Evaluation of cohort sleep performance

Abstract

Certain aspects and features of the present disclosure relate to assessing the sleep performance of a cohort of individuals sleeping in a shared environment (e.g., a single bed, a single room, adjacent rooms in a single example, or a single household). Individual or collaborative sleep performance scores, and other sleep performance metrics, can be determined. If one individual is receiving treatment for a sleep-related and / or breathing disorder (e.g., with a respiratory therapy device), an assessment of the sleep performance of the entire cohort may be useful. The assessment of the cohort helps determine what actions can be taken to improve the sleep performance of all individuals in the cohort. In some cases, parameters of the user therapy device are adjusted based on the monitored sleep performance of other individuals in the cohort.

Description

Cross-reference of related applications

[0001] This application claims the rights to U.S. Provisional Patent Application No. 63 / 127,597, filed on 18 December 2020, entitled “Evaluation of Cohort Sleep Performance,” the disclosures of which are incorporated herein by full quotation. [Technical Field]

[0002] This disclosure relates to the treatment of general sleep conditions, and more specifically, to monitoring sleep performance in multi-person environments. [Background technology]

[0003] Many individuals suffer from sleep-related and / or respiratory disorders, such as periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), such as obstructive sleep apnea syndrome (OSA), central sleep apnea syndrome (CSA), other types of apnea, such as mixed apnea-hypopnea, respiratory effort-related awakening (RERA), Cheyne-Stokes respiration (CSR), respiratory failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular diseases (NMD), chest wall disorders, and insomnia. Sleep-related respiratory disorders may be associated with one or more events that may occur during sleep, such as snoring, apnea, hypoventilation, restless legs syndrome, sleep disturbances, choking, increased heart rate, dyspnea, asthma attacks, epilepsy, epileptic seizures, or any combination thereof. Individuals with such sleep-related respiratory disorders are typically treated with one or more medical devices to improve sleep and reduce the likelihood of events occurring during sleep. One example of such a device is a respiratory therapy system that can provide positive airway pressure to an individual, although other devices may also be used.

[0004] Furthermore, many individuals suffering from sleep-related and / or respiratory disorders sleep in the same environment as one or more other individuals. Any individual in the environment can potentially affect the sleep performance of another individual in that environment. Examples include interruptions by exercise or noise (e.g., the first bed partner getting into bed after the second bed partner has entered the early stages of sleep), interruptions by normal sleep hygiene (e.g., the timing of using digital devices or eating before sleep), and other factors. Treatment of individuals suffering from sleep-related and / or respiratory disorders not only affects the sleep performance of healthy individuals, but actions by healthy individuals also affect the sleep performance of individuals suffering from sleep-related and / or respiratory disorders.

[0005] It is necessary to provide meaningful metrics regarding an individual's sleep performance in an environment that includes one or more other individuals. Such meaningful metrics can be used to improve the overall sleep quality of one or more individuals in an environment, for example, by improving sleep therapy compliance and user participation, identifying actions that positively impact sleep performance, and otherwise improving sleep performance. [Overview of the project]

[0006] Certain aspects and features of the present disclosure include the steps of: receiving sensor data from one or more sensors related to an individual's sleep session in an environment; determining first sleep performance data from the sensor data; receiving second sleep performance data related to a sleep session of a different user of a respiratory therapy device in an environment; generating one or more sleep performance metrics using the first sleep performance data and the second sleep performance data; and presenting the one or more sleep performance metrics.

[0007] In some cases, the one or more sleep performance metrics include i) a coordinated sleep performance score, ii) an individual sleep performance score related to the individual, iii) an individual sleep performance score related to the user, iv) a sleep progression chart related to the individual, v) a sleep progression chart related to the user, vi) the user's treatment score, vii) a resonance score, or viii) any combination of i-vii. In some cases, the individual's sleep sessions and the user's sleep sessions overlap in time. In other cases, the individual's sleep sessions and the user's sleep sessions do not overlap in time, such as when either the individual or the user is a shift worker with a unique sleep-wake pattern. In some cases, the first sleep performance data includes sleep stage information or sleep state information, and the second sleep performance data includes respiratory therapy device usage information. In some cases, the step of generating one or more sleep performance metrics includes the step of generating a coordinated sleep performance score, the step of generating the coordinated sleep performance score includes the step of generating a first sleep performance score using the first sleep performance data, the step of generating a second sleep performance score using the second sleep performance data, and the step of generating a coordinated sleep performance score using the first sleep performance score and the second sleep performance score.

[0008] In some cases, the method further includes the steps of: receiving goal information relating to a first user and a second user, wherein the goal information indicates a goal relating to i) the individual's sleep session, ii) the user's sleep session, or iii) a combination of i and ii; generating a goal state update, which includes evaluating the goal information using i) the first sleep performance data, ii) the second sleep performance data, or iii) a combination of i and ii; and outputting the goal state update. In some cases, the step of receiving goal information includes generating a set of one or more proposed goals; and accepting the selection of a goal selected from the set of proposed goals. In some cases, the step of generating a set of suggested goals includes: submitting a questionnaire containing one or more questions; receiving response information in response to the presentation of the questionnaire; and generating a set of suggested goals using the received response information. In some cases, the step of generating a set of suggested goals includes: accessing historical sleep performance data related to historical sleep performance metrics; identifying one or more factors that influence the historical sleep performance metrics; determining, for each of the one or more factors, suggested actions that are estimated to improve future sleep performance metrics; and generating a set of suggested goals using the suggested actions for each of the one or more factors. In some cases, the step of generating a set of suggested goals includes: receiving demographic information related to the individual or user; and generating a set of suggested goals using the received demographic information. In some cases, the step of generating a set of suggested goals includes: receiving historical respiratory therapy device usage information related to the user; and generating a set of suggested goals using the received historical respiratory therapy device usage information. In some cases, the step of generating a set of suggested goals includes: receiving subjective feedback related to multiple historical sleep sessions; and generating a set of suggested goals using the subjective feedback.In some cases, the step of evaluating the goal further comprises the step of using the sensor data. In some cases, the step of evaluating the goal using the sensor data comprises the step of estimating a distance between the individual and the user using the sensor data. In some cases, the step of receiving the goal information comprises the step of receiving a target achievement date associated with the goal, and the step of receiving the target achievement date comprises automatically determining the target achievement date using i) said first sleep performance data, ii) said second sleep performance data, iii) said historical sleep performance data, or iv) any combination of i) to iii). In some cases, the goal information includes a goal related to a start time of the individual's future sleep session and a start time of the user's future sleep session. In some cases, the goal information includes a goal related to a distance between the individual and the user during a future sleep session. In some cases, the goal information includes a goal related to future use of a respiratory therapy device. In some cases, after one goal is completed, further selection of a goal is proposed. Further selection of a goal may be based on one or more of an achieved goal, time spent to achieve the achieved goal, sleep performance data and / or subjective data of the user, sleep performance data and / or subjective data of the individual, or any combination thereof.

[0009] In some cases, the method further includes the steps of identifying coaching suggestions for improving future sleep performance metrics, and providing coaching suggestions after a first sleep session. In some cases, the step of identifying coaching suggestions includes the steps of receiving subjective feedback related to a number of historical sleep sessions, and generating coaching suggestions using the subjective feedback. In some cases, the step of identifying coaching suggestions includes accessing historical sleep performance data related to historical sleep performance metrics, identifying one or more factors that influence the historical sleep performance metrics, determining, for each of the one or more factors, suggested actions that are estimated to improve future sleep performance metrics, and generating coaching suggestions using the suggested changes for each of the one or more factors.

[0010] In some cases, the method further includes a step of providing an incentive based on the first sleep performance data and the second sleep performance data. In some cases, the step of providing the incentive further includes a comparison between the one or more sleep performance metrics and the historical sleep performance metrics. In some cases, the step of generating the one or more sleep performance metrics includes a step of generating a coordinated sleep performance score, the step of generating the coordinated sleep performance score includes a step of generating a first sleep performance score using the first sleep performance data, a step of generating a second sleep performance score using the second sleep performance data, and a step of generating a coordinated sleep performance score using the first sleep performance score and the second sleep performance score, wherein the incentive is provided if the first sleep performance score exceeds a first threshold and the second sleep performance score exceeds a second threshold. In some cases, the step of providing the incentive includes a step of providing a first individual incentive related to the individual and a step of providing a second individual incentive related to the user. In some cases, the method further includes a step of providing a first individual incentive related to the individual if the first sleep performance score exceeds the first threshold, and a step of providing a second individual incentive related to the user if the second sleep performance score exceeds the second threshold. In some cases, the method further includes a step of providing a first individual incentive related to the user if the first sleep performance score exceeds the first threshold, and a step of providing a second individual incentive related to the individual if the second sleep performance score exceeds the second threshold.

[0011] In some cases, the method further comprises transmitting summary information based on said first sleep performance data, wherein said summary information is usable, when received by a user device associated with the user, to generate an entry in a feed of historical summary information associated with the individual. In some cases, the method further comprises receiving responsive feedback in response to generating the entry. In some cases, said step of generating the one or more sleep performance metrics comprises generating a first sleep performance score using said first sleep performance data, and said summary information comprises said first sleep performance score. In some cases, the method further comprises: receiving summary information at a user device associated with the individual, wherein said summary information is based on second sleep performance data; and generating, using the received summary information, an entry in a feed of historical summary information associated with the user. In some cases, said step of generating the one or more sleep performance metrics comprises generating a second sleep performance score using said second sleep performance data, and said summary information comprises said second sleep performance score. In some cases, said second sleep performance data is determined using sensor data, and said sensor data is further associated with a user's sleep session within an environment. In some cases, said second sleep performance data is determined using second sensor data from a second set of one or more sensors, and said second sensor data is associated with a user's sleep session within an environment.

[0012] Specific aspects and features of the present disclosure relate to a method comprising: using the respiratory therapy device to supply air to a user interface worn by a user in an environment participating in a sleep session; receiving sleep session data relating to an individual's sleep session in an environment different from the user's; and adjusting the parameters of the respiratory therapy device in response to the received sleep session data. In some cases, the parameters are dynamically adjusted between the user's sleep session and the individual's sleep session. In some cases, the sleep session data includes individual sleep stage data, and the parameters of the respiratory therapy device are adjusted based on the sleep stage data. In some cases, the step of adjusting the parameters of the respiratory therapy device includes adjusting the parameters to a first setting if the sleep session data indicates that the individual is awake, and adjusting the parameters to a second setting if the sleep session data indicates that the individual is asleep, wherein the respiratory therapy device is quieter when the parameters are adjusted to the first setting than when the parameters are adjusted to the second setting.

