenic oscillations. In some instances, the pressure sensor 212 can be used to determine the user's blood pressure.

[0055] Temperature sensor 216 outputs temperature data that may be stored in storage device 204 and / or analyzed by processor 202 of control system 200. In some implementations, temperature sensor 216 generates temperature data indicative of a core body temperature of user 20, a skin temperature of user 20, a temperature of air flowing from respiratory therapy device 110 and / or through conduit 140, a temperature in user interface 120, an ambient temperature, or any combination thereof. Temperature sensor 216 may be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap temperature sensor or a semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0056] The motion sensor 218 outputs motion data that can be stored in the storage device 204 and / or analyzed by the processor 202 of the control system 200. The motion sensor 218 can be used to detect the movement of the user 20 during a sleep session, and / or detect the movement of any of the components of the respiratory therapy system 100 (such as the respiratory therapy device 110, the user interface 120, or the catheter 140). The motion sensor 218 may include one or more inertial sensors, such as an accelerometer, a gyroscope, and a magnetometer. In some embodiments, the motion sensor 218 alternatively or additionally generates one or more signals representing the user's body movement, from which a signal representing the user's sleep state can be obtained; for example, through the user's breathing movement. In some embodiments, the motion data from the motion sensor 218 can be used in combination with additional data from another sensor in the sensor 210 to determine the user's sleep state.

[0057] The microphone 220 outputs sounds and / or audio data that can be stored in the storage device 204 and / or analyzed by the processor 202 of the control system 200. The audio data generated by the microphone 220 can be reproduced as one or more sounds (e.g., sounds from the user 20) during the sleep session. The audio data from the microphone 220 can also be used to identify (e.g., using the control system 200) events experienced by the user during the sleep session, as described in further detail herein. The microphone 220 can be coupled to or integrated into the respiratory therapy device 110, the user interface 120, the catheter 140, or the user device 260. In some embodiments, the system 10 includes multiple microphones (e.g., two or more microphones and / or a microphone array with beamforming) such that the sound data generated by each of the multiple microphones can be used to distinguish the sound data generated by another microphone in the multiple microphones.

[0058] Speaker 222 outputs information to the user of system 10 (e.g., Figure 2 The speaker 222 can be used, for example, as an alarm clock or to play an alarm or message to the user 20 (e.g., in response to an event). In some embodiments, the speaker 222 can be used to transmit audio data generated by the microphone 220 to the user. The speaker 222 can be coupled to or integrated into the respiratory therapy device 110, the user interface 120, the conduit 140, or the user device 260.

[0059] The microphone 220 and the speaker 222 can be used as separate devices. In some embodiments, the microphone 220 and the speaker 222 can be combined into an acoustic sensor 224 (e.g., a SONAR sensor), as described in, for example, WO 2018 / 050913, WO2020 / 104465, U.S. Patent Application Publication No. 2022 / 0007965, each of which is hereby incorporated by reference in its entirety. In such embodiments, the speaker 222 generates or emits sound waves at predetermined intervals, and the microphone 220 detects reflections of the emitted sound waves from the speaker 222. The sound waves generated or emitted by the speaker 222 have a frequency that is 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 20 or the bed partner 30. Based at least in part on data from microphone 220 and / or speaker 222, control system 200 may determine the position of user 20 and / or one or more of the sleep-related parameters described herein, such as, for example, a breathing signal, a breathing rate, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, a number of events per hour, a pattern of events, a sleep state, a sleep stage, a pressure setting of respiratory therapy device 110, or any combination thereof. In such context, sonar sensors may be understood to involve active acoustic sensing such as generating and / or transmitting ultrasound and / or low-frequency ultrasound sensing signals (e.g., in a frequency range of, for example, about 17 kHz to 23 kHz, 18 kHz to 22 kHz, or 17 kHz to 18 kHz) through the air.

[0060] In some embodiments, the sensor 210 includes: (i) a first microphone that is the same as or similar to the microphone 220 and is integrated into the acoustic sensor 224; and (ii) a second microphone that is the same as or similar to the microphone 220, but is separate and different from the first microphone integrated into the acoustic sensor 224.

[0061] The RF transmitter 228 generates and / or transmits 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 226 detects reflections of the radio waves transmitted from the RF transmitter 228, and this data can be analyzed by the control system 200 to determine the user's position and / or one or more of the sleep-related parameters described herein. The RF receiver (RF receiver 226 and RF transmitter 228 or another RF pair) can also be used for wireless communication between the control system 200, the respiratory therapy device 110, one or more sensors 210, the user device 260, or any combination thereof. Although the RF receiver 226 and the RF transmitter 228 are in Figure 12 and 3. Although shown as separate and distinct elements, in some embodiments, the RF receiver 226 and the RF transmitter 228 are combined as part of the RF sensor 230 (e.g., a RADAR sensor). In some such embodiments, the RF sensor 230 includes a control circuit. The format of the RF communication can be Wi-Fi, Bluetooth, etc.

[0062] In some embodiments, the RF sensor 230 is part of a mesh system. An example of a mesh system is a Wi-Fi mesh system, which may include mesh nodes, mesh routers, and mesh gateways, each of which may be mobile / removable or fixed. In such embodiments, the Wi-Fi mesh system includes a Wi-Fi router and / or a Wi-Fi controller and one or more satellites (e.g., access points), each of which includes an RF sensor that is the same or similar to the RF sensor 230. The Wi-Fi router and the satellites communicate continuously with each other using Wi-Fi signals. The Wi-Fi mesh system can be used to generate motion data based on changes in Wi-Fi signals (e.g., differences in received signal strength) between the router and the satellite due to partial obstruction of the signal by a moving object or person. The motion data may indicate motion, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0063] The camera 232 outputs image data that can be reproduced as one or more images (e.g., still images, video images, thermal images, or any combination thereof) that can be stored in the storage device 204. The image data from the camera 232 can be used by the control system 200 to determine one or more of the sleep-related parameters described herein, such as, for example, one or more events (e.g., periodic limb movements or restless legs syndrome), a breathing signal, a breathing rate, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, the number of events per hour, an event pattern, a sleep state, a sleep stage, or any combination thereof. In addition, the image data from the camera 232 can be used, for example, to identify the position of the user, determine the chest movement of the user, determine the air flow of the mouth and / or nose of the user, determine the time when the user enters the bed, and determine the time when the user leaves the bed. In some embodiments, the camera 232 includes a wide-angle lens or a fisheye lens.

[0064] IR sensor 234 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, video images, or both) that can be stored in storage device 204. Infrared data from IR sensor 234 can be used to determine one or more sleep-related parameters during a sleep session, including the temperature of user 20 and / or the movement of user 20. IR sensor 234 can also be used in conjunction with camera 232 when measuring the presence, position, and / or movement of user 20. For example, IR sensor 234 can detect infrared light having a wavelength between about 700 nm and about 1 mm, while camera 232 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0065] The PPG sensor 236 outputs physiological data associated with the user 20, which can be used to determine one or more sleep-related parameters, such as, for example, heart rate, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, estimated blood pressure parameters, or any combination thereof. The PPG sensor 236 can be worn by the user 20, embedded in clothing and / or fabric worn by the user 20, embedded in the user interface 120 and / or its associated headgear (e.g., band, etc.) and / or coupled to the user interface 120 and / or its associated headgear (e.g., band, etc.), etc.

[0066] ECG sensor 238 outputs physiological data associated with the electrical activity of the heart of user 20. In some embodiments, ECG sensor 238 includes one or more electrodes positioned on or around a portion of user 20 during a sleep session. Physiological data from ECG sensor 238 can be used, for example, to determine one or more of the sleep-related parameters described herein.

[0067] The EEG sensor 240 outputs physiological data associated with the electrical activity of the brain of the user 20. In some embodiments, the EEG sensor 240 includes one or more electrodes positioned on or around the scalp of the user 20 during a sleep session. The physiological data from the EEG sensor 240 can be used, for example, to determine the sleep state and / or sleep stage of the user 20 at any given time during the sleep session. In some embodiments, the EEG sensor 240 can be integrated into the user interface 120 and / or associated headgear (e.g., a band, etc.).

[0068] Capacitive sensor 242, force sensor 244, and strain gauge sensor 246 output data that can be stored in storage device 204 and used / analyzed by control system 200 to determine, for example, one or more of the sleep-related parameters described herein. EMG sensor 248 outputs physiological data associated with electrical activity generated by one or more muscles. Oxygen sensor 250 outputs oxygen data indicating the oxygen concentration of a gas (e.g., in conduit 140 or at user interface 120). Oxygen sensor 250 can be, for example, an ultrasonic oxygen sensor, an electrical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, a pulse oximeter (e.g., a SpO2 sensor), or any combination thereof.

[0069] The analyte sensor 252 can be used to detect the presence of an analyte in the exhaled breath of the user 20. The data output by the analyte sensor 252 can be stored in the storage device 204 and used by the control system 200 to determine the identity and concentration of any analyte in the user's breath. In some embodiments, the analyte sensor 252 is positioned near the user's mouth to detect analytes in the breath exhaled from the user's mouth. For example, when the user interface 120 is a mask covering the user's nose and mouth, the analyte sensor 252 can be positioned in the mask to monitor the user's mouth breathing. In other implementations, such as when the user interface 120 is a nasal mask or a nasal pillow mask, the analyte sensor 252 can be positioned near the user's nose to detect analytes in the breath exhaled through the user's nose. In other embodiments, when the user interface 120 is a nasal mask or a nasal pillow mask, the analyte sensor 252 can be positioned near the user's mouth. In this embodiment, the analyte sensor 252 can be used to detect whether any air leaks unintentionally from the user's mouth and / or the user interface 120. In some embodiments, the analyte sensor 252 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds. In some embodiments, the analyte sensor 252 can also be used to detect whether the user breathes through their nose or through their mouth. For example, if data output by the analyte sensor 252 positioned near the user's mouth or positioned within the mask (e.g., in embodiments where the user interface 120 is a mask) detects the presence of an analyte, the control system 200 can use this data as an indication that the user is breathing through their mouth.