[0013] In some cases, the method further includes receiving first sensor data relating to the user's sleep session; receiving second sensor data relating to the individual's sleep session, wherein sleep session data relating to the second sleep session is determined using the second sensor data; and synchronizing the first sensor data and the second sensor data. In some cases, the method further includes improving the signal-to-noise ratio of the signal of the first sensor data using the synchronized second sensor data. In some cases, the method further includes detecting possible events using the first sensor data; and confirming the events using the synchronized second sensor data. In some cases, the method further includes estimating the user's posture using the synchronized first sensor data and the synchronized second sensor data. In some cases, the method further includes establishing a wireless connection with a user device relating to the individual, wherein the sleep session data is received using the wireless connection; measuring the characteristics of the wireless connection; and determining the individual's location information based on the measured characteristics of the wireless connection. In some cases, the method further includes adjusting the parameters of a respiratory therapy device based on the location information. In some cases, the wireless connection is a Bluetooth® connection.

[0014] In some cases, the environment is a building. In some cases, the environment is a pair of adjacent rooms. In some cases, the environment is a room. In some cases, the environment is a sleeping surface.

[0015] Specific aspects and features of the present disclosure include the steps of generating a simulated respiratory therapy device sound, outputting the simulated respiratory therapy device sound, monitoring the outputted simulated respiratory therapy device sound using a microphone, and adjusting the output of the simulated respiratory therapy device sound based on the monitored outputted simulated respiratory therapy device sound.

[0016] In some cases, the method further includes a step of accessing a set of predetermined respiratory therapy settings, the step of generating the simulated respiratory therapy device sound being based on the set of predetermined respiratory therapy settings. In some cases, the method further includes a step of accessing a set of therapy settings for the respiratory therapy device, the step of generating the simulated respiratory therapy device sound being based on the set of therapy settings for the respiratory therapy device. In some cases, the method further includes a step of receiving an adjustment command, a step of adjusting the volume of the simulated respiratory therapy device in response to the receipt of the adjustment command, and a step of providing a respiratory therapy recommendation based on the adjusted volume of the simulated respiratory therapy device. In some cases, the respiratory therapy recommendation includes i) a respiratory therapy device model, ii) a user interface type, iii) a user interface model, iv) a conduit type, v) a conduit model, or vi) any combination of i-v. In some cases, the method further includes the steps of: receiving sensor data from one or more sensors relating to a user participating in a sleep session, wherein the simulated respiratory therapy device sound is output during the sleep session; determining the sleep performance information using the sensor data; and outputting the sleep performance information. In some cases, the method further includes the step of modifying the output of the simulated respiratory therapy device sound during the sleep session. In some cases, the output of the simulated respiratory therapy device sound is modified based on the determined sleep performance information. In some cases, the generation of the simulated respiratory therapy device sound is related to the first respiratory therapy device model, and the method further includes the steps of: obtaining historical sleep performance information relating to the historical sleep session, wherein the historical sleep session occurs during the output of an additional simulated respiratory therapy device sound relating to a second respiratory therapy device model; and generating a comparison between the sleep performance information and the historical sleep performance information. In some cases, the method further includes generating recommendations for the first respiratory therapy device model or the second respiratory therapy device model based on the generated comparison.In some cases, the method further includes the steps of receiving medical information relating to an individual and modifying the output of the simulated respiratory therapy device sound based on the received medical information.

[0017] Certain aspects and features of the present disclosure relate to a system comprising a control system including one or more processors and a memory for storing machine-readable instructions, wherein the control system is coupled to the memory, and any of the above methods are performed when a machine-executable instruction in the memory is executed by at least one of the one or more processors of the control system.

[0018] Certain aspects and features of this disclosure relate to a system for shared sleep scoring, which includes a control system configured to carry out any of the methods described above.

[0019] Certain aspects and features of this disclosure relate to a system for controlling respiratory therapy, which includes a control system configured to perform any of the methods described above.

[0020] Certain aspects and features of this disclosure relate to a system for simulating respiratory therapy, which includes a control system configured to perform any of the methods described above.

[0021] Certain aspects and features of this disclosure relate to computer program products that, when executed by a computer, include instructions causing the computer to perform any of the methods described above. In some cases, the computer program product is a non-temporary computer-readable medium. This application refers to the following drawings, and the use of the same reference numerals in different drawings is intended to indicate the same or similar components. [Brief explanation of the drawing]

[0022] [Figure 1]Figure 1 is a functional block diagram of a system suitable for scoring sleep performance according to a particular aspect of this disclosure. [Figure 2] Figure 2 is a perspective view of the system, user, and bed partner of Figure 1, relating to a specific aspect of this disclosure. [Figure 3] Figure 3 shows an exemplary timeline of a sleep session relating to several implementations of the present disclosure. [Figure 4] Figure 4 shows an exemplary sleep progression diagram relating to the sleep session in Figure 3, in a particular aspect of this disclosure. [Figure 5] Figure 5 is a perspective view of a pair of cohort members, including the first and second cohort members, relating to a particular aspect of this disclosure. [Figure 6] Figure 6 is a flowchart illustrating a process for generating and presenting sleep performance metrics for a sleep cohort, according to a particular aspect of this disclosure. [Figure 7] Figure 7 is a flowchart showing a process for tracking sleep cohort goals according to a particular aspect of this disclosure. [Figure 8] Figure 8 is a flowchart illustrating the process for generating coaching suggestions for a sleep cohort according to a particular aspect of this disclosure. [Figure 9] Figure 9 is a flowchart showing a process for generating simulated respiratory therapy device sounds according to a particular aspect of this disclosure. [Modes for carrying out the invention]

[0023] While this disclosure is susceptible to various modifications and alternative forms, specific implementations and embodiments are shown as examples in the accompanying drawings and described in detail herein. However, it should be understood that this disclosure is not intended to limit itself to any particular form disclosed, but rather encompasses all modifications, equivalents, and alternatives that fall within the spirit and scope of this disclosure as defined by the accompanying claims.

[0024] Certain aspects and features of this disclosure relate to evaluating the sleep performance of a cohort of multiple individuals sleeping in a shared environment (e.g., a bed, a room, a series of adjacent rooms, or a household). Individual or coordinated sleep performance scores and other sleep performance metrics can be determined. If at least one individual is receiving treatment for sleep-related and / or respiratory disorders (e.g., a respiratory treatment system), evaluating the sleep performance of the entire cohort may be useful. Cohort evaluation helps determine what actions can be taken to improve the sleep performance of all individuals in the cohort.

[0025] Certain aspects and features of this disclosure also relate to adjusting the parameters of a respiratory therapy device used by another individual (e.g., a therapy user) in the same environment using sleep data of one individual in the same environment. In some cases, these automated or manually performable adjustments can improve the sleep quality of one or all individuals and improve the compliance and engagement of the user being treated by the system with the respiratory therapy system.

[0026] Specific aspects and features of this disclosure also relate to generating simulated respiratory therapy device sounds to help individuals adapt to sleep in an environment where a respiratory therapy device is being used. This simulation can help adapt a first individual to the sounds of a respiratory therapy system when a second individual sleeping in an environment shared with the first individual is using or about to begin respiratory therapy. Similarly, the simulation can help adapt a second individual, who is the intended user of a respiratory therapy system, before initiating respiratory therapy using the respiratory therapy system. In some cases, such simulations can be tailored to the settings in which the respiratory therapy system is being used or is planned to be used. In some cases, the simulation can be tailored to the sleep performance of an individual in the environment. By tracking the sleep performance of an individual in the environment, the impact of the simulated respiratory therapy system sounds on the individual's sleep performance can be determined.

[0027] Certain aspects of this disclosure may be used to determine sleep performance metrics (e.g., coordinated sleep performance score and / or individual sleep performance score) related to the sleep sessions of a user receiving respiratory therapy (e.g., therapy user) and the sleep sessions of different individuals sleeping in the same environment. Other individuals sleeping in an environment shared with a therapy user may be referred to as therapy adjacent individuals, bed partners (e.g., if an individual shares the same bed as a therapy user), or simply individuals. Therapy adjacent individuals may, but not necessarily, be receiving sleep-related treatment themselves, such as therapy with a respiratory therapy system. Therapy adjacent individuals may, but not necessarily be, be receiving sleep-related treatment themselves, such as therapy with a respiratory therapy system. Therapy adjacent individuals may be separated from a therapy user or may be in a different room from a therapy user. When used in relation to therapy adjacent individuals, the term “therapy adjacent” includes individuals sleeping in the same environment as a therapy user whose sleep performance affects or may be affected by a therapy user.

[0028] Therapy users and therapy-adjacent individuals may sleep in a shared environment. This shared environment may be any environment where the actions of the therapy user or therapy-adjacent individual affect the sleep performance of others. For example, the environment could be a bed, such as when the therapy user and therapy-adjacent individual are bed partners. In another example, the environment could be a room, such as when the therapy user and therapy-adjacent individual sleep in the same room but in different beds. In yet another example, the environment could be adjacent rooms, such as rooms sharing a common wall. In yet another example, the environment could be within a household, such as when the therapy user and therapy-adjacent individual sleep in spaced-out rooms within a single structure. Other environments may also be used.

[0029] In one example, the treatment user and the treatment adjacent individual may be spouses, in which case one spouse's sleep-related actions may affect the other spouse's sleep performance (for example, one spouse going to bed later than the other spouse may wake the other spouse from sleep). In another example, the treatment user may be a parent, and the treatment adjacent individual may be a child sleeping in an adjacent room, in which case the parent's or child's sleep-related actions may affect the other's sleep performance (for example, a parent's loud snoring may affect the child's ability to sleep). In some cases, a coordinated sleep performance score can be determined for the treatment user and multiple individuals adjacent to the treatment (e.g., the individual, the individual's spouse, and one or more of the individual's children). As used herein, a reference to a single treatment adjacent individual may appropriately include references to multiple treatment adjacent individuals. For example, if certain aspects and features of this disclosure describe the use of treatment adjacent individual sleep performance data to adjust the parameters of the treatment user's treatment device, then sleep performance data of multiple treatment adjacent individuals may also be used to adjust the parameters of the treatment user's treatment device.

[0030] As used herein, a cohort consisting of a treatment user and treatment-adjacent individuals may be referred to as a sleep cohort. A sleep cohort may include any number of members, including a treatment user and any number of treatment-adjacent individuals. Accordingly, certain aspects and features of this disclosure relate to the calculation of sleep performance metrics (e.g., coordinated sleep performance scores) for sleep cohorts including a treatment user and one or more treatment-adjacent individuals.

[0031] Various sleep performance metrics may include, for example, i) coordinated sleep performance score, ii) individual sleep performance score related to the adjacent treatment individual, iii) individual sleep performance score related to the treatment user, iv) sleep progression chart related to the adjacent treatment individual, v) sleep progression chart related to the user's treatment, vi) user treatment score, vii) resonance score (e.g., a score indicating consistency between the sleep sessions of the treatment user and the adjacent treatment individual), or viii) any combination of i-vii. Other sleep performance metrics may also be used. Sleep performance metrics may be based on one or more aspects of an individual's sleep, such as total sleep time, sleep efficiency, number of wakes and / or awakenings, total REM sleep time, total deep sleep time, sleep latency, REM latency, and post-sleep wakefulness (WASO).