[0070] The humidity sensor 254 outputs data that can be stored in the storage device 204 and used by the control system 200. The humidity sensor 254 can be used to detect humidity in various areas surrounding the user (e.g., within the conduit 140 or the user interface 120, near the user's face, near the connection between the conduit 140 and the user interface 120, near the connection between the conduit 140 and the respiratory therapy device 110, etc.). Therefore, in some embodiments, the humidity sensor 254 can be connected to the user interface 120 or the conduit 140 or integrated in the user interface 120 or in the conduit 140 to monitor the humidity of the pressurized air from the respiratory therapy device 110. In other embodiments, the humidity sensor 254 is placed near any area where the humidity level needs to be monitored. The humidity sensor 254 can also be used to monitor the humidity of the surrounding environment surrounding the user, such as the air in a bedroom.

[0071] LiDAR sensor 256 can be used for depth sensing. This type of optical sensor (e.g., laser sensor) can be used to detect objects and build a three-dimensional (3D) map of the surrounding environment (such as living space). LiDAR can usually use pulsed lasers to measure time of flight. LiDAR is also called 3D laser scanning. In an example using this sensor, a fixed or mobile device (such as a smart phone) with a LiDAR sensor 256 can measure and map an area extending 5 meters or more away from the sensor. For example, LiDAR data can be fused with point cloud data estimated by an electromagnetic RADAR sensor. LiDAR sensor 256 can also use artificial intelligence (AI) to automatically set a geo-fence for the RADAR system by detecting and classifying features in the space that may cause problems for the RADAR system, such as glass windows (which can be highly reflective to RADAR). For example, LiDAR can also be used to provide an estimate of a person's height, as well as changes in height when a person sits down or falls. LiDAR can be used to form a 3D grid representation of the environment. In other uses, for solid surfaces that radio waves pass through (e.g., radio-transmissive materials), LiDAR can reflect off such surfaces, allowing classification of different types of obstacles.

[0072] In some embodiments, the one or more sensors 210 also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a sphygmomanometer sensor, an oximeter sensor, a sonar sensor, a RADAR sensor, a blood glucose sensor, a color sensor, a pH sensor, an air quality sensor, a tilt sensor, a rain sensor, a soil moisture sensor, a water flow sensor, an alcohol sensor, or any combination thereof.

[0073] Although in Figure 110, but any combination of the one or more sensors 210 may be integrated into and / or coupled to any one or more of the components of the system 10, including the respiratory therapy device 110, the user interface 120, the conduit 140, the humidifier 160, the control system 200, the user device 260, the activity tracker 270, or any combination thereof. For example, the microphone 220 and the speaker 222 may be integrated into and / or coupled to the user device 260, and the pressure sensor 212 and / or the flow sensor 214 may be integrated into and / or coupled to the respiratory therapy device 110. In some implementations, at least one of the one or more sensors 210 is not coupled to the respiratory therapy device 110, the control system 200, or the user device 260, and is generally positioned adjacent to the user 20 during a sleep session (e.g., positioned on or in contact with a portion of the user 20, worn by the user 20, coupled to or positioned on a nightstand, coupled to a mattress, coupled to a ceiling, etc.).

[0074] One or more of respiratory therapy device 110, user interface 120, conduit 140, display device 150, and humidifier 160 may include one or more sensors (e.g., a pressure sensor, a flow sensor, or more generally any of the other sensors 210 described herein). These one or more sensors may be used, for example, to measure the air pressure and / or flow of pressurized air supplied by respiratory therapy device 110.

[0075] Data from one or more sensors 210 may be analyzed (e.g., by the control system 200) to determine one or more sleep-related parameters, which may include a respiratory signal, a respiratory rate, a respiratory pattern, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, the occurrence of one or more events, the number of events per hour, an event pattern, a sleep state, an apnea-hypopnea index (AHI), or any combination thereof. The one or more events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak, cough, restless legs, sleep disorder, choking, increased heart rate, dyspnea, asthma attack, epileptic seizure, convulsion, increased blood pressure, or any combination thereof. Many of these sleep-related parameters are physiological parameters, although some of the sleep-related parameters may be considered non-physiological parameters. Other types of physiological parameters and non-physiological parameters may also be determined based on data from one or more sensors 210 or based on other types of data.

[0076] The user device 260 includes a display device 262. The user device 260 can be, for example, a mobile device such as a smart phone, a tablet computer, a game console, a smart watch, a laptop, etc. Alternatively, the user device 260 can be an external sensing system, a television (e.g., a smart TV) or another smart home device (e.g., a smart speaker, such as Google Home, Amazon Echo, Alexa, etc.). In some embodiments, the user device is a wearable device (e.g., a smart watch). The display device 262 is generally used to display images including still images, video images, or both. In some embodiments, the display device 262 acts as a human-machine interface (HMI), which includes a graphical user interface (GUI) and an input interface configured to display an image. The display device 262 can be an LED display, an OLED display, an LCD display, etc. The input interface can be, for example, a touch screen or a touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with the user device 260. In some embodiments, the system 10 can use and / or include one or more user devices.

[0077] In some embodiments, the system 10 also includes an activity tracker 270. The activity tracker 270 is generally used to help generate physiological data associated with the user. The activity tracker 270 may include one or more of the sensors 210 described herein, such as, for example, a motion sensor 218 (e.g., one or more accelerometers and / or gyroscopes), a PPG sensor 236, and / or an ECG sensor 238. The physiological data from the activity tracker 270 can be used to determine, for example, the number of steps, the distance traveled, the number of steps climbed, the duration of physical activity, the type of physical activity, the intensity of physical activity, the time spent standing, the breathing rate, the average breathing rate, the resting breathing rate, the maximum breathing rate, the breathing rate variability, the heart rate, the average heart rate, the resting heart rate, the maximum heart rate, the heart rate variability, the number of calories burned, the blood oxygen saturation, the skin electrode activity (also known as skin conductance or skin electrode response) or any combination thereof. In some embodiments, the activity tracker 270 is (e.g., electronically or physically) connected to the user device 260.

[0078] In some embodiments, the activity tracker 270 is a wearable device that can be worn by a user, such as a smart watch, wristband, ring, or patch. Figure 2, activity tracker 270 is worn on the wrist of user 20. Activity tracker 270 may also be coupled to or integrated into a garment or clothing worn by the user. Alternatively, activity tracker 270 may also be coupled to user device 260 or integrated into user device 260 (e.g., within the same housing). More generally, activity tracker 270 may be communicatively coupled to or physically integrated into control system 200, storage device 204, respiratory therapy system 100, and / or user device 260 (e.g., within a housing).

[0079] In some embodiments, the system 10 also includes a blood pressure device 280. The blood pressure device 280 is generally used to assist in generating cardiovascular data for determining one or more blood pressure measurements associated with the user 20. The blood pressure device 280 may include at least one of the one or more sensors 210 to measure, for example, a systolic blood pressure component and / or a diastolic blood pressure component.

[0080] In some embodiments, blood pressure device 280 is a sphygmomanometer that includes an inflatable cuff and a pressure sensor (e.g., pressure sensor 212 described herein) that can be worn by user 20. For example, in Figure 2 In an example of , the blood pressure device 280 can be worn on the upper arm of the user 20. In such embodiments where the blood pressure device 280 is a sphygmomanometer, the blood pressure device 280 also includes a pump (e.g., a manually operated bulb) for inflating the cuff. In some embodiments, the blood pressure device 280 is coupled to the respiratory therapy device 110 of the respiratory therapy system 100, which in turn delivers pressurized air to inflate the cuff. More generally, the blood pressure device 280 can be communicatively coupled to and / or physically integrated into (e.g., within a housing) the control system 200, the storage device 204, the respiratory therapy system 100, the user device 260, and / or the activity tracker 270.

[0081] In other embodiments, the blood pressure device 280 is a mobile blood pressure monitor communicatively coupled to the respiratory therapy system 100. The mobile blood pressure monitor includes a portable recording device attached to a band or belt worn by the user 20 and an inflatable cuff attached to the portable recording device and worn around the arm of the user 20. The mobile blood pressure monitor is configured to measure blood pressure about every 15 minutes to about 30 minutes over a 24-hour or 48-hour period. The mobile blood pressure monitor can simultaneously measure the heart rate of the user 20. These multiple readings are averaged over a 24-hour period. The mobile blood pressure monitor determines any changes in the blood pressure and heart rate of the user 20 measured during the sleep period and the wake period of the user 20, as well as any distribution and / or trend patterns of the blood pressure and heart rate data. The measured data and statistical data can then be transmitted to the respiratory therapy system 100.