[0032] The resonance score may indicate the consistency between sleep sessions of two or more members of a sleep cohort, as measured by any appropriate metric or combination of metrics. In some cases, consistency may be based on the proximity of the members' sleep onset times. In other cases, consistency may be based on the similarity between sleep performance metrics, such as sleep performance scores and / or sleep quality scores. Other metrics may also be used to establish the resonance score.

[0033] The coordinated sleep performance score (CPR) represents the overall sleep performance of a sleep cohort. Generally, the CPR increases when all members of the sleep cohort achieve improved sleep. In some cases, though not always, the CPR can be calculated based on the sleep performance scores of the treatment user and the treatment adjacency. In some cases, the CPR may be the average or sum of the sleep performance scores of the treatment user and the treatment adjacency, although other formulas and measurements may be used. Individual sleep performance scores can be determined based on sensor data specific to the treatment user or specific to the treatment adjacency. However, in some cases, the CPR may be directly based on sensor data without involving the determination of individual sleep performance scores.

[0034] Sleep performance scores, including individual sleep performance scores and coordinated sleep performance scores, can be based on the sleep sessions of sleep cohort members. Each member of the sleep cohort may undergo a separate sleep session, as described further herein with reference to Figure 3, and this separate sleep session may or may not overlap with another member's separate sleep session. In some cases, a cohort sleep session may be defined as the sleep session from the earliest initial start time of all cohort members' sleep sessions to the latest end time of all cohort members' sleep sessions. Thus, in an overall cohort sleep session, a treatment user may undergo a treatment user's sleep session, and a treatment adjacency may undergo a treatment adjacency's sleep session. The treatment user's sleep session and the treatment adjacency's sleep session can be completely separate, overlapping, or identical. Therefore, the coordinated sleep performance score of a sleep cohort may be a score related to the cohort sleep session.

[0035] Sensor data may include data from one or more sensors, such as sensors within or around a cohort member, within or around the entire cohort of sleepers, or within or around the environment. Once processed, various sleep performance data can be extracted from the sensor data, which can then be used to determine various sleep performance metrics. For the purpose of determining sleep performance metrics, sensor data can be processed to obtain sleep performance data such as treatment information and sleep quality information. Treatment information may include information related to the use of a treatment user to a treatment system, such as a respiratory treatment system. For example, treatment information may include usage information (e.g., data indicating the treatment user's use of the treatment device) and event information (e.g., data indicating sleep apnea syndrome or other events such as on / off and leak events of the user interface). Sleep quality information may include objective and / or subjective data regarding the sleep quality of a cohort member. Objective data may include data such as sleep state information (e.g., data indicating whether the user is asleep) and / or sleep stage information (e.g., data indicating the user's sleep stage). Subjective data may include subjective feedback provided by a given cohort member or other members of the cohort. For example, data provided by the treatment adjacent individual might be in response to the question, "On a scale of 1 to 5, how do you feel about your rest?" Upon waking, data provided by the treatment user might be in response to the question, "On a scale of 1 to 5, how well do you think your bed partner slept last night?" Therefore, sensor data may include objective metrics obtained from sensors such as radio frequency sensors, microphones, and pressure monitors, as well as data obtained via sensors such as touchscreens and interactive buttons (e.g., feedback responses).

[0036] Treatment information may be data related to the use of sleep-related therapeutic devices by the therapeutic user. Any suitable sleep-related therapeutic device may be used. In some cases, sleep-related therapeutic devices may include adjustable parameters such as dynamically adjustable parameters and / or manually adjustable parameters. In some cases, sleep-related therapeutic devices may include respiratory therapeutic devices, mandibular repositioning devices, sleep therapeutic implants (e.g., implantable stimulators for stimulating the hypoglossal nerve in the neck), or other such devices. Certain aspects and features of this disclosure may be particularly useful when the therapeutic device used by the therapeutic user is a respiratory therapeutic device.

[0037] A respiratory therapy system may include a respiratory therapy device that supplies pressurized air to a therapy user via a conduit and a user interface. Different models and types of conduits, as well as different models and types of user interfaces, can be used to fluidly couple the therapy user to the respiratory therapy device. While receiving respiratory therapy, the therapy user can participate in a sleep session, during which first sensor data can be collected from one or more sensors in a first set, such as sensors in the respiratory therapy device, sensors in a user device (e.g., a smartphone), sensors in an activity tracker (e.g., a wearable activity tracker), or other sensors placed in, on, or around the therapy user (e.g., implantable devices, clothing-integrated sensors, mattress-integrated sensors, wall-mounted sensors, or ceiling sensors). The first sensor data collected from one or more sensors in a first set may be used to determine therapy information relevant to the therapy user (e.g., one or more use variables related to the use of the respiratory therapy system). The first sensor data may also be used to determine sleep quality information relevant to the therapy user (e.g., sleep state information, sleep stage information, and / or subjective feedback). The first sensor data may be used to determine other variables and / or information.

[0038] For patients adjacent to a treatment, second sensor data can be collected from one or more sensors in a second set. The one or more sensors in the second set may be the same as one or more sensors in the first set, a subset of one or more sensors in the first set, a superset of one or more sensors in the first set, overlap with one or more sensors in the first set (e.g., some sensors are shared with one or more sensors in the first set, but not all), or be an exclusive set of one or more sensors (e.g., none of the sensors are shared with one or more sensors in the first set). Similarly, second sensor data may be the same as the first sensor data, a subset of the first sensor data, a superset of the first sensor data, share some sensor data with the first sensor data, or share nothing with the first sensor data. Second sensor data may be collected simultaneously with the first sensor data or in close temporal proximity to the first sensor data. In an example using an exclusive set of one or more sensors, the sleep performance of a treatment user can be evaluated using sensor data from the treatment user's respiratory treatment device and optionally from the treatment user's smartphone, while the sleep performance of a treatment-adjacent individual can be evaluated using sensor data from the treatment-adjacent individual's smartphone. Other devices and sensors can also be used.

[0039] Treatment information may include use variables related to the use of the respiratory therapy system. Use variables related to the use of the respiratory therapy system may include any appropriate variables related to how the therapy user uses the respiratory therapy system. Examples of appropriate use variables include usage time (e.g., duration of use by the therapy user), seal quality variables (e.g., indications of seal quality between the therapy user and the user interface), leak flow rate variables (e.g., indications of unintended leak flow rate, such as leaks due to poor quality seals or leaks due to mouth breathing when wearing a nasal pillow type user interface), event information (e.g., indications of detected events that occurred during a sleep session, such as the apnea-hypopnea index (AHI)), user interface compliance information (e.g., indications of detected user interface transformation events, such as putting on or taking off the user interface), multiple therapy subsessions during a sleep session (e.g., multiple individual blocks in which the respiratory therapy system is used sequentially), and user interface pressure. Other use variables may also be used. Statistical summaries of one or more use variables (e.g., mean, maximum, minimum, count) may be used as one or more additional use variables. One or more use variables may include any appropriate combination of use variables.

[0040] The step of determining the variables to be used may include processing sensor data to identify one or more values ​​associated with the variables to be used. The one or more values ​​may be measured or calculated scores associated with the use of the variables. For example, the seal quality variable may be a measured value of leak flow rate (e.g., L / min) or a seal quality score (e.g., 18 out of 20). The step of determining the variables to be used may include determining a single value or multiple values ​​(e.g., timestamp values). For example, in some cases, the step of determining the seal quality variable may include determining a single value (e.g., 18 out of 20) that represents the overall (e.g., average) seal quality for the entire sleep session. However, in some cases, the step of determining the seal quality variable may include determining a set of timestamp values ​​that represent the change in seal quality over time (e.g., in the range of 0 to 20, such as 18 at 10:00:00 pm, 18.1 at 10:00:05 pm, 18.2 at 10:00:10 pm, etc.), so that, for example, data on seal quality over the entire duration can be charted.

[0041] Sleep quality information may include objective information such as sleep state information, sleep stage information, and / or other such information obtained from sensor data. It may also include subjective information such as subjective feedback received in response to the presentation of feedback questions to the user. Sleep state information is information that indicates the sleep state of a cohort member. Sleep state indicates whether the cohort member is asleep or not. Sleep stage information may include information that indicates the sleep stages experienced by the cohort member during a sleep session. Examples of sleep stages include wakefulness, rapid eye movement (REM) stage, light sleep stage, and deep sleep stage. Sensor data can be processed to determine when a cohort member enters and exits various sleep stages. In some cases, determining sleep stage information may include determining the total duration spent by the cohort member in each sleep stage. In an exemplary 8-hour sleep session, sleep stage information may show a total of 21 minutes of wakefulness, 101 minutes of REM sleep, 267 minutes of light sleep, and 91 minutes of deep sleep. However, in some cases, determining sleep state information and / or sleep stage information may involve generating timestamped data showing the sleep state and / or sleep stage of cohort members at various points in time throughout a sleep session. Timestamped sleep stage information can be graphed to generate a sleep progression chart of the cohort members' sleep session.

[0042] In some cases, objective data may include various parameters extracted from sensor data, such as total bedtime, total sleep duration, sleep latency, post-sleep wakefulness parameters, sleep efficiency, number of awakenings and / or wakefulnesses, fragmentation index, total REM sleep duration, REM latency, total deep sleep duration, or any combination thereof.

[0043] For treatment-adjacent individuals, sleep performance metrics may be based solely on sensor data, which is sleep quality data (e.g., data related to the treatment-adjacent individual's sleep session but not specifically related to the use of a treatment device), which may include subjective data in the form of subjective feedback. In this case, sleep performance metrics may include, or be based on, various factors related to the treatment-adjacent individual's sleep, such as sleep stage information including the time spent in different sleep stages, and subjective feedback such as instructions on how the user felt rested at or after the end of a sleep session.

[0044] When using subjective feedback, the subjective feedback of a particular cohort member (e.g., a treatment user) may include subjective feedback obtained from that particular cohort member (e.g., feedback provided by the treatment user) and / or subjective feedback obtained from other members of the cohort (e.g., feedback provided by treatment-adjacent individuals).

[0045] Sleep quality data can be used to determine a sleep quality score or other sleep quality metrics. A sleep quality score may be an indicator of the quality of sleep experienced by cohort members during a sleep session. For example, a sleep session with many awakenings or interruptions may have a low sleep quality score, while a sleep session with many awakenings or interruptions may have a high sleep quality score. In some cases, the sleep quality score may be based on subjective feedback (e.g., feedback from cohort members indicating subjective calmness after a sleep session), on objective data, or a combination of both. As used herein, subjective feedback may also include diurnal information such as subjective energy levels, fatigue levels, and mood (e.g., satisfied, irritable, etc.). Some of this information may also be objectively collected by sensors such as wearable sensors that detect cardiac, respiratory, and / or motor parameters, which can estimate energy levels, fatigue levels, mood, etc.