[0082] The blood pressure device 280 can be positioned external to the respiratory therapy system 100, coupled directly or indirectly to the user interface 120, coupled directly or indirectly to a headgear associated with the user interface 120, or inflatably coupled to a portion of or around the user 20. The blood pressure device 280 is generally used to help generate physiological data to determine one or more blood pressure measurements associated with the user, such as a systolic blood pressure component and / or a diastolic blood pressure component. In some embodiments, the blood pressure device 280 is a sphygmomanometer that includes an inflatable cuff that can be worn by the user and a pressure sensor (e.g., the pressure sensor 212 described herein).

[0083] In some embodiments, the blood pressure device 280 is an invasive device that can continuously monitor the arterial blood pressure of the user 20 and collect arterial blood samples as needed to analyze the gas of the arterial blood. In some other embodiments, the blood pressure device 280 is a continuous blood pressure monitor that uses a radio frequency sensor and can measure the blood pressure of the user 20 only once every few seconds (e.g., every 3 seconds, every 5 seconds, every 7 seconds, etc.). The radio frequency sensor can use continuous wave, frequency modulated continuous wave (FMCW with ramp linear frequency modulation, triangle, sine wave), other schemes such as PSK, FSK, etc., pulse continuous wave, and / or extension in ultra-wideband range (which can include extension, PRN code or pulse system).

[0084] Although the control system 200 and the storage device 204 are Figure 1 10 as separate and distinct components of system 10, but in some embodiments, control system 200 and / or storage device 204 are integrated into user device 260 and / or respiratory therapy device 110. Alternatively, in some embodiments, control system 200 or a portion thereof (e.g., processor 202) can be located in the cloud (e.g., integrated in a server, integrated in an Internet of Things (IoT) device, connected to the cloud, subject to edge cloud processing, etc.), located in one or more servers (e.g., remote servers, local servers, etc., or any combination thereof).

[0085] Although system 10 is shown as including all of the above components, more or fewer components may be included in systems according to embodiments of the present disclosure. For example, a first alternative system includes control system 200, storage device 204, and at least one of one or more sensors 210, and does not include respiratory therapy system 100. As another example, a second alternative system includes control system 200, storage device 204, at least one of one or more sensors 210, and user device 260. As yet another example, a third alternative system includes control system 200, storage device 204, respiratory therapy system 100, at least one of one or more sensors 210, and user device 260. Thus, various systems may be formed using any one or more portions of the components shown and described herein and / or in combination with one or more other components.

[0086] Reference now Figure 3 As used herein, a sleep session may be defined in a variety of ways. For example, a sleep session may be defined by an initial start time and an end time. In some embodiments, a sleep session is a duration of time during which a user sleeps, i.e., a sleep session has a start time and an end time, and during the sleep session, the user does not wake up until the end time. That is, any period of time during which the user is awake is not included in a sleep session. According to this first definition of a sleep session, if a user wakes up and falls asleep multiple times in the same night, each of the sleep intervals separated by the wake intervals is a sleep session.

[0087] Alternatively, in some embodiments, a sleep session has a start time and an end time, and during a sleep session, as long as the user is awake for a continuous duration below an awake duration threshold, the user can wake up without the sleep session ending. The awake duration threshold can be defined as a percentage of the sleep session. The awake duration threshold can be, for example, about 20% of the sleep session, about 15% of the sleep session duration, about 10% of the sleep session duration, about 5% of the sleep session duration, about 2% of the sleep session duration, etc., or any other threshold percentage. In some embodiments, the awake duration threshold is defined as a fixed amount of time, such as, for example, about one hour, about thirty minutes, about fifteen minutes, about ten minutes, about five minutes, about two minutes, etc., or any other amount of time.

[0088] In some embodiments, a sleep session is defined as the entire time between the time the user first goes to bed in the evening and the time the user last leaves bed the next morning. In other words, a sleep session can be defined as a period of time that begins on a first date (e.g., Monday, January 6, 2020) at a first time (e.g., 10:00 p.m.) when the user first goes to bed intending to go to sleep (e.g., this is not the case if the user does not intend to first watch TV or use a smartphone before going to sleep, etc.), which can be referred to as the current evening, and ends on a second date (e.g., Tuesday, January 7, 2020) at a second time (e.g., 7:00 a.m.) when the user first gets out of bed and does not intend to return to sleep the next morning.

[0089] In some embodiments, a user can manually define the start of a sleep session and / or manually terminate a sleep session. For example, a user can select (e.g., by clicking or tapping) a sleep session on user device 260 ( Figure 1 ) to manually initiate or terminate a sleep session by displaying one or more user-selectable elements displayed on display device 262 of the device.

[0090] In general, a sleep session includes any point in time after the user has laid down or sat down in bed (or another area or object on which they intend to sleep), and has turned on the respiratory therapy device 110 and put on the user interface 120. A sleep session may thus include time periods: (i) when the user is using the respiratory therapy system 100, but before the user attempts to fall asleep (e.g., when the user is reading a book in bed); (ii) when the user begins to try to fall asleep but is still awake; (iii) when the user is in light sleep (also known as stage 1 and stage 2 of non-rapid eye movement (NREM) sleep); (iv) when the user is in deep sleep (also known as slow wave sleep, SWS, or stage 3 of NREM sleep); (v) when the user is in rapid eye movement (REM sleep); (vi) when the user is awake periodically between light sleep, deep sleep, or REM sleep; or (vii) when the user wakes up and does not fall asleep again.

[0091] A sleep session is generally defined as ending once the user removes user interface 120, turns off respiratory therapy device 110, and gets out of bed. In some embodiments, a sleep session may include additional time periods, or may be limited to only some of the time periods disclosed above. For example, a sleep session may be defined as a time period that begins when respiratory therapy device 110 begins supplying pressurized air to the airway or user, ends when respiratory therapy device 110 stops supplying pressurized air to the airway of the user, and includes some or all points in between when the user falls asleep or wakes up.

[0092] Figure 3An exemplary timeline 300 of a sleep session is illustrated. The timeline 300 includes the bed entry time (t 入床 ), sleep time (t GTS )、initial sleep time (t 睡眠 )、first micro wakeup MA1、second micro wakeup MA2、wakeup A、wakeup time (t 觉醒 ) and wake-up time (t 起床 ).

[0093] Time to bed 入床 The user initially enters the bed (e.g., before falling asleep) (e.g., when the user lies down or sits in bed) Figure 2 The time of entering the bed 40 is related to the time of entering the bed 40. 入床 The bed threshold duration may be identified based at least in part on the bed threshold duration to distinguish between times when the user enters the bed to sleep and times when the user enters the bed for other reasons (e.g., watching TV). For example, the bed threshold duration may 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. Although the bed entry time t is described herein with reference to the bed, the bed entry time t 入床 , but more generally, the bed entry time t 入床 May refer to the time when a user initially enters any position (eg, sofa, chair, sleeping bag, etc.) for the purpose of sleeping.

[0094] Going to sleep time (GTS) and the time it takes for the user to enter the bed (t 入床 ) is associated with the time of an initial attempt to fall asleep after entering the bed. For example, after entering the bed, the user may engage in one or more activities to relax before attempting to fall asleep (e.g., reading, watching television, listening to music, using user device 260, etc.). The initial sleep time (t 睡眠 ) is the time when the user initially falls asleep. For example, the initial sleep time (t 睡眠 ) may be the time when the user initially enters the first non-REM sleep stage.

[0095] Wake up time 觉醒 is the time associated with the time when the user wakes up and does not fall asleep again (e.g., as opposed to the user waking up in the middle of the night and falling asleep again). The user may experience one of a number of unconscious micro-awakenings (e.g., micro-awakenings MA1 and MA2) with short durations (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.) after initially falling asleep. 觉醒 Instead, the user falls asleep again after each of the micro-awakenings MA1 and MA2. Similarly, the user may have one or more conscious awakenings (e.g., awakening A) after initially falling asleep (e.g., getting up to go to the bathroom, taking care of a child or pet, sleepwalking, etc.). However, the user falls asleep again after awakening A. Therefore, the wake-up time t觉醒 It can be defined, for example, based at least in part on a wakefulness threshold duration (e.g., the user has been awake for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).

[0096] Similarly, the wake-up time t 起床 is associated with the time when the user gets out of bed and away from the bed with the intention of ending a sleep session (e.g., as opposed to the user getting up during the night to go to the bathroom, take care of children or pets, sleepwalk, etc.). In other words, the wake-up time t 起床 is the time when the user last left the bed and did not return to the bed until the next sleep session (e.g., the next night). Therefore, the wake-up time t 起床 The bed entry time t of the second subsequent sleep session may be defined, for example, based at least in part on a get-up threshold duration (e.g., the user has been out of bed for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). The bed entry time t of the second subsequent sleep session may also be defined based at least in part on a get-up threshold duration (e.g., the user has been out of bed for at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours, etc.). 入床 time.

[0097] As mentioned above, the user may initially 入床 and finally t 起床 In some embodiments, the final wake-up time t 觉醒 and / or final wake-up time t 起床 The threshold duration may be determined based at least in part on a predetermined threshold duration after an event (e.g., falling asleep or leaving bed). Such a threshold duration may be customized for the user. For a typical user who goes to bed at night and then wakes up and gets out of bed in the morning, any time period of about 12 hours to about 18 hours may be used (after the user wakes up (t 觉醒 ) or get up (t 起床 ) and the user goes to bed (t 入床 ), enter sleep (t GTS ) or fall asleep (t 睡眠 )). For users who spend longer periods of time in bed, a shorter threshold time period (e.g., about 8 hours to about 14 hours) may be used. The threshold time period may be initially selected and / or later adjusted based at least in part on the system monitoring the user's sleep behavior.

[0098] Total time in bed (TIB) is the time spent in bed 入床 and wake-up time 起床The total sleep time (TST) is associated with the duration between the initial sleep time and the wake time, excluding any conscious or unconscious arousals and / or micro-awakenings in between. Typically, the total sleep time (TST) will be shorter than the total time in bed (TIB) (e.g., one minute shorter, ten minutes shorter, one hour shorter, etc.). For example, as shown in timeline 300, the total sleep time (TST) is 10 minutes shorter than the initial sleep time t 睡眠 and wake-up time t 觉醒 The durations spanning between, but excluding, the first micro-awakening MA1, the second micro-awakening MA2, and awakening A. As shown in the figure, in this example, the total sleep time (TST) is shorter than the total time in bed (TIB).

[0099] In some embodiments, total sleep time (TST) can be defined as continuous total sleep time (PTST). In such embodiments, continuous total sleep time does not include a predetermined initial portion or time period of the first non-REM stage (e.g., a light sleep stage). For example, the predetermined initial portion may be between about 30 seconds and about 20 minutes, between about 1 minute and about 10 minutes, between about 3 minutes and about 5 minutes, etc. Continuous total sleep time is a measure of continuous sleep and smoothes the sleep-wake hypnogram. For example, when a user initially falls asleep, the user may be in the first non-REM stage for a very short time (e.g., about 30 seconds), then return to the wake stage for a very short period (e.g., one minute), and then return to the first non-REM stage. In this example, continuous total sleep time does not include a first instance of the first non-REM stage (e.g., about 30 seconds).

[0100] In some embodiments, a sleep session is defined as the time between bed entry (t 入床 ) and starts at the wake-up time (t 起床 ) ends, i.e., the sleep session is defined as total time in bed (TIB). In some embodiments, a sleep session is defined as the total time in bed (TIB) during the initial sleep time (t 睡眠 ) starts and starts at the wake-up time (t 觉醒 ) ends. In some embodiments, a sleep session is defined as total sleep time (TST). In some embodiments, a sleep session is defined as the time between sleep onset time (t GTS ) and starts at the wake-up time (t 觉醒 ) ends. In some embodiments, a sleep session is defined as a sleep session starting at sleep entry time (t GTS ) and starts at the wake-up time (t 起床 ) ends. In some embodiments, a sleep session is defined as the time at which a person enters the bed (t 入床 ) and starts at the wake-up time (t 觉醒 ) ends. In some embodiments, a sleep session is defined as a sleep session at an initial sleep time (t睡眠 ) and starts at the wake-up time (t 起床 )Finish.

[0101] refer to Figure 4 , illustrating the Figure 3 300. As shown, the hypnogram 400 includes a sleep-wake signal 401, a wake stage axis 410, a REM stage axis 420, a light sleep stage axis 430, and a deep sleep stage axis 440. The intersection between the sleep-wake signal 401 and one of the axes 410 to 440 indicates the sleep stage at any given time during the sleep session.

[0102] The sleep-wake signal 401 can be generated at least in part based on physiological data associated with the user (e.g., generated by one or more of the sensors 210 described herein). The sleep-wake signal can indicate one or more sleep stages, including wakefulness, awakening in a relaxed state, micro-arousal, REM stage, first non-REM stage, second non-REM stage, third non-REM stage, or any combination thereof. In some embodiments, one or more of the first non-REM stage, the second non-REM stage, and the third non-REM stage can be grouped together and classified as a light sleep stage or a deep sleep stage. For example, the light sleep stage may include the first non-REM stage, and the deep sleep stage may include the second non-REM stage and the third non-REM stage. Although in Figure 4 The hypnogram 400 shown in FIG. 4 includes a light sleep stage axis 430 and a deep sleep stage axis 440, but in some embodiments, the hypnogram 400 may include an axis for each of the first non-REM stage, the second non-REM stage, and the third non-REM stage. In other embodiments, the sleep-wake signal may also indicate a breathing signal, a breathing rate, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory amplitude ratio, an inspiratory-expiratory duration ratio, a number of events per hour, an event pattern, or any combination thereof. Information describing the sleep-wake signal may be stored in the storage device 204.

[0103] The hypnogram 400 may be used to determine one or more sleep-related parameters, such as, for example, sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), sleep segmentation index, sleep blocks, or any combination thereof.

[0104] Sleep onset latency (SOL) is defined as the time to enter sleep (t GTS ) and initial sleep time (t 睡眠). In other words, the sleep onset latency indicates the time it takes for the user to actually fall asleep after the initial attempt to fall asleep. In some embodiments, the sleep onset latency is defined as the sustained sleep onset latency (PSOL). The difference between the sustained sleep onset latency and the sleep onset latency is that the sustained sleep onset latency is defined as the duration between the time of entering sleep and the predetermined amount of sustained sleep. In some embodiments, the predetermined amount of sustained sleep may include, for example, at least 10 minutes of sleep in the second non-REM stage, the third non-REM stage and / or the REM stage with no more than 2 minutes of awakening, the first non-REM stage and / or the movement therebetween. In other words, the sustained sleep onset latency requires sustained sleep for up to, for example, 8 minutes in the second non-REM stage, the third non-REM stage and / or the REM stage. In other embodiments, the predetermined amount of sustained sleep may include at least 10 minutes of sleep in the first non-REM stage, the second non-REM stage, the third non-REM stage and / or the REM stage after the initial sleep time. In such embodiments, the predetermined amount of sustained sleep may not include any micro-awakening (e.g., a ten-second micro-awakening does not restart a 10-minute time period).

[0105] Awakenings after sleep onset (WASO) is associated with the total duration of time a user is awake between the initial sleep time and the wake time. Thus, WASO includes brief awakenings and micro-awakenings during a sleep session (e.g., Figure 4 In some embodiments, a wakefulness after sleep onset (WASO) is defined as a persistent wakefulness after sleep onset (PWASO) that includes only wakefulness with a total duration of a predetermined length (e.g., greater than 10 seconds, greater than 30 seconds, greater than 60 seconds, greater than about 5 minutes, greater than about 10 minutes, etc.).

[0106] Sleep efficiency (SE) is determined as the ratio of total time in bed (TIB) to total sleep time (TST). For example, if the total time in bed is 8 hours and the total sleep time is 7.5 hours, the sleep efficiency of the sleep session is 93.75%. Sleep efficiency indicates the sleep hygiene of the user. For example, if the user enters the bed before going to bed and spends time participating in other activities (e.g., watching TV), the sleep efficiency will be reduced (e.g., the user is punished). In some embodiments, sleep efficiency (SE) can be calculated based at least in part on the total time in bed (TIB) and the total time the user attempts to sleep. In such embodiments, the total time the user attempts to sleep is defined as the duration between the time to go to sleep (GTS) and the time to get up as described herein. For example, if the total sleep time is 8 hours (e.g., 11 pm to 7 am), the time to go to sleep is 10:45 pm, and the time to get up is 7:15 am, then in such embodiments, the sleep efficiency parameter is calculated to be about 94%.

[0107] The segment index is determined based at least in part on the number of awakenings during the sleep session. For example, if a user has two micro-awakenings (e.g., Figure 4 If the segment index is 2, the segment index can be expressed as 2. In some embodiments, the segment index is scaled between a predetermined range of integers (eg, between 0 and 10).

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

[0109] In some embodiments, the systems and methods described herein may include generating or analyzing a hypnogram including a sleep-wake signal to determine or identify a bed entry time (t 入床 ), sleep time (t GTS )、initial sleep time (t 睡眠 ), one or more first micro-awakenings (e.g., MA1 and MA2), wake-up time (t 觉醒 ), wake-up time (t 起床 ) or any combination thereof.

[0110] In other embodiments, one or more of the sensors 210 may be used to determine or identify a bed entry time (t 入床 ), sleep time (t GTS )、initial sleep time (t 睡眠 ), one or more first micro-wake-ups (e.g., MA1 and MA2), wake-up time (t 觉醒), wake-up time (t 起床 ) or any combination thereof. For example, bed entry time t 入床 The time to go to sleep may be determined at least in part based on, for example, data generated by motion sensor 218, microphone 220, camera 232, or any combination thereof. The time to go to sleep may be determined at least in part based on, for example, data from motion sensor 218 (e.g., data indicating that the user is not moving), data from camera 232 (e.g., data indicating that the user is not moving and / or the user has turned off the light), data from microphone 220 (e.g., data indicating that the user has turned off the TV), data from user device 260 (e.g., data indicating that the user is no longer using user device 260), data from pressure sensor 212 and / or flow sensor 214 (e.g., data indicating that the user has turned on respiratory therapy device 110, data indicating that the user has put on user interface 120, etc.), or any combination thereof.

[0111] Reference now Figure 5A , Figure 5B and Figure 5C , data related to the use of a vehicle by an individual (such as user 20) can be analyzed to determine the individual's risk factors for developing and / or suffering from a condition. In some cases, the condition is sleep disordered breathing (SDB), such as obstructive sleep apnea (OSA). In other cases, the condition includes allergies, colds, dry throat, sore throat, viral infection, pneumonia, chronic obstructive pulmonary disease (COPD), asthma, stroke, enlarged tonsils, hypertension, hypotension, or any combination thereof. The present disclosure will refer generally to "conditions" or specifically to SDB. However, those skilled in the art will understand that reference to a condition or specifically to SDB can generally refer to any number of conditions that an individual may develop, not just SDB.