[0046] For example, a sleep quality score or its components can be objectively determined based on sleep stage information, for instance. For example, the time spent in different sleep stages can be used to determine the sleep quality score. Additionally or alternatively, sleep stage patterns (e.g., sleep structure) can be used to determine the sleep quality score. Sleep stage information can be divided into sleep stage segments, each indicating the time spent in a particular sleep stage (e.g., the total time spent in each sleep stage during a sleep session, or the duration of each consecutive sleep stage that occurred during a sleep session).

[0047] In some cases, the sleep quality score may be assessed at least partially based on i) respiratory rate, ii) heart rate, iii) heart rate variability, iv) exercise data, v) electroencephalogram data, vi) oxygen saturation data, vii) respiratory rate variability, viiii) respiratory depth, ix) tidal volume data, x) inspiratory amplitude data, xi) expiratory amplitude data, xii) inspiratory volume data, xiii) expiratory volume data, xiv) inspiratory-to-expiratory ratio data, xv) sweating data, xvi) temperature data, xvii) pulse duration data, xviii) blood pressure data, xix) posture data, xx) pressure data, xxi) blood glucose data, or xxii) any combination of i-xxi.

[0048] For therapeutic users or individuals adjacent to therapeutic use of sleep-related therapeutic devices, sleep performance metrics can be based on sensor data including therapeutic information and / or sleep quality information. In some cases, therapeutic information and sleep quality information can be combined to determine sleep performance metrics. For example, tracking the total time a therapeutic user uses a respiratory therapeutic device during a sleep session (generally, longer usage is better) and sleep stage information can be informative and useful. In another example, since apnea and hypopnea events are more common during REM sleep (e.g., due to decreased tension in the genioglossus muscle of the tongue) and more harmful during REM and deep sleep (e.g., due to opportunities to interrupt REM sleep, negatively affect spatial memory, and / or reduce the amount of deep sleep), tracking the amount of time a respiratory therapeutic device is used during REM sleep and / or deep sleep may be more useful. Therefore, in addition to tracking total usage time, the usage time of a respiratory therapeutic device during a particular sleep stage (e.g., REM sleep or deep sleep) can be emphasized (e.g., given more weight) than the usage time of a respiratory therapeutic device during other sleep stages (e.g., wakefulness or light sleep). Therefore, longer use of the respiratory therapy device before bedtime may not significantly increase sleep performance scores for treatment users, or may not increase them at all. However, longer periods of REM sleep using the respiratory therapy device may significantly increase sleep performance scores for the same treatment users.

[0049] Treatment information may be used to determine a treatment score. The treatment score can indicate the treatment user's use of the treatment, including the effectiveness of the treatment and / or the treatment user's compliance with the treatment.

[0050] Sleep quality scores and treatment scores can be components of a sleep performance score. In some cases, sleep quality scores and treatment scores can be further broken down into subcomponents such as scores for REM sleep duration, sleep session length, treatment device usage time, and unintentional leakage. Thus, sleep quality scores and / or treatment scores, and optionally one or more subcomponent scores, can be used as sleep performance metrics or used to generate sleep performance metrics. In some cases, various component scores and optional subcomponent scores of cohort members can be used to generate an individual sleep performance score, which can then be presented as an individual sleep performance score, and / or optionally, a coordinated sleep performance score. In some cases, various component scores and optional subcomponent scores of cohort members can be used to directly generate a coordinated sleep performance score, rather than first generating an individual sleep performance score.

[0051] For example, sleep performance scores can be presented using numerical scores, but this is not always necessary. In some cases, coordinated sleep performance scores can be presented using a graphic device that shows the balance between two or more components or subcomponents. For example, a coordinated sleep performance score can be represented as the balance between the individual sleep performance scores of two members in a sleep cohort. In one example, the graphic device is a bubble level or similar device that allows individuals to quickly and easily see which components / subcomponents / members are relatively better than others, and optionally by how much. If one individual achieves a much higher sleep performance score than another, the graphic device may show a strong imbalance, while a slightly higher score may show only a slight or no imbalance. In some cases, multiple presentation methods can be combined (e.g., presenting sleep performance scores as digital scores along with a graphic device showing the balance).

[0052] The calculation of a coordinated sleep performance score (e.g., a combined sleep performance score) may include the calculation of scores for the treatment user and / or adjacent treatment individuals. Alternatively, it may include calculating a single coordinated sleep performance score using various sensor data and / or subjective feedback received from the treatment user and / or adjacent treatment individuals. Sleep performance scores and other sleep performance metrics may be provided, for example, as a range of numbers (e.g., a number in the range of 0 to 100), as chart data (e.g., a sleep progression chart of sleep stage data), as graphically represented information (e.g., a green checkmark indicating that no events were detected), or in any other appropriate way.

[0053] In one example, the Coordinated Sleep Performance Score is presented as a numerical value within the range of 0 to 100, where higher values ​​may represent higher quality sleep within the sleep cohort (e.g., higher quality sleep overall for the treatment user and any number of individuals adjacent to the treatment). In some cases, the Coordinated Sleep Performance Score may be the average of the individual sleep performance scores. In one example, a treatment user may have a sleep performance score of 70, and a treatment-adjacent individual may have a sleep performance score of 80; in this case, the Coordinated Sleep Performance Score may be 75. In the same example, if the treatment user subsequently achieves a sleep performance score of 78, and the treatment-adjacent individual achieves a subsequent sleep performance score of 76, the Coordinated Sleep Performance Score may be 76. This can result in a decrease in the sleep performance scores of cohort members and an increase in the Coordinated Sleep Performance Score. In some cases, a coordinated version of other sleep performance metrics can be calculated in a similar manner to how the Coordinated Sleep Performance Score is calculated from individual sleep performance scores. As described herein, the Coordinated Sleep Performance Score may also be presented in other external or additional ways, such as a graphic device showing the balance between the treatment user's sleep performance score and the treatment-adjacent individual's sleep performance score.

[0054] Sleep performance metrics can be presented to cohort members in any appropriate manner, for example, via a display device on a respiratory therapy device, a display device on a user device (e.g., a smartphone), or by other means. Presenting any sleep performance metrics may include presenting the sleep performance metrics and base component and / or minor component scores. For example, presenting sleep performance metrics may include presenting i) the coordinated sleep performance score, ii) the individual sleep performance score of a given cohort member, iii) the individual sleep performance scores of other members in the cohort, iv) one or more component and / or minor component scores that make up the sleep performance scores i-iii, or v) any combination of i-iv. Examples of component and / or minor component scores include scores for each of the variables used, sleep stage information, sleep state information, and / or sleep quality information. In some cases, presenting sleep performance metrics may include presenting a graphic representation of the scores of the components and / or minor components that make up the sleep performance metrics. For example, presenting the coordinated sleep performance score may include presenting a graphic representation of the individual sleep performance scores that make up the coordinated sleep performance score.

[0055] In some cases, presenting sleep performance metrics may include presenting the contribution of specific components or subcomponents to the overall sleep performance metrics. In some cases, components or subcomponents, such as use variables, may be decomposed (e.g., binned) and / or rearranged by sleep stage information. For example, a set of four subcomponent scores (e.g., binned), including scores for use time during wakefulness, use time during REM sleep, use time during light sleep, and use time during deep sleep, may be presented as a use time variable. It should be understood that each subcomponent score may be a score calculated by applying weighted values ​​to the use variable, as described herein with respect to the calculation of the overall sleep performance score.

[0056] Sleep performance metrics, such as sleep performance scores, can be used as objective metrics for the sleep sessions of cohort members. In some cases, for treatment users, sleep performance metrics may be limited, though not necessarily, to the portion of the treatment user's sleep session in which respiratory therapy is used. Sleep performance metrics can provide information to treatment users to help monitor, maintain, and / or encourage self-compliance (e.g., using respiratory therapy devices as desired or prescribed), and / or provide information to treatment adjacency individuals to help monitor, maintain, and / or encourage compliance of treatment users. In some cases, sleep performance metrics can provide information to healthcare providers, facilities, and / or healthcare-related companies (e.g., health insurance providers) about the compliance and effectiveness of treatment users using respiratory therapy devices during sleep, and / or the impact that treatment users or treatment adjacency individuals have on the sleep of others. In some cases, sleep performance metrics can be used to provide objective measures for research purposes and evaluation.

[0057] In some cases, goals can be set to improve overall sleep quality or to improve certain aspects related to sleep quality (e.g., to improve certain sleep performance metrics). In some cases, cohort members can set goals directly through a graphical user interface of an application running on a user device (e.g., a smartphone). In some cases, cohort members can choose from a list of suggested goals, e.g., a list of globally pre-configured goals (e.g., goals commonly selected by all users), a list of demographically specific pre-configured goals (e.g., goals commonly selected by users sharing a certain demographic with the cohort member), or a list of custom-generated goals (e.g., goals custom-generated for the cohort member). Custom-generated goals can be automatically generated based on sensor data (e.g., sensor data from historical sleep sessions) or input provided by the member. Once a list of suggested goals is provided, cohort members can select one or more goals to use.

[0058] In examples where custom-generated goals are automatically based on sensor data, historical sleep performance metrics (e.g., historical coordinated sleep performance score) can be analyzed to determine one or more factors that may influence a particular sleep performance metric. The analysis may include using historical sensor data to identify the factors discussed and then identifying suggested actions to take to improve future sleep performance metrics (e.g., future coordinated sleep performance score). Suggested actions may include performing a given action (e.g., brushing teeth before bed) or avoiding performing a given action (e.g., avoiding caffeine intake two hours before bed). Factors discussed may include factors that may influence a given sleep performance metric. Examples of factors include loud snoring, obstructive sleep apnea diagnosis, AHI, cohort members with different work shifts, cohort members including infants, poor sleep hygiene, cohort members' anxiety about their own sleep or the sleep of other cohort members, caffeine intake, alcohol intake, etc. In some cases, one or more factors may be determined from feedback provided in response to questionnaires, such as those asking about potential problems related to the cohort members who are treatment users. Illustrative problems include concerns that the bed partner may have to sleep in a different room, health concerns, concerns about whether the treatment is adequate, concerns that the treatment is not being adequately performed (e.g., if the user interface is removed during a sleep session), treatment device settings (e.g., pressure levels, noise, leaks), and concerns about having to contact the medical device vendor or manufacturer. In some cases, questionnaires may be based on clinically validated questionnaires (e.g., the Epworth Sleepiness Scale, Quality of Life Index, Binary Adjustment Scale, or Beck Depression Inventory).