[0112] Often, determining whether an individual already has these conditions (or other conditions) or is at risk of developing these conditions (or other conditions) will be referred to as determining the individual's risk factors for these conditions (or other conditions). Figure 5A , Figure 5B and Figure 5C Illustrated is how data related to an individual's vehicle use may be collected to determine the individual's risk factors for these conditions.

[0113] FIG. 5A to FIG. 5C Individual 502 is shown operating vehicle 500, which in some embodiments may be considered to be Figure 110. Vehicle 500 includes a rearview mirror 504 having an embedded image sensor 506. Image sensor 506 may be a camera (such as camera 232 of system 10), an IR sensor (such as IR sensor 234 of system 10), or various other types of image sensors. Image sensor 506 is operable to generate image data that can be reproduced as one or more images and / or one or more videos of individuals 502 within vehicle 500. Image sensor 506 has a field of view 508 that will generally include individual 502. Objects within field of view 508 will generally be shown in any images and / or videos generated from the image data. Rearview mirror 504 may include an additional image sensor (not shown) that faces outwardly away from individual 502 toward the road (or any surface on which vehicle 500 travels). Image data from the additional image sensor may be used to analyze the movement of the vehicle.

[0114] The vehicle 500 also includes a center console 510, a data port 512, and a mobile device 514 installed in view of the individual 502, as well as the data port 512. In the illustrated embodiment, the center console 510 is a display device that displays various information and can be used by the individual 502 to adjust various settings in the vehicle 500, such as climate control settings and audio settings. The center console 510 can also have other forms.

[0115] In the illustrated embodiment, the mobile device 514 (which may be the same as or similar to the user device 260 of the system 10) is a smartphone of the individual 502 and may be mounted on the dashboard of the vehicle 500 so that the individual 502 may use the mobile device 514 for various purposes, such as as a GPS device. The mobile device 514 may include additional sensors (e.g., image sensors, optical sensors, vibration sensors, etc.) that may generate data associated with the individual 502 operating the vehicle.

[0116] The data port 512 allows access to data stored in the onboard control system of the vehicle 500. When the vehicle is running, various different data can be stored in the control system of the vehicle 500. The data can include data related to the operation of the vehicle 500 (e.g., speed, acceleration, tire movement, movement of the vehicle 500, location of the vehicle 500, etc.), data related to the health of the vehicle 500, data related to various settings and / or features of the vehicle 500 (e.g., climate control settings, position of the driver's seat, activation of heated or cooled seats, activation of massage features in the seats, etc.). A device can be electrically coupled to the data port 512 (e.g., wired connection, wireless connection, or both) to collect data stored in the control system of the vehicle 500.

[0117] Any suitable device may be electrically coupled to the data port 512 to collect data, such as the mobile device 514, the user device 260 of the system 10 (if the user device 260 is different from the mobile device 514), the control system 200 of the system 10, and other devices. The vehicle 500 may include additional or alternative sensors that may be used to analyze the individual 502 and the individual's 502 use of the vehicle 500. In some cases, some or all of these sensors store data in the control system of the vehicle 500, where the data may be collected via the data port 512. In other cases, some or all of these sensors do not store data in the control system of the vehicle, but rather store data locally and / or transmit data to other devices separate from the control system of the vehicle 500 (such as the mobile device 514, the user device 260 of the system 10, the control system 200 of the system 10, etc.).

[0118] Data related to the operation of vehicle 500 by individual 502 may be analyzed to help determine risk factors for individual 502. For example, individuals with SDB (or at risk of developing SDB) may show signs if they feel tired during the day when they are driving. Image data generated by image sensor 506 (or any other image sensor) may be analyzed to determine whether individual 502 shows signs of tiredness or sleepiness, such as blinking faster than usual, nodding, etc. Image data may also be analyzed to determine characteristics related to the individual, such as the individual's posture, weight, neck circumference, waist circumference, etc. The operation of vehicle 500 may also be analyzed to help determine risk factors for individual 502. For example, if individual 502 feels tired during the day, the individual may turn up the volume of the radio, activate the massage function of their seat, activate the heating or cooling settings of their seat, and other actions. Data showing these actions may be generated and analyzed to help determine risk factors for individual 502. In another example, an individual 502 who feels tired due to SBD during the day may drive erratically, such as swerving in and out of the lane, braking harder than an average driver, etc. Data related to the operation of the vehicle may be analyzed to help determine risk factors for individual 502 .

[0119] Figure 5A Inset view 520A of individual 502 is shown when individual 502 is fully alert. In general, inset view 520A is an example of an image and / or video generated from image data from image sensor 506. As can be seen in inset view 520A, individual 502's head is facing upward and eyes are open, indicating that individual 502 is not showing any signs of fatigue. Individual 502 also has their hands in a standard position on steering wheel 516 of vehicle 500 and is keeping vehicle 500 within a lane on the road. Thus, the analysis is from Figure 5AThe data obtained from the scenario in may indicate that the risk factor for individual 502 is low.

[0120] exist Figure 5B , individual 502 is showing signs of fatigue. As can be seen in embedded view 520B, individual 502's head has begun to droop, and individual 502's eyes are half closed. Individual 502 has begun to inadvertently rotate steering wheel 516 clockwise so that vehicle 500 has begun to pull out of line. Individual 502 has also begun to inadvertently reduce the speed of vehicle 500. Image data generated by image sensor 506 (or any other image sensor) can be analyzed to detect movement of individual 502's head and eyes, which can be used to determine risk factors for individual 502. Additional data related to the movement of the vehicle can be obtained via data port 512, which can also be used to determine risk factors for individual 502.

[0121] exist Figure 5C , individual 502 is showing more severe signs of fatigue. As shown in inset view 520C, individual 502's head is tilted to the side, and individual 502's eyes are closed. Individual 502 also continues to inadvertently rotate steering wheel 514 further clockwise, causing vehicle 500 to continue to turn out of the lane. Individual 502 also continues to inadvertently reduce the speed of vehicle 500. Similar to Figure 5B , image data generated by image sensor 506 and data related to the movement of the vehicle may be analyzed to determine risk factors for individual 502 for a condition such as SDB.

[0122] Figure 6 A method 600 is illustrated for determining a likelihood that an individual suffers from a condition based at least in part on data associated with operation of a vehicle (which may be the same as or similar to vehicle 500). The condition may be a sleep apnea-related condition, such as SDB or OSA. The condition may also be any other type of condition, including allergies, colds, dry throat, sore throat, viral infection, pneumonia, chronic obstructive pulmonary disease (COPD), asthma, stroke, swollen tonsils, high blood pressure, low blood pressure, or any combination thereof.

[0123] Method 600 may utilize data generated by a variety of different sensors, such as any of sensors 210 of system 10. Method 600 may also utilize data generated by a respiratory therapy device (such as respiratory therapy device 110) of a respiratory therapy system (such as respiratory therapy system 100), data generated by a user device (such as user device 260 of system 10), data generated by an activity tracker (such as activity tracker 270 of system 10), data generated by a blood pressure device (such as blood pressure device 280 of system 10), and / or data from any other source. Method 600 may be implemented using a control system (such as control system 200 of system 10). Portions of method 600 may be implemented using additional or alternative devices, such as an individual's mobile device, laptop, tablet, etc., all of which may be user devices 260 of system 10.

[0124] At step 602 of method 600, data associated with a plurality of driving sessions is generated. During the driving sessions, the individual whose risk factors are being determined is typically located in a vehicle. During some or all of the driving sessions, the individual is seated in the driver's seat and is operating the vehicle. However, the individual need not be the person operating the vehicle. During some or all of the driving sessions, the individual may be a passenger in the vehicle while another person operates the vehicle (or if the vehicle is an autonomous vehicle, the vehicle itself operates).

[0125] A driving period may be defined in a number of different ways. In some cases, a driving period begins when an individual enters a vehicle and / or when the vehicle leaves its origin (such as the individual's house), and ends when the individual's vehicle reaches its destination (such as the individual's workplace) and / or when the individual leaves the vehicle. In some embodiments, a driving period may include only the time during that trip when the vehicle is actually being operated. Thus, if a vehicle stops and shuts down anywhere (such as at a gas station or rest stop), the time period spent at that location is not considered part of a driving period, and data associated with that time period will not be analyzed.

[0126] However, in other embodiments, the driving period includes all the time between the vehicle leaving its starting point and arriving at its destination, regardless of how many times the vehicle stops during the trip. In other embodiments, the time period in which the vehicle stops is excluded from the driving period only when the vehicle stops for a threshold amount of time. Therefore, if an individual spends 5 minutes at a gas station to fill up the tank of the vehicle and then continues towards the destination, this time period can be considered as part of the driving period. However, if an individual stays at a certain location for a long time (e.g., 20 minutes, 1 hour, etc.), this time period is excluded. In some cases, the trip between the starting point and the stop is the first driving period, and the trip between the stop and the destination is the second driving period. In other cases, the trip between the starting point and the destination is a single driving period, and data associated with the time period of the vehicle at the stop is excluded.

[0127] Typically, stops made to comply with traffic regulations while the vehicle is still being operated (e.g., the engine is on, but the vehicle is parked, or not moving because the driver has the brake pedal depressed) are not excluded from the driving period. Thus, if a vehicle is stopped at a red light for one minute, data associated with that one minute of time is considered part of the driving period even though the vehicle is not moving. However, in some embodiments, this data may be excluded from the driving period. Thus, the driving period may include only the time when the vehicle is actually moving, or may include both the time when the vehicle is moving and the time when the vehicle is stopped.