[0059] The proposed actions may be related to these factors. For example, if sleep hygiene factors are insufficient, the proposed actions may be to stop using electronic screens at least 30 minutes before bedtime, to avoid eating at least 60 minutes before bedtime, and other such actions. Another example is a cohort member feeling anxious about the sleep of other cohort members; the proposed actions may be to engage in exercise to reduce anxiety, to discuss anxiety with other cohort members, etc. The list of proposed goals may also be goals related to taking the proposed actions. For example, if an analysis of historical coordinated sleep performance scores reveals that a treatment user tends to have a lower coordinated sleep performance score if they go to bed more than 30 minutes before the treatment adjoining individual goes to bed, the proposed action may be for the treatment adjoining individual to go to bed within 30 minutes after the treatment user goes to bed, and the goal may be for the treatment adjoining individual to go to bed within 30 minutes of the treatment user going to bed in at least 75% of sleep sessions over the next two weeks.

[0060] Another example where customized-generated goals are automatically generated based on sensor data is the analysis of historical treatment information to identify historical respiratory therapy device usage information. Further analysis of historical respiratory therapy device usage information can be performed to identify one or more targets. For example, if analysis of historical respiratory therapy device usage information reveals that using the respiratory therapy device for at least 5 hours during a sleep session improves the coordinated sleep performance score, the proposed action could be to use the respiratory therapy device for at least 5 hours during each sleep session, and the list of proposed goals could include a first goal of using the respiratory therapy device for at least 5 hours during each sleep session for the next 5 days, a second goal of using the respiratory therapy device for at least 8 hours during 3 sleep sessions in the following week, and a third goal of using the respiratory therapy device for at least 5 hours for 7 consecutive days over the next 3 months.

[0061] In some cases, a questionnaire containing one or more questions can be provided to cohort members. The cohort members' responses to these questions can be used to generate a list of proposed goals and / or identify one or more particularly relevant goals. In some cases, the list of proposed goals can be generated using responses from some or all members within the cohort.

[0062] In some cases, cohort members may provide input in the form of suggestive feedback related to one or more historical sleep sessions. Such subjective feedback may be used to determine one or more goals. For example, if feedback that a treatment adjacency feels they are not getting enough rest coincides with sleep sessions in which the treatment user sleeps in a different room from the treatment adjacency, a goal could be proposed in which the treatment user and the treatment adjacency sleep in the same room for at least a threshold number of nights per week. This exemplary goal can be evaluated by estimating the distance between the treatment user and the treatment adjacency based on sensor data (e.g., by analyzing patterns in audio data, analyzing the signal strength of wireless signals, etc.).

[0063] Goals can be set for individual cohort members or for the cohort as a whole. For example, an individual cohort member may have a personal goal of stopping viewing electronic screens by 9:30 p.m., in which case the cohort member's goal may be tracked individually, while the cohort as a whole may have a goal of achieving a coordinated sleep performance score of at least 90 out of 100.

[0064] Once goals are established, they can be monitored and evaluated after each sleep session and / or after each cohort sleep session for the cohort members for whom the goals have been set. Sensor data can be used for monitoring and evaluating goals. The progress of cohort members or the progress of the cohort toward any given goal can be presented to one or more members of the cohort, for example, by one or more display devices. This presentation of progress helps to incentivize cohort members to improve sleep hygiene and enhance overall sleep quality.

[0065] For example, if the goal of cohort members is for them to go to bed at essentially the same time, the start time of each cohort member's sleep session can be monitored to determine whether the cohort members start their sleep sessions within a threshold time that is sufficiently far apart from each other.

[0066] In some cases, receiving goal information may include receiving a target achievement date related to the goal. The target achievement date can be received as a specific date or as the number of days from the current date. The target achievement date can be manually specified, such as by user input, or it can be automatically determined. The automatically determined target achievement date can then be automatically set for a given goal or suggested to cohort members. The automatic determination of the target achievement date can be based on any appropriate data, such as i) first sleep performance data, ii) second sleep performance data, iii) historical sleep performance data, or iv) any combination of i-iii. The target achievement date can be determined as a target achievement date that is achievable for a given cohort member or cohort. The feasibility of the target achievement date may be based on historical sleep performance data, for example, by identifying previous instances in which the goal was achieved, identifying trends in sleep performance metrics, or through other analyses. For example, if the goal is to avoid caffeine for four consecutive hours after going to bed, an analysis of historical sleep performance data (including historical subjective feedback on caffeine use) can identify whether a cohort member was able to attempt the goal of avoiding caffeine for four consecutive hours after going to bed. This allows the target achievement date to be set proactively relative to the previous trial (e.g., within two weeks from the current date), to be set to the same time as the previous trial (e.g., within two weeks from the current date), or to be set reserved relative to the previous trial (e.g., within two weeks from the current date).

[0067] In some cases, the target achievement date can be updated based on the estimated duration to achieve the goal. Updating the target achievement date in this way can be done to avoid discouragement felt by cohort members if sufficient progress is not made as the target achievement date approaches. The estimated duration to achieve the goal can be determined based on sleep performance data from the treatment user's current sleep session (e.g., last night's sleep session), sleep performance data from the current sleep session of adjacent treatment individuals, and / or historical sleep performance data from one or all users. For example, if current and historical sleep performance data related to a treatment user indicates that the treatment user is gradually using the respiratory therapy device for more time each night, but less than the expected target achievement date for the set goal, it can be estimated when the treatment user is likely to achieve the goal. The target achievement date can then be updated using this estimate. The target achievement date may be updated proactively against the estimate, updated to the same extent as the estimate, or reserved for updating against the estimate. In some cases, estimates may be generated for other purposes, such as helping to incentivize cohort members to achieve the goal on time or early, or to assess whether cohort members are improving in their attempts to achieve the goal.

[0068] Possible goals include using the treatment device for a set amount of time each night, ensuring that the treatment user and treatment adjacency sleep in the same environment for at least a threshold number of nights each week, improving sleep performance metrics for the treatment user and treatment adjacency, improving coordinated sleep performance metrics, improving subjective feedback (e.g., improving responses to routine questions about how rested cohort members feel), stopping or minimizing snoring, weight loss, improving mood, reducing sleepiness during sleep, improving compliance with the use of the treatment device, and improving compliance with the use of systems to monitor sleep performance (e.g., activating a sleep monitoring application on a smartphone every night).

[0069] In some cases, an interactive feed may be provided to share sleep-related data (e.g., individual sleep performance scores) and / or goals among cohort members. The interactive feed can allow cohort members to comment on each other's entries, for example, through text-based comments, image-based comments, or reactions. Reactions can include any number of pre-set responses (e.g., "like," "thumbs up," various emoticons, etc.). In some cases, reactions can be recorded by counting the number of reactions presented on an entry in the feed. In some cases, the interactive feed can provide further motivation to achieve a given goal. In some cases, sleep-related data and / or goals of a cohort member or other members of the cohort can be shared with an external individual (e.g., an individual not in the sleep cohort, e.g., a different sleep cohort member). Such data sharing, in a similar interactive feed, can allow individuals to comment on each other's entries, providing further motivation to achieve a given objective and leading to better sleep hygiene and overall sleep quality.

[0070] Cohort members' user devices (e.g., smartphones) can send summary information based on sleep performance data when sharing data to an interactive feed. This summary information may include sleep-related data, comments provided by the member, etc. In some cases, the summary information can be sent directly to other cohort members' user devices. In other cases, the summary information may be sent to a network-accessible server (e.g., via a local area network, wide area network, cloud, or the internet), which may then be accessed by another cohort member's user device. When a server is used, the unique identifiers (UIDs) associated with each member of the cohort may be related to each other directly or via the cohort's UIDs.

[0071] In some cases, coaching suggestions can be identified and provided to improve the sleep performance metrics of cohort members or the coordinated sleep performance metrics of the cohort. Coaching suggestions may be recommendations to take or not take specific actions. In some cases, but not necessarily, these actions may be similar to goal-setting actions as described herein. Coaching suggestions can be generated automatically based on the analysis of historical sensor data and / or historical sleep performance metrics, or they can be generated manually, for example, in response to subjective feedback.

[0072] When automatically generated, coaching suggestions are obtained by analyzing sleep performance data and sleep performance metrics to identify factors that influence the history of a given sleep performance metric, and then identifying suggested actions related to those factors. Suggested actions may be selected as actions that are expected to improve the given sleep performance metric in the future (e.g., future improvement of sleep performance metrics). The coaching suggestions can then be generated as suggestions designed to get cohort members to perform the suggested actions. Suggested actions may be to perform the given action or to avoid performing the given action.

[0073] When generated manually, coaching suggestions may be based on specific subjective feedback from cohort members. For example, a cohort member might be able to say that they want to go to sleep before 10 p.m., or that they feel they haven't gotten enough rest when they go to sleep after 10 p.m. the previous night. In this case, coaching suggestions can be automatically generated to encourage the cohort member to go to sleep by 10 p.m., or to take other measures to encourage them to go to sleep by 10 p.m.

[0074] In some cases, manually generated coaching suggestions may be provided to individual cohort members. For example, coaching suggestions to remember how to use respiratory therapy devices may be provided only to therapy users.

[0075] Coaching suggestions can be direct or indirect. Direct coaching suggestions indicate a desired outcome. Indirect coaching suggestions do not necessarily indicate an expected outcome, but the achievement of the desired outcome is expected. Indirect suggestions can be implicit, implicit, or ambiguous. Implicit suggestions are designed to achieve a desired outcome without the individual realizing that the suggestion is intended to achieve that outcome. Implicit suggestions are designed to achieve a desired outcome by suggesting an action that may be related to the desired outcome. An individual may recognize that an implicit suggestion relates to an action related to the desired outcome, and that performing that action will improve the desired outcome, but an implicit suggestion does not directly indicate the desired outcome. In some cases, indirect suggestions can take the form of explanations or questions rather than explicitly indicating an action to be taken. For example, instead of explicitly suggesting that a therapy user use a respiratory therapy system in their next sleep session, an indirect coaching suggestion can be used as a reminder to check the suitability of the user interface.

[0076] In an example where the desired outcome is to use the respiratory therapy system on more days of the week, various coaching suggestions can be used. A direct coaching suggestion is to use the respiratory therapy system for sleep at least five times this week. Intent coaching suggestions can take the form of a series of prompts to the therapy user, such as providing a motivational prompt every morning after using the respiratory therapy system, which can subconsciously motivate the therapy user to use the respiratory therapy system more frequently that week. Implicit suggestions can be presented as a notification indicating how long the respiratory therapy system was used during a previous sleep session, suggesting that the therapy user try to reach or exceed the usage time of the previous sleep session. Thus, while implicit suggestions are intended to improve the length of usage time, they also have the effect of increasing the opportunity for the therapy user to use the respiratory therapy system not only during the next sleep session, but potentially during subsequent sleep sessions as well.