[0128] The data may be generated in any suitable manner. In some cases, such as in this article regarding FIG. 5A to FIG. 5C As discussed, the data may be generated by sensors or other components of the vehicle itself and then downloaded from the vehicle's control system. In some cases, additional or alternative data may be generated using individual devices such as smart phones, smart watches, etc.

[0129] Step 604 of method 600 includes analyzing data generated during multiple driving periods. In some embodiments, the data associated with the multiple driving periods include one or more parameters associated with each of the multiple driving periods. These parameters can be analyzed to determine the risk factors of an individual. These parameters can generally be divided into four different categories as described in detail below. However, other parameters not mentioned in this article, and other parameters that do not necessarily belong to any of the following four categories, can also be analyzed.

[0130] The first category of parameters includes parameters associated with the individual's body. These parameters may include the individual's posture, the individual's weight, the individual's neck circumference, the individual's waist circumference, the ratio of the individual's neck circumference to the individual's waist circumference, the individual's eye movement amount (e.g., whether the individual is focused on the road), the individual's eye movement speed (e.g., how quickly the individual looks around), the individual's blinking amount, the individual's any head movement amount, the individual's any head movement speed, the individual's any head movement direction, the individual's body movement amount, the individual's weight distribution on the vehicle seat (e.g., whether the individual's weight is forward when the individual attempts to stay awake, whether the individual's weight is reclined (which can indicate fatigue), etc.), the strength and / or position of the individual's grip on the vehicle steering wheel, the frequency of movement of the individual's grip on the vehicle steering wheel, and other parameters.

[0131] Typically, these parameters can indicate that an individual suffers from SDB or is at risk of developing SDB. Large weight, neck circumference, waist circumference, and the ratio of neck circumference to waist circumference can be indications that an individual suffers from SDB or is at risk of developing SDB. Certain movements of the individual's head (e.g., nodding up and down while driving), eyes (e.g., blinking more frequently to stay awake) and / or body (movements of restlessness and / or fidgeting, such as continuous or nearly continuous movement of individual arms and legs) can indicate that the individual is tired during the day, which can be an indication that the individual is suffering from SDB or is at risk of developing SDB. Tired individuals may also move their eyes faster or slower, and may tend to focus their gaze on a target for a longer or shorter period of time. The individual's grip on the steering wheel can also indicate that the individual is tired during the day. For example, weak grip strength, specific grip positions on the steering wheel (e.g., hands at 10:00 and 2:00, one hand at the bottom, hands and / or wrists at certain angles, etc.), and frequently changing grip positions can all indicate that the individual is tired and trying to stay awake. In general, any combination of the above parameters and / or other parameters can be determined to help determine risk factors for an individual.

[0132] The second category of parameters includes parameters associated with the environment around the individual during multiple driving periods. These parameters may include ambient light level, temperature in the vehicle, temperature outside the vehicle, ambient sound level and other parameters. These parameters may indicate that the individual is tired during the day. For example, loud sounds in the vehicle (e.g., if the radio is played at a high volume) may indicate that the individual is trying to stay awake. In another example, in order to try to stay awake, the individual may run the air conditioner at a high level (e.g., the temperature in the car is relatively much colder than the temperature outside the car). An increase in the light level in the car may indicate that the individual has turned on one or more lights in the car to try and stay awake.

[0133] The third category of parameters includes parameters associated with the vehicle itself. The parameters in this category are generally related to the movement of the vehicle during operation, or different functions of the vehicle, etc. These parameters can include the volume of the audio being played by the vehicle (which is usually a narrower version of the ambient sound level parameter in the second category above), the state of the vehicle's seat massage function, the reclining angle of the individual's seat in the vehicle, whether the vehicle is the individual's main vehicle, the speed of the vehicle, the acceleration of the vehicle, the braking frequency of the vehicle, the braking force of the vehicle, and the lateral movement of the vehicle, etc. Parameters related to the settings of the vehicle can indicate that the individual is trying to stay awake. For example, an individual who plays vehicle audio (e.g., radio or music) at a high volume, activates a seat massage function, or makes the seat at a relatively upright angle can indicate that the individual is trying to stay awake. If these occur during the day, this can indicate that the individual is experiencing daytime fatigue, which may increase the risk of suffering from or developing SDB. Parameters related to the movement of the vehicle can indicate whether the individual is difficult to stay awake while driving. For example, braking too frequently, braking with too much force, irregular acceleration, and detouring inside and outside the vehicle's lane can all indicate that the individual is tired and difficult to control the vehicle. Parameters related to whether a vehicle is the individual's primary vehicle may be used as a check on other parameters in some embodiments. If the vehicle is not the individual's primary vehicle (e.g., a rental car, a spouse's car, etc.), the other parameters may not strongly indicate the individual's risk of developing or suffering from the condition. For example, if the individual is not accustomed to driving a vehicle, they may operate the brakes too frequently or with too much force. Thus, while frequent and / or forceful braking may otherwise indicate that the individual is showing signs of daytime fatigue, they do not do so here because the vehicle is not the individual's primary vehicle.

[0134] The fourth category of parameters includes parameters related to the driving session itself. These parameters may include the starting point and destination of the vehicle for each driving session, the distance traveled during each driving session, the time span of each driving session, the frequency of taking breaks during each driving session, the time span of any breaks taken during each driving session, the start time and end time of each driving session (which can be used to determine the time span of each driving session), and other parameters. In some cases, a sedentary lifestyle may increase the risk that an individual will develop SDB. Therefore, these parameters can be analyzed to determine whether the driving session indicates that the individual is relatively sedentary. For example, longer driving sessions with relatively few breaks may indicate that the individual is sedentary and is at an increased risk of developing SDB.

[0135] Typically, each of these parameters has a value. The values ​​of these parameters can be digital quantities or ranges (e.g., the neck circumference of an individual, the current speed of a vehicle, etc.). The values ​​of these parameters can also be binary values ​​corresponding to an on state or an off state. Other possible values ​​can also be used. The values ​​of these parameters can be analyzed in various different ways at step 604.

[0136] In optional step 604A, for each driving period in a plurality of driving periods, determine any one or more values ​​of the parameters. Therefore, if there are 10 different parameters being analyzed and 10 different driving periods, there will be 100 unique parameter values. In some embodiments, determine the absolute values ​​of these parameters. In other embodiments, determine the relative values ​​of these parameters. This relative value can be relative to any suitable baseline, such as the baseline value of an individual, the baseline value of an individual group, etc. The group to which an individual belongs can be based on many different factors, such as age, height, weight, sex, medical condition, occupation, driving frequency, etc. In some embodiments, the parameter values ​​are all determined in the same way (e.g., all absolute values, all values ​​related to the baseline of an individual, all values ​​related to the group baseline, etc.). In other embodiments, the values ​​of different parameters (e.g., the absolute values ​​of some parameters, the values ​​related to the baseline of an individual of other parameters, the values ​​related to the group baseline of other parameters, etc.) can be determined differently.

[0137] The baseline value for an individual may change depending on the circumstances. For example, in some embodiments, the baseline value for an individual is a baseline value when it is known that the individual has not yet developed SDB and / or is not at risk of developing SDB. Similarly, the baseline value for a population of individuals may include only individuals that are known not to suffer from SDB and / or are not at risk of developing SDB. In other embodiments, the baseline value for an individual is a simple rolling average of a parameter across one or more driving periods, regardless of whether the individual suffers from SDB or is at risk of developing SDB during any of these driving periods. Similarly, the baseline value for a population of individuals may be simply a rolling average of a parameter for multiple individuals and / or driving periods, regardless of whether any of the individuals suffers from SDB or is at risk of developing SDB during any of these driving periods.

[0138] In optional step 604B, the change of the value of one or more parameters between two driving periods is determined. Typically, the change of the value between two driving periods is the difference between the values. In some cases, a single change of the value of the parameter between two driving periods is determined. In other cases, multiple changes of the value of the parameter between two driving periods can be determined. For example, if there are three different driving periods being analyzed, step 604B may include determining the change of the parameter value between the first driving period and the second driving period and between the second driving period and the third driving period. In other cases, step 604B includes determining only one change of the value of some parameters, while determining multiple changes of the value of other parameters. In some cases, the two driving periods during which the change of the value is determined are continuous driving periods. In other cases, the two driving periods during which the change of the value is determined are not continuous driving periods, and are separated by at least one additional driving period.

[0139] In optional step 604C, the rate of change of the value of one or more parameters across at least two of the driving periods is determined. In some embodiments, the rate of change is the average change of the value per driving period. In other embodiments, the rate of change is the average change of the value per unit time. The rate of change can also be a rolling average change per driving period or per unit time. In embodiments where the rate of change is the average change of the value per driving period across two driving periods, the rate of change in step 604C will generally be the same as the change between the two driving periods in step 604B.

[0140] In some embodiments, the parameter may be weighted differently depending on whether the individual is operating a vehicle. In these embodiments, a first weighting may be applied to the parameter value associated with the driving period in which the individual is operating the vehicle (e.g., the original value, the change in value, the rate of change of the value, or other quantification of the parameter), and a second weighting may be applied to the parameter value associated with the driving period. The first weighting and the second weighting are typically different from each other and will typically include unique values ​​for each different parameter.