[0077] Coaching suggestions based on subjective feedback or data related to a specific cohort member may be ambiguous or explicit. In some cases, ambiguous suggestions may be a form of indirect suggestions. In some cases, explicit suggestions may be a form of direct suggestions. Explicit coaching suggestions are suggestions that indicate underlying subjective feedback or underlying data. For example, if subjective feedback indicates that a treatment adjacency wants the treatment user to use a respiratory therapy device, or that the treatment adjacency feels unrested several days after a sleep session in which the treatment user did not use a respiratory therapy device, an explicit coaching suggestion could encourage the treatment adjacency to use a respiratory therapy device to ensure the treatment adjacency gets quality sleep. This explicit coaching suggestion indicates an underlying sleep-related problem (e.g., treatment of the adjacency's sleep quality). Similarly, ambiguous coaching suggestions could be to remind the treatment user to properly adjust the user interface strap, or to remind the treatment user that they used a respiratory therapy device for one minute during their last sleep session, and that they hope to improve in the next sleep session. Such ambiguous coaching reminders, while not directly addressing underlying sleep-related concerns, may have the effect of improving them. Therefore, while a suggestion to adjust the user interface straps might fail to recognize that the patient-neighbored individual desires the patient to use a respiratory therapy device to achieve higher quality sleep, the suggestion has the effect of bringing the respiratory therapy device into the patient's mind, and especially if offered near the start of a sleep session, it can lead to the desired outcome of the patient using the respiratory therapy device.

[0078] In another example, a clear coaching suggestion could be a suggestion to a therapy-adjacent individual to encourage a therapy user to use the therapy device for a longer period in the next sleep session, given that they only used it for one hour in the previous sleep session. This clear coaching suggestion reveals data relevant to the therapy user, namely the length of time the therapy device was used during the previous sleep session. Similarly, an ambiguous coaching suggestion could be a suggestion to a therapy-adjacent individual to encourage a therapy user to use the therapy device for at least five hours during the next sleep session. Such an ambiguous coaching suggestion can provide a similar effect without revealing underlying data (e.g., therapist data).

[0079] Coaching proposals can be essentially personal or related to the entire cohort. For example, an individual coaching proposal might involve having individual cohort members take or not take a specific action, while a cohort coaching proposal might involve having all members of the cohort take or not take a specific action.

[0080] Coaching suggestions can be evaluated using subjective feedback or objective data. When using subjective feedback, cohort members can indicate that the suggestions they attempted were successful. For example, in response to prompts asking whether they tried the suggestion and whether they felt they got a break, they might answer, "Yes, I tried the suggestion," and "Yes, I felt I got a break." When objective data is used, one or more sleep performance metrics from previous sleep sessions can be analyzed and / or compared to historical sleep performance metrics to determine whether improvement occurred. The system can assume that the suggestion was made, or prompt members to indicate whether the suggestion was made. Thus, the effectiveness of the suggestion can be evaluated based on the detected changes in sleep performance metrics.

[0081] In some cases, incentive systems can be used to provide additional incentives to individual members of a cohort or to the cohort as a whole. Incentives may be based on achieving threshold sleep performance metrics (e.g., threshold sleep performance score), achieving goals or goal milestones (e.g., quantifiable progress toward a goal), and / or implementing coaching suggestions (e.g., confirming whether or not to take a specific action). In some cases, cohort incentives may be provided based on the overall performance of the cohort, for example, when threshold coordinated sleep performance metrics are reached or when cohort goals are reached.

[0082] In some cases, individual incentives can be provided to individual cohort members, such as when they reach a threshold individual sleep performance score or when they implement individual coaching suggestions. In some cases, a combination of individual and cohort incentives may be provided. For example, a treatment user may reach a threshold individual sleep performance score, but adjacent treatment individuals may not reach their respective threshold individual sleep performance scores, and the cohort as a whole may not reach a threshold coordinated sleep performance score. In this example, although not always the case, only the treatment user receives an incentive.

[0083] In some cases, cohort members may receive individual incentives based on the sleep sessions of other cohort members. For example, a treatment user may receive a specific incentive only if a treatment-adjacent individual achieves a sleep performance score above a threshold. In this case, cohort members may be encouraged to improve the sleep of other members within the cohort.

[0084] Incentives may be offered for individual sleep sessions or for longer durations (e.g., all sleep sessions in a week or all sleep sessions in a month). For longer durations, incentives may be based on achieving a desired outcome (e.g., achieving a threshold sleep performance score or a target) once during each sleep session or in at least a threshold number of sleep sessions.

[0085] Incentives may be provided by cohort members, other cohort members, or third parties. Any appropriate incentive can be used. For example, incentives may take the form of prize money, gift cards, gift items, or discount codes to retailers. In some cases, incentives may be related to sleep, or to goals or coaching suggestions.

[0086] In some cases, incentives can be dynamically adjusted based on historical data (e.g., historical sleep performance scores) and / or current data. For example, if a cohort member is offered an incentive worth $10, but analysis of historical data determines that the cohort member has not improved their sleep performance score or has deviated from the desired goal, the incentive can be automatically adjusted to increase (e.g., to $15) to provide a stronger incentive for improving sleep performance and overall sleep quality.

[0087] In some cases, sensor data from the adjacent patient can be used to control parameters related to the patient's treatment device (e.g., a respiratory treatment device). This control can be dynamic (e.g., automatically during the same sleep session), automatic (e.g., automatically between the current and subsequent sleep sessions), or manual (e.g., recommended parameter adjustments provided to the patient after the current sleep session). In one example of dynamic control, if sensor data indicates that the adjacent patient is in a particular sleep state or stage, the air pressure supplied by the respiratory treatment device may be automatically adjusted to increase (e.g., to a level that is more effective but may produce a louder sound). In one example of manual control, if sensor data indicates that the adjacent patient is experiencing micro-awakening whenever the seal quality of the patient's user interface falls below a threshold, the patient may be presented with a message to take action to improve the seal quality before the subsequent sleep session (e.g., by adjusting or replacing the conduit or user interface).

[0088] In one example, a therapy user can begin using a respiratory therapy device before the adjacent therapy individual begins a sleep session. If it is identified that the adjacent therapy individual is not yet attempting to fall asleep, the respiratory therapy device can act using its normal parameters. However, if sensor data indicates that the adjacent therapy individual is attempting to fall asleep, the respiratory therapy device can operate using adjusted parameters that allow it to operate more quietly (e.g., operating the flow generator motor at a slower speed). The respiratory therapy device can continue operating with the adjusted parameters until sensor data indicates that the adjacent therapy individual has reached a specific sleep state (e.g., sleep) or sleep stage (e.g., light sleep). In some cases, the respiratory therapy device can continue operating with the adjusted parameters for a preset duration while the adjacent therapy individual is still attempting to fall asleep, for example, if the adjacent therapy individual does not fall asleep, before reverting to the previous parameters or other parameters.

[0089] In some cases, sensor data associated with one cohort member may be collected from a different set of sensors on another cohort member. For example, sleep performance data for a treatment user may be obtained from one or more sensors embedded in the treatment user's respiratory treatment device and user device, while sleep performance data for a treatment adjacency may be obtained from one or more sensors embedded in the treatment user's user device. In this case, sensor data associated with the treatment user can be synchronized with sensor data associated with the treatment adjacency. Synchronizing sensor data may include synchronizing data according to timestamps, synchronizing data according to commonly detected events (e.g., adjusting data based on the detection of particularly loud snoring), or any combination thereof. The resulting synchronized sensor data can be used for any combination of i) to improve the signal-to-noise ratio of specific raw sensor data (e.g., by identifying and filtering noise, or by identifying and amplifying desired signals), ii) to perform further analysis and / or obtain sleep performance metrics, iii) to confirm events that may have been detected using the raw sensor data, or iv) any combination of i-iii. In some cases, further analysis may involve detecting the location of cohort members relative to various sensors used to collect raw sensor data (e.g., sensors in the treatment user's smartphone and sensors in the treatment adjacent individual's smartphone). Using such location detection, the location of the treatment user or treatment adjacent individual in the environment (e.g., position on the bed or posture in the room) can be identified. In some cases, parameters of the respiratory therapy device can be adjusted based on this location information.

[0090] Specific aspects and features of this disclosure relate to the generation of simulated respiratory therapy device sounds. Such simulated sounds can be simulated by any suitable device, such as a user device (e.g., a smartphone), which includes a speaker and a microphone. The speaker is used to output the simulated respiratory therapy device sound, while the microphone may be used to monitor the outputted sound and adjust it as necessary to ensure the accuracy of the simulation.

[0091] Simulated respiratory therapy device sounds can be generated on demand (e.g., programmatically via an electronic oscillator) or pre-recorded (e.g., a pre-recorded file containing electronically generated sounds, or a pre-recorded file containing recordings of a physical respiratory therapy device). In some cases, the outputted simulated respiratory therapy device sounds may be selected by the individual using the respiratory therapy device simulator (e.g., a user receiving respiratory therapy, an individual planning to start respiratory therapy in the future, an individual sleeping in the same environment as a user receiving respiratory therapy, or an individual sleeping in the same environment as an individual planning to start respiratory therapy in the future). The selection of a specific simulated respiratory therapy device sound may include selecting the model and / or type of respiratory therapy device, and optionally selecting one or more settings or parameters. For example, an individual sleeping in the same environment as a future treatment user may select the model and default settings that the future treatment user will use. Based on this selection, the simulator can adjust the generated sounds or select a specific pre-recorded file to accurately simulate the simulated respiratory therapy device sounds related to the individual's selection. In some cases, the simulated respiratory therapy device sounds may be adjusted based on the actual model and / or actual settings of the treatment user's respiratory therapy device.

[0092] In some cases, individuals can adjust the output of the simulated respiratory therapy device sound (e.g., adjust the volume of the simulated respiratory therapy device sound). Based on this adjustment (e.g., the adjusted volume), respiratory therapy recommendations can be provided. Respiratory therapy recommendations may include the respiratory therapy device model, user interface type and / or model, conduit type and / or model, respiratory therapy device settings, or any combination thereof. For example, an individual may set the maximum volume level they are willing to tolerate for comfortable sleep, and the simulator can provide respiratory therapy recommendations for a specific user interface that achieves the desired result.

[0093] In some cases, individuals can undergo sleep sessions while emitting simulated respiratory therapy device sounds. In this case, sensor data can be used to track each individual's sleep performance as they experience the simulated respiratory therapy device sounds. Such sensor data can be used to determine sleep performance metrics that can be used to identify the degree of an individual's tolerance to the simulated respiratory therapy device sounds. In some cases, the emitted simulated respiratory therapy device sounds can be automatically adjusted using sensor data, whether the individual is a future therapy user or a future therapy adjacent. For example, if an individual experiences a different sleep stage, the simulated respiratory therapy device sounds can be automatically adjusted based on the individual's current sleep stage by increasing or decreasing the volume or other characteristics of the sound (e.g., simulated unintended leakage sounds or other simulated sounds associated with the use of the respiratory therapy device). In some cases, the simulated respiratory therapy device sounds can be adjusted to become more invasive (e.g., at a higher volume or with other characteristics that may disrupt the individual's sleep) until the individual's sleep performance metrics fall below a threshold. Thus, one or more sleep performance metrics can be used to objectively assess an individual's tolerance to different volumes and types of simulated respiratory therapy device sounds.

[0094] In some cases, the simulated respiratory therapy device sound can be adjusted based on historical sleep performance information collected during the previous sleep session (e.g., historical sleep performance metrics and / or underlying sensor data). In this case, the simulated respiratory therapy device sound used in the current sleep session may differ from the simulated respiratory therapy device sound used in the previous sleep session. Next, the historical sleep performance information can be compared with the sleep performance information from the current sleep session to generate a comparison between the two different simulated respiratory therapy device sounds. This comparison can be presented to the individual. In some cases, recommendations may be generated based on a comparison between the sleep performance information from the previous sleep session and the current sleep session. Differences in the simulated respiratory therapy device sounds may be due to the use of different respiratory therapy device models, differences in volume, or differences in other characteristics. This allows the individual to select a more desirable configuration based on the sleep performance comparison. For example, a future therapy user may try the simulated respiratory therapy device sound for the first respiratory therapy device on the first night and the simulated respiratory therapy device sound for the second respiratory therapy device on the second night. If a future treatment user's sleep performance metrics improve on the second night, the future treatment user may choose to continue using the second respiratory therapy device instead of the first respiratory therapy device.

[0095] In some cases, the simulated respiratory therapy device sounds may be modified based on medical information relevant to the individual. Such medical information may include height, weight, sex, diagnosis, or other such information. For example, when the simulator is first started, the simulator may prompt the individual to answer certain questions (e.g., questions about obstructive sleep apnea, such as the STOP-BANG questionnaire), and the simulator may use these answers to modify (e.g., change and / or select) the simulated respiratory therapy device sounds. For example, if the individual answers the questions in a way that suggests a high probability of obstructive sleep apnea, the simulated respiratory therapy device sounds may be modified to include characteristics associated with the use of a respiratory therapy device by a person suffering from obstructive sleep apnea.

[0096] Certain aspects and features of this disclosure also relate to an interactive system for identifying problems associated with a sleep cohort in which members are treatment users. Once identified, the system can provide information or lead suggestions to mitigate the identified problems. For example, if a cohort member responds to a questionnaire in a way that indicates anxiety about the treatment and its impact on the cohort member's sleep quality, the system can provide information and / or coaching suggestions to mitigate the cohort member's anxiety. For example, the system can provide knowledge and tips, interactive content to address common questions about the treatment (e.g., noise from treatment devices), interactive topics to help cohort members exchange concerns with each other, and / or cross-orientation content related to questionnaire responses of other members of the cohort. Such information and / or coaching suggestions may be in text, audio, video, or any other format. In some cases, historical responses to questions can be used to generate new questions for future questionnaires. The questionnaires can be used as part of subjective feedback corresponding to sleep quality scores, or in other ways.

[0097] In some cases, the system may allow the user to respond in free text or speech (e.g., via a microphone) that can be interpreted by a natural language processor. This interpretation, along with sentiment analysis of the input as needed, can lead to the extraction of useful information that can be stored in a structured format. In some cases, the system may allow the user to respond with fixed choices (e.g., multiple choices, a Likert scale, or a graphic choice).

[0098] In some cases, the system can provide coaching suggestions related to the concerns of other members in the cohort.

[0099] These illustrative examples are presented to the reader to introduce the general subject matter discussed herein and are not intended to limit the scope of the concepts disclosed. The following sections illustrate various additional features and examples with reference to the drawings, the same numbers in the drawings represent the same elements, and the directional explanations are for illustrative purposes only, but like the illustrations, are not intended to limit the disclosure. Elements included in these drawings may not be scaled.

[0100] Referring to Figure 1, several implementations of the System 100 of the present disclosure are shown. The System 100 includes a control system 110, a memory device 114, an electronic interface 119, a respiratory therapy system 120, one or more sensors 130, one or more user devices 170, one or more light sources 180, and one or more activity trackers 190.

[0101] In some cases, a single system 100 can be used to monitor multiple members of a sleep cohort. In some such cases, the single system 100 may include multiple user devices 170 incorporating multiple instances of one or more sensors 130. In some cases, multiple iterations of system 100 can be used to monitor multiple members of a sleep cohort (e.g., a separate system 100 for each member of the cohort). Embodiments and features of system 100 can be used to monitor sleep and interact with any member of the cohort, e.g., a treatment user and a user adjacent to the treatment.

[0102] The control system 110 includes one or more processors 112 (hereinafter referred to as processor 112). The control system 110 is generally used to control (e.g., operate) various components of system 100 and / or to analyze data acquired and / or generated by the components of system 100. The processors 112 may be general-purpose or special-purpose processors or microprocessors. Although one processor 112 is shown in Figure 1, the control system 110 may include any appropriate number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that may reside in a single enclosure or be located apart from one another. The control system 110 (or any other control system) or a part of the control system 110, for example, processor 112 (or any other processor or a part of any other control system), may be used to perform one or more steps of the methods described herein and / or claimed. The control system 110 may be coupled to and / or located within, for example, the housing of the user device 170, part of the breathing system 120 (e.g., housing), and / or the housing of one or more sensors 130. The control system 110 can be centralized (within one such housing) or distributed (within two or more physically separate such housings). In such an implementation configuration involving two or more housings housing the control system 110, such housings may be located close to and / or far apart from each other.

[0103] The memory device 114 stores machine-readable instructions that can be executed by the processor 112 of the control system 110. The memory device 114 may be any suitable computer-readable storage device or medium, such as a random-access memory device or a serial-access memory device, a hard drive, a solid-state drive, or a flash memory device. Although one memory device 114 is shown in Figure 1, the system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 114 may be connected to and / or located inside the housing of the breathing device 122, the housing of the user device 170, the housing of one or more sensors 130, or any combination thereof. As in the control system 110, the memory device 114 may be centralized (within one storage device) or distributed (within two or more physically separate storage devices).

[0104] In some implementations, the memory device 114 (Figure 1) stores member profiles associated with cohort members. The member profile may include identifying information of the sleep cohort to which the member belongs. The member profile may include, for example, demographic information, biometric information, medical information, self-reported feedback, sleep parameters (e.g., sleep-related parameters recorded from one or more early sleep sessions), or any combination thereof. Demographic information may include, for example, information indicating the age, sex, race, geographical location, relationship status, family history of insomnia, employment status, education level, socioeconomic status, or any combination thereof. Medical information may include, for example, one or more medical conditions associated with the cohort member, medication use, or both. Medical information data may also include user-related fall risk assessments (e.g., fall risk scores using the Morse Fall Scale), multiple sleep latency tests (MSLT) results or scores, and / or Pittsburgh Sleep Quality Index (PSQI) scores or values. Self-reported feedback may include information indicating self-reported subjective sleep scores (e.g., poor, average, good), self-reported subjective stress levels, self-reported subjective fatigue levels, self-reported subjective health status, recent life events experienced by cohort members, or any combination thereof.

[0105] The electronic interface 119 is configured to receive data (e.g., physiological data and / or audio data) from one or more sensors 130, the data is stored in a memory device 114, and / or analyzed by a processor 112 of the control system 110. The electronic interface 119 can communicate with one or more sensors 130 using a wired or wireless connection (e.g., via a cellular network using an RF communication protocol, WiFi communication protocol, Bluetooth® communication protocol, etc.). The electronic interface 119 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 may further include one or more processors and / or one or more memory devices identical or similar to the processor 112 and memory device 114 described herein. In some implementations, the electronic interface 119 is coupled to or integrated with a user device 170. In some other implementations, the electronic interface 119 is coupled to the control system 110 and / or the memory device 114, or integrated with the control system 110 and / or the memory device 114 (for example, within the enclosure).

[0106] As described above, in some implementations, system 100 optionally includes a respiratory system 120 (also called a respiratory therapy system). The respiratory system 120 may include a respiratory pressure therapy device 122 (hereinafter referred to as respiratory device 122), a user interface 124 (also referred to as a mask or patient interface), a conduit 126 (also referred to as a tube or air circuit), a display device 128, a humidifier tank 129, or any combination thereof. In some implementations, a control system 110, a memory device 114, a display device 128, one or more sensors 130, and a humidifier tank 129 are part of the respiratory device 122. Respiratory pressure therapy means supplying air to the patient's airway inlet at a nominally positive, controlled target pressure relative to the atmosphere throughout the patient's respiratory cycle (as opposed to negative pressure therapy such as a tank ventilator or cuirass). The respiratory device 120 is commonly 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).

[0107] The breathing device 122 is generally used to generate pressurized air to be delivered to a therapeutic user (for example, using one or more motors (e.g., blower motors) to drive one or more compressors). In some implementations, the breathing device 122 continuously generates a constant air pressure, which is delivered to the therapeutic user. In other implementations, the breathing device 122 generates two or more predetermined pressures (e.g., a first predetermined pressure and a second predetermined pressure). In yet another embodiment, the breathing device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the breathing device 122 can transport at least about 6 cm H2O, at least about 10 cm H2O, at least about 20 cm H2O, about 6 cm H2O to about 10 cm H2O, about 7 cm H2O to about 12 cm H2O, etc. The breathing device 122 can also deliver pressurized air at a predetermined flow rate between, for example, about -20 liters / min and about 150 liters / min while maintaining positive pressure (relative to ambient pressure).

[0108] The user interface 124 engages with a portion of the treatment user's face and delivers pressurized air from the breathing device 122 to the treatment user's airway to help prevent airway narrowing and / or collapse during sleep. This may also increase the treatment user's oxygen intake during sleep. Depending on the treatment applied, the user interface 124 can form a seal with, for example, an area or portion of the user's face, thereby facilitating the delivery of gas at a pressure that is sufficiently varied from the ambient pressure, for example, at a positive pressure of about 10 cmH2O relative to the ambient pressure, and enabling the treatment to be carried out effectively. For other forms of treatment, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate the delivery of gas to the airway at a positive pressure of about 10 cmH2O.