[0141] Additionally, some parameters are weighted more heavily if the individual is driving, while other parameters are weighted more heavily if the individual is not driving. For example, if the individual is driving, the recline angle of the driver's seat may not be weighted much because, regardless of whether the individual is tired or not, they cannot recline their seat very much. However, if the individual is not driving and is sitting in the passenger seat, the recline angle of the passenger seat may be more indicative of the individual's risk factors because the individual is free to recline the passenger seat at any angle. In another example, the movement of the vehicle may be weighted more heavily when the individual is driving than when the individual is not driving.

[0142] Finally, at step 606 of method 600, a risk factor for the individual for the condition can be determined based at least in part on the analyzed data. In some embodiments, the risk factor indicates the onset of the condition or the severity of the condition. Thus, the risk factor can indicate to the individual whether they are at risk of developing the condition, or if the individual has already developed the condition, how severe the condition is. In some embodiments, the risk factor is a percentage probability that the individual will develop the condition or has already developed the condition. In some embodiments, the risk factor is an estimated time period during which the individual will develop the condition.

[0143] In some embodiments, the data is analyzed in a variety of different ways to determine the risk factors for the individual. For example, determining the risk factors for the individual at step 606 can be based on the value of one or more parameters (step 604A) and the change in the value of one or more parameters (step 604B), the value of one or more parameters (step 604A) and the rate of change of the value of one or more parameters (step 604C), or all three. Additional or alternative analysis methods can be used on the data to determine the risk factors for the individual.

[0144] In some cases, step 606 includes inputting parameter values, changes in values, rates of change of values, and / or any other data into a trained model. The trained model can process these inputs and output risk factors. The model can be trained using a training data set that has been collected from individuals who have developed SDB (or another disease of interest). Typically, the training data set for each individual will include various parameter values, changes in values, and rates of change of values, as well as some indication of the temporal relationship between the time when the inputs were obtained and the time when the individual developed the disease. Therefore, for a specified individual, the training data set can include parameters obtained from multiple driving periods, wherein the parameters include some kind of timestamp that indicates when the parameters were generated relative to when the individual developed the disease.

[0145] Any suitable model can be trained and used to generate risk factors. In some embodiments, the model is an artificial neural network trained using any suitable training algorithm. However, other models such as linear regression models, naive Bayes classifiers, clustering-based models, etc. can also be used.

[0146] like Figure 6As shown, method 600 may also include optional steps 608 and 610. In optional step 608, data associated with the individual outside of the plurality of driving sessions is generated. In optional step 610, risk factors are adjusted based on the external data. The external data may include any relevant data that is not typically associated with any of the driving sessions, such as demographic information associated with the individual (e.g., age, gender, race, family medical history, etc.), sleep data associated with the individual (e.g., sleep-related parameters associated with one or more sleep sessions of the individual), activity data associated with any activity or exercise performed by the individual, food data associated with food eaten by the individual, calendar data associated with entries in the calendar of the individual, and various other data.

[0147] In some cases, external data may indicate that an individual's risk factors are wrong. For example, if an individual was very active the day before a driving session (e.g., the individual went hiking) and did not sleep much that night, data from the next day's driving session may indicate that the individual is experiencing daytime fatigue. However, in this case, daytime fatigue is not an indication that the individual is developing SDB (or another condition), but simply because the individual was very active and did not sleep much the day before (for reasons unrelated to the condition). Therefore, risk factors may be adjusted based on this external data to account for the individual's activity and lack of sleep.

[0148] Generally, method 600 is shown as determining risk factors for an individual based on data from multiple driving sessions. However, in some embodiments, the determination of risk factors may be based on data from only a single driving session.

[0149] The method 600 may include additional steps. For example, in some embodiments, the method 600 includes taking an action based at least in part on the individual's risk factors for the condition. In some cases, the action may include notifying the individual of the determined risk factors and recommending a follow-up action by the individual. The follow-up action may include visiting a health care provider, starting treatment with a respiratory therapy system (such as the respiratory therapy system 100 of system 10), updating the treatment settings of the respiratory therapy system if the individual is already using a respiratory therapy system, and other actions.

[0150] The method 600 may also include collecting feedback data to perform a review of the determined risk factors. For example, the method 600 may include prompting the individual to answer questions related to the driving period, or otherwise provide data related to the driving period (e.g., by inputting information about the driving period into a device such as the user device 260 of the system 10). Based on the feedback data, the individual's risk factor may be reviewed to ensure that the risk factor is accurate, and then updated if necessary.

[0151] In general, method 600 can be implemented using a system having a control system having one or more processors and a memory storing machine-readable instructions. The control system can be coupled to the memory, and method 600 can be implemented when the machine-readable instructions are executed by at least one of the processors of the control system. Method 600 can also be implemented using a computer program product (such as a non-transitory computer-readable medium) including instructions that, when executed by a computer, cause the computer to perform the steps of method 600.

[0152] In some embodiments, the vehicle is a shared mobility vehicle that the individual does not own and is also driven by other individuals. In these embodiments, most of the data associated with the driving session will be collected by the individual's mobile device so that the individual's driving session can still be monitored. In some cases, the individual may have a key fob that the individual carries with them while driving the vehicle. The key fob is capable of generating, collecting, and storing data associated with the driving session.

[0153] Alternative Implementation

[0154] Embodiment 1. A method for determining the likelihood that an individual has or will develop a condition, the method comprising: generating data associated with a plurality of driving periods during the plurality of driving periods, the individual being in a vehicle during at least a portion of each of the plurality of driving periods; analyzing the data; and determining a risk factor for the individual associated with the condition based at least in part on the analyzed data.

[0155] Embodiment 2. The method of embodiment 1, wherein the data associated with the plurality of driving periods comprises one or more parameters associated with each of the plurality of driving periods.

[0156] Embodiment 3. A method according to embodiment 2, wherein analyzing the data includes determining a value for each of the one or more parameters for each of the multiple driving periods, and wherein the risk factor is determined at least in part based on the determined value for each of the one or more parameters for at least one of the multiple driving periods.

[0157] Embodiment 4. A method according to embodiment 2 or embodiment 3, wherein analyzing the data includes determining a change in the value of at least one of the one or more parameters between a first driving period in the plurality of driving periods and a second driving period in the plurality of driving periods, and wherein the risk factor is determined at least in part based on the determined change in the value of at least one of the one or more parameters.

[0158] Embodiment 5. A method according to any one of embodiments 2 to 4, wherein analyzing the data includes determining a rate of change of the value of at least one of the one or more parameters across two or more of the multiple driving periods, and wherein the risk factor is a factor determined at least in part based on the determined range of change of the value of the at least one of the one or more parameters.

[0159] Embodiment 6. A method according to any one of embodiments 2 to 5, wherein the one or more parameters for each of the multiple driving periods include one or more parameters associated with the individual's body, one or more parameters associated with the environment around the individual during the multiple driving periods, one or more parameters associated with the vehicle, one or more parameters associated with the multiple driving periods, or any combination thereof.

[0160] Embodiment 7. A method according to embodiment 6, wherein the one or more parameters associated with the body of the individual include the posture of the individual, the weight of the individual, the neck circumference of the individual, the waist circumference of the individual, the ratio of the neck circumference of the individual to the waist circumference of the individual, the amount of eye movement of the individual, the eye movement speed of the individual, the amount of blinking of the individual, the amount of head movement of the individual, the head movement speed of the individual, the direction of head movement of the individual, the weight distribution of the individual on the seat of the vehicle, the strength of the individual's grip on the steering wheel of the vehicle, or any combination thereof.

[0161] Embodiment 8. A method according to embodiment 6 or embodiment 7, wherein the one or more parameters associated with the environment around the individual during the multiple driving periods include ambient light level, temperature inside the vehicle, temperature outside the vehicle, ambient sound level, or any combination thereof.

[0162] Embodiment 9. A method according to any one of embodiments 6 to 8, wherein the one or more parameters associated with the vehicle include the volume of audio being played in the vehicle, the state of a seat massager of the vehicle, the reclining angle of the individual's seat, whether the vehicle is the individual's primary vehicle, the speed of the vehicle, the acceleration of the vehicle, the braking frequency of the vehicle, the lateral movement of the vehicle, or any combination thereof.

[0163] Embodiment 10. A method according to any one of embodiments 6 to 9, wherein the one or more parameters associated with the multiple driving periods include a starting point of the vehicle for each of the multiple driving periods, a destination of the vehicle for each of the multiple driving periods, a time span of each of the multiple driving periods, a distance traveled during each of the multiple driving periods, a frequency of breaks taken during each of the multiple driving periods, a length of breaks taken during each of the multiple driving periods, a start time of each of the multiple driving periods, an end time of each of the multiple driving periods, or any combination thereof.

[0164] Embodiment 11. A method according to any one of Embodiments 1 to 10, wherein during each of the plurality of driving periods, the individual is operating the vehicle or is a passenger in the vehicle.

[0165] Embodiment 12. A method according to embodiment 11, wherein analyzing the data associated with the multiple driving periods includes applying a first weighting to data generated during driving periods in which the individual operates the vehicle, and applying a second weighting to data generated during driving periods in which the individual is a passenger in the vehicle, the first weighting being different from the second weighting.

[0166] Embodiment 13. A method according to any one of Embodiments 1 to 12, wherein each driving period begins when the individual enters the vehicle at a starting point and ends when the individual exits the vehicle at a destination.

[0167] Embodiment 14. The method of embodiment 13, wherein each driving period includes at least a first amount of time when the vehicle is moving and a second amount of time when the vehicle is not moving.