[0109] As shown in Figure 2, in some implementations, the user interface 124 is a face mask that covers the nose and mouth of the treatment user (for example, as shown in Figure 2). Alternatively, the user interface 124 may be a nasal mask that supplies air to the treatment user's nose, or a nasal pillow mask that supplies air directly to the treatment user's nostrils. The user interface 124 may include a number of straps (e.g., including hook-and-loop fasteners) for positioning and / or stabilizing the interface on a part of the treatment user (e.g., the face), and a conformal cushion (e.g., silicone resin, plastic, foam, etc.) to facilitate an airtight seal between the user interface 124 and the treatment user. In some examples, the user interface 124 may be a tube-up mask, in which the mask straps are configured to function as conduits for delivering pressurized air to the face mask or nasal mask. The user interface 124 may also include one or more vents that allow carbon dioxide and other gases exhaled by the treatment user 210 to escape. In other implementations, the user interface 124 may include a mouthpiece (for example, a night guard mouthpiece molded to fit the teeth of the treatment user, a mandibular positioning device, etc.).

[0110] The conduit 126 (also called an air circuit or tube) allows air to flow between two components of the respiratory device 120 (e.g., the respiratory device 122 and the user interface 124). In some embodiments, this conduit may have separate branches for inhalation and exhalation. In other implementations, a single branch conduit is used for both inhalation and exhalation. Generally, the respiratory therapy system 120 forms an air path extending between the motor of the respiratory therapy device 122 and the user and / or the user's airway. Thus, the air path generally includes at least the motor of the respiratory therapy device 122, the user interface 124, and the conduit 126.

[0111] One or more of the breathing device 122, user interface 124, conduit 126, display device 128, and humidification tank 129 may include one or more sensors (e.g., pressure sensors, flow sensors, or more generally, any other sensors 130 described herein). These one or more sensors may be used, for example, to measure the air pressure and / or flow rate of pressurized air supplied by the breathing device 122.

[0112] The display device 128 is typically used to display images and / or information related to the breathing device 122, including still images, video images, or both. For example, the display device 128 can provide information about the status of the respiratory device 122 (e.g., whether the respiratory device 122 is on / off, the pressure of the air expelled by the respiratory device 122, the temperature of the air expelled by the respiratory device 122, etc.), and / or other information (e.g., sleep performance metrics, sleep performance score, sleep score, or treatment score (e.g., myAir® score as described in WO 2016 / 061629 and US 2017 / 0311879, each linked by a collective reference), the current date and time, personal information of the user receiving treatment, user questionnaires, etc.). In some implementations, the display device 128 functions as a human-machine interface (HMI) including a graphical user interface (GUI) configured to display images as an input interface. The display device 128 may be an LED display, OLED display, LCD display, etc. The input interface can be, for example, a touchscreen or touch-sensitive board, a mouse, a keyboard, or any sensor system configured to sense input from a human interacting with the respiratory device 122.

[0113] The humidifying tank 129 is coupled to or integrated with the respiratory device 122 and includes a reservoir that can be used to humidify the pressurized air delivered from the respiratory device 122. The respiratory device 122 may include a heater that heats the water in the humidifying tank 129 in order to humidify the pressurized air supplied to the therapeutic user. Furthermore, in some implementations, the conduit 126 may further include a heating element (e.g., coupled to and / or embedded in the conduit 126) that heats the pressurized air delivered to the therapeutic user. The humidifying tank 129 may be fluidically coupled to the water vapor inlet of the air passage and formed to deliver water vapor into the air passage via the water vapor inlet, or it may be formed as part of the air passage itself and aligned with the air passage. In other implementations, the respiratory therapy device 122 or the conduit 126 may include an anhydrous humidifier. The anhydrous humidifier may incorporate a sensor that interfaces with other sensors located elsewhere in the system 100.

[0114] The respiratory system 120 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 automated positive airway pressure (APAP) system, a bilevel or variable positive airway pressure (BPAP or VPAP) system, or any combination thereof. A CPAP system supplies a predetermined air pressure (e.g., determined by a sleep physician) to the treatment user. An APAP system automatically changes the air pressure supplied to the treatment user based, for example, respiratory data related to the treatment user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure lower than the first predetermined pressure (e.g., expiratory positive airway pressure or EPAP).

[0115] Referring to Figure 2, some implementations of the system 100 (Figure 1) are shown. The therapy user 210 of the respiratory device 120 and the therapy adjacent individual 220 (e.g., bed partner) are located in a bed 230 within the environment 280 and lie on a mattress 232. A user interface 124 (e.g., a full-face mask) can be worn by the therapy user 210 during the therapy user's sleep session. The user interface 124 is fluidically coupled and / or connected to the respiratory device 122 via a conduit 126. Conversely, the respiratory device 122 delivers pressurized air to the therapy user 210 via the conduit 126 and the user interface 124, increasing the air pressure in the therapy user 210's throat, thereby helping to prevent airway closure and / or narrowing during sleep. The respiratory therapy device 122 may include a display device 128 that allows the user to interact with the respiratory therapy device 122. The respiratory therapy device 122 may further include a humidification tank 129 for storing water to humidify the pressurized air. The respiratory therapy device 122 can be placed on a nightstand 240 directly adjacent to the bed 230, as shown in Figure 2, more generally on any surface or structure generally adjacent to the bed 230 and / or the user 210. The user may also wear, for example, a blood pressure device and / or an activity tracker 190 while lying on the mattress 232 in the bed 230.

[0116] As shown in Figure 2, although not necessarily so, a treatment user 210 may have a user device 170A, and a treatment adjacent individual 220 may have their own user device 170B. User devices 170A and 170B may be iterations of user device 170, and each device may include any combination of one or more sensors 130 for acquiring sensor data that may be used to generate the sleep performance metrics disclosed herein.

[0117] Returning to Figure 1, the explanation will be as follows: One or more sensors 130 of system 100 include a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoplethysmography (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, a sample sensor 174, a humidity sensor 176, a laser radar sensor 178, or any combination thereof. Generally, each of the one or more sensors 130 is configured to output sensor data received and stored by a memory device 114 or one or more other memory devices.

[0118] One or more sensors 130 are shown and described as including each of the following: pressure sensor 132, flow sensor 134, temperature sensor 136, motion sensor 138, microphone 140, speaker 142, RF receiver 146, RF transmitter 148, camera 150, infrared sensor 152, photoplethysmography (PPG) sensor 154, electrocardiogram (ECG) sensor 156, electroencephalogram (EEG) sensor 158, volume sensor 160, force sensor 162, strain gauge sensor 164, electromyogram (EMG) sensor 166, oxygen sensor 168, specimen sensor 174, humidity sensor 176, and laser radar sensor 178, but more generally, one or more sensors 130 can include any combination and any number of sensors described and / or illustrated herein.

[0119] One or more sensors 130 can be used to generate sensor data such as physiological data, audio data, or both. The control system 110 can use the physiological data generated by one or more sensors 130 to determine sleep-wake signals and one or more sleep-related parameters associated with cohort members during a sleep session. The sleep-wake signals can indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-wakefulness, rapid eye movement (REM) stage, first non-REM stage (commonly called "N1"), second non-REM stage (commonly called "N2"), third non-REM stage (commonly called "N3"), or any combination thereof. N1 and N2 can be considered light sleep stages, and N3 can be considered deep sleep stages. Methods for determining sleep stages based on physiological data generated by one or more sensors, such as sensor 130, are described, for example, in International Publication 2014 / 047310, U.S. Patent No. 10,492,720, U.S. Patent No. 10,660,563, U.S. Patent Application Publication 2020 / 0337634, International Publication 2017 / 132726, International Publication 2019 / 122413, U.S. Patent Application Publication 2021 / 0150873, International Publication 2019 / 122414, and U.S. Patent Application Publication 2020 / 0383580, each of which is incorporated herein by reference in its entirety. Sleep-wake signals can also be time-stamped to indicate the time a cohort member went to bed, the time a cohort member left bed, the time a cohort member attempted to fall asleep, and so on. During a sleep session, the sleep-wake signal may be measured by sensor 130 at a predetermined sampling rate, e.g., one sample per second, one sample every 30 seconds, one sample per minute, etc. Examples of one or more sleep-related parameters that may be determined for a cohort member during a sleep session based on the sleep-wake signal include total bedtime, total sleep duration, sleep latency, post-sleep wakefulness parameters, sleep efficiency, fragmentation index, or any combination thereof.

[0120] Physiological and / or audio data generated by one or more sensors 130 may also be used to determine respiratory signals associated with cohort members during a sleep session. Respiratory signals typically indicate the respiration or breathing of cohort members during a sleep session. Respiratory signals may indicate, for example, respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, pattern of events, pressure setting of the respiratory device 122, or any combination thereof. These events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, RERA, flow limitation (e.g., events where flow does not increase despite an increase in intrathoracic negative pressure indicating an increasing effort), mask leakage (e.g., from the user interface 124), restless legs, sleep disturbance, suffocation, increased heart rate, dyspnea, asthma attack, epileptic seizure, seizure, increased blood pressure, hyperventilation, or any combination thereof. The event can be detected by any means known in the art, such as those described in U.S. Patent No. 5,245,995, U.S. Patent No. 6,502,572, International Publication No. 2018 / 050913, and International Publication No. 2020 / 104465, each of which is incorporated herein by reference in whole.

[0121] The pressure sensor 132 outputs pressure data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the pressure sensor 132 is an air pressure sensor (e.g., a barometric pressure sensor) that generates sensor data indicating the respiration (e.g., inhalation and / or exhalation) and / or ambient pressure of a therapeutic user using the respiratory system 120. In such embodiments, the pressure sensor 132 can be coupled to or integrated with the respiratory device 122. The pressure sensor 132 may be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potential sensor, or any combination thereof. In one example, the pressure sensor 132 can be used to determine the blood pressure of a cohort member.

[0122] The flow sensor 134 outputs flow data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the flow sensor 134 is used to determine the airflow rate from the breathing device 122, the airflow rate through the conduit 126, the airflow rate through the user interface 124, or any combination thereof. In such embodiments, the flow sensor 134 can be connected to or integrated with the breathing device 122, the user interface 124, or the conduit 126. The flow sensor 134 may be a mass flow sensor such as a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot-wire sensor, an eddy current sensor, a membrane sensor, or any combination thereof.

[0123] The temperature sensor 136 outputs temperature data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the temperature sensor 136 generates temperature data indicating the core body temperature of the cohort members, the skin temperature of the cohort members, the temperature of the air flowing from and / or through the conduit 126, the temperature within the user interface 124, the ambient temperature, or any combination thereof. The temperature sensor 136 may be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap temperature sensor or semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0124] The motion sensor 138 outputs motion data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The motion sensor 138 can be used to detect the user's movements during a sleep session and / or the movements of any component of the respiratory therapy system 120, such as the respiratory therapy device 122, the user interface 124, or the conduit 126. The motion sensor 138 may include one or more inertial sensors, such as an accelerometer, gyroscope, and magnetometer. The motion sensor 138 can be used to detect movements