[0168] Embodiment 15. The method of embodiment 13, wherein each driving period includes only time when the vehicle is moving.

[0169] Embodiment 16. The method according to any one of embodiments 1 to 15 further includes: generating data associated with the individual outside of the multiple driving periods; and adjusting the risk factor based at least in part on the data associated with the individual outside of the driving periods.

[0170] Embodiment 17. A method according to embodiment 16, wherein the data associated with the individual outside of the multiple driving periods includes activity data associated with the individual, sleep data associated with the individual, calendar data associated with the individual, food data associated with the individual, or any combination thereof.

[0171] Embodiment 18. The method of any one of embodiments 1 to 17, wherein the risk factor is indicative of the onset of the condition or the severity of the condition.

[0172] Embodiment 19. A method according to any one of embodiments 1 to 18, wherein the risk factors include the percentage likelihood that the individual will develop the condition, the estimated time period within which the individual will develop the condition, the percentage likelihood that the individual currently suffers from the condition, or any combination thereof.

[0173] Embodiment 20. A method according to any one of embodiments 1 to 19, wherein the condition comprises sleep disordered breathing (SDB), allergies, colds, dry throat, sore throat, viral infection, pneumonia, chronic obstructive pulmonary disease (COPD), asthma, stroke, enlarged tonsils, high blood pressure, low blood pressure, or any combination thereof.

[0174] Embodiment 21. A method according to any one of embodiments 1 to 20, wherein the data is generated by one or more sensors located within the vehicle, the one or more sensors including at least one image sensor.

[0175] Embodiment 22. The method of embodiment 21, wherein the at least one image sensor comprises a camera coupled to a rearview mirror of the vehicle and facing the individual when the individual is within the vehicle.

[0176] Embodiment 23. A method according to embodiment 21 or embodiment 22, wherein the at least one image sensor includes a camera facing away from the individual toward the outside of the vehicle.

[0177] Embodiment 24. A method according to any one of embodiments 1 to 23, wherein the data is generated by a mobile device of the individual.

[0178] Embodiment 25. A method according to any one of embodiments 1 to 23, wherein the data is generated by a control system of the vehicle.

[0179] Embodiment 26. A system for determining the likelihood that an individual has or will develop a condition, the system comprising: a control system including one or more processors; and a memory having machine-readable instructions stored thereon; wherein 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, a method according to any one of embodiments 1 to 25 is implemented.

[0180] Embodiment 27. A system for determining the sleep stage of an individual, the system comprising a control, the system comprising a control system having one or more processors, the one or more processors configured to implement the method according to any one of embodiments 1 to 25.

[0181] Embodiment 28. A computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method according to any one of embodiments 1 to 25.

[0182] Embodiment 29. The computer program product of embodiment 28, wherein the computer program product is a non-volatile computer-readable medium.

[0183] One or more elements or aspects or steps or any portions thereof from one or more of any one of the claims may be combined with one or more elements or aspects or steps or any portions thereof from one or more of any one of the other claims or a combination thereof to form one or more additional embodiments and / or claims of the present disclosure.

[0184] Although the present disclosure has been described with reference to one or more specific embodiments or implementations, those skilled in the art will recognize that many changes may be made thereto without departing from the spirit and scope of the present disclosure. Each of these implementations and obvious variations thereof are contemplated as falling within the spirit and scope of the present disclosure. It is also contemplated that additional implementations according to various aspects of the present disclosure may combine any number of features from any of the implementations described herein.

Claims

1. A method for determining the likelihood that an individual has or will develop a condition, the method comprising: generating data associated with a plurality of driving periods during a plurality of driving periods, the individual being in a vehicle during at least a portion of each of the plurality of driving periods; analyzing the data; as well as A risk factor for the individual associated with the condition is determined based at least in part on the analyzed data. 2 . The method of claim 1 , wherein the data associated with the plurality of driving periods comprises one or more parameters associated with each of the plurality of driving periods.

3. The method of claim 2 , wherein analyzing the data comprises determining a value for each of the one or more parameters for each of the plurality of driving periods, and wherein the risk factor is determined based at least in part on the determined value for each of the one or more parameters for at least one of the plurality of driving periods.

4. The method of claim 2 or 3, wherein analyzing the data comprises determining a change in the value of at least one of the one or more parameters between a first driving period in the plurality of driving periods and a second driving period in the plurality of driving periods, and wherein the risk factor is determined based at least in part on the determined change in the value of the at least one of the one or more parameters.

5. The method of any one of claims 2 to 4, wherein analyzing the data comprises determining a rate of change of the value of at least one of the one or more parameters across two or more of the plurality of driving periods, and wherein the risk factor is a factor determined based at least in part on the determined range of change of the value of the at least one of the one or more parameters.

6. The method according to any one of claims 2 to 5, wherein the one or more parameters for each of the multiple driving periods include one or more parameters associated with the individual's body, one or more parameters associated with the environment around the individual during the multiple driving periods, one or more parameters associated with the vehicle, one or more parameters associated with the multiple driving periods, or any combination thereof.

7. The method of claim 6, wherein the one or more parameters associated with the body of the individual include the individual's posture, the individual's weight, the individual's neck circumference, the individual's waist circumference, the ratio of the individual's neck circumference to the individual's waist circumference, the individual's eye movement amount, the individual's eye movement speed, the individual's blinking amount, the individual's head movement amount, the individual's head movement speed, the individual's head movement direction, the individual's weight distribution on the vehicle's seat, the strength of the individual's grip on the vehicle's steering wheel, or any combination thereof.

8. The method of claim 6 or 7, wherein the one or more parameters associated with the environment surrounding the individual during the plurality of driving periods include ambient light level, temperature inside the vehicle, temperature outside the vehicle, ambient sound level, or any combination thereof.

9. The method of any one of claims 6 to 8, wherein the one or more parameters associated with the vehicle include the volume of audio being played in the vehicle, the status of a seat massager of the vehicle, the reclining angle of the individual's seat, whether the vehicle is the individual's primary vehicle, the speed of the vehicle, the acceleration of the vehicle, the braking frequency of the vehicle, the amount of lateral movement of the vehicle, or any combination thereof.

10. The method of any one of claims 6 to 9, wherein the one or more parameters associated with the plurality of driving periods include a starting point of the vehicle for each of the plurality of driving periods, a destination of the vehicle for each of the plurality of driving periods, a time span of each of the plurality of driving periods, a distance traveled during each of the plurality of driving periods, a frequency of taking breaks during each of the plurality of driving periods, a length of taking breaks during each of the plurality of driving periods, a start time of each of the plurality of driving periods, an end time of each of the plurality of driving periods, or any combination thereof.

11. The method of any one of claims 1 to 10, wherein during each of the plurality of driving periods, the individual is operating the vehicle or the individual is a passenger in the vehicle.

12. The method of claim 11 , wherein analyzing the data associated with the plurality of driving periods comprises applying a first weighting to data generated during driving periods in which the individual was operating the vehicle and applying a second weighting to data generated during driving periods in which the individual was a passenger in the vehicle, the first weighting being different from the second weighting.

13. The method of any one of claims 1 to 12, wherein each driving period begins when the individual enters the vehicle at a starting point and ends when the individual exits the vehicle at a destination.

14. The method of claim 13, wherein each driving period includes at least a first amount of time when the vehicle is moving and a second amount of time when the vehicle is not moving.

15. The method of claim 13, wherein each driving period includes only time when the vehicle is moving.

16. The method according to any one of claims 1 to 15, further comprising: generating data associated with the individual outside of the plurality of driving periods; as well as The risk factor is adjusted based at least in part on the data associated with the individual outside of the driving period.

17. The method of claim 16, wherein the data associated with the individual outside of the plurality of driving periods comprises activity data associated with the individual, sleep data associated with the individual, calendar data associated with the individual, food data associated with the individual, or any combination thereof.

18. The method of any one of claims 1 to 17, wherein the risk factor is indicative of the onset of the condition or the severity of the condition.

19. The method of any one of claims 1 to 18, wherein the risk factors include the percentage likelihood that the individual will develop the condition, the estimated time period within which the individual will develop the condition, the percentage likelihood that the individual currently suffers from the condition, or any combination thereof.

20. The method of any one of claims 1 to 19, wherein the condition comprises sleep disordered breathing (SDB), allergies, colds, dry throat, sore throat, viral infection, pneumonia, chronic obstructive pulmonary disease (COPD), asthma, stroke, enlarged tonsils, high blood pressure, low blood pressure, or any combination thereof.

21. A method according to any one of claims 1 to 20, wherein the data is generated by one or more sensors located within the vehicle, the one or more sensors including at least one image sensor.

22. The method of claim 21, wherein the at least one image sensor comprises a camera coupled to a rearview mirror of the vehicle and facing the individual when the individual is within the vehicle.

23. The method of claim 21 or 22, wherein the at least one image sensor comprises a camera facing away from the individual towards the exterior of the vehicle.

24. A method according to any one of claims 1 to 23, wherein the data is generated by a mobile device of the individual.

25. A method according to any one of claims 1 to 23, wherein the data is generated by a control system of the vehicle.

26. A system for determining the likelihood that an individual has or will develop a condition, the system comprising: a control system including one or more processors; as well as a memory having machine-readable instructions stored thereon; Wherein 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 according to any one of claims 1 to 25 is implemented.

27. A system for determining the sleep stage of an individual, the system comprising controls, the system comprising a control system having one or more processors, the one or more processors 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 according to any one of claims 1 to 25.

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

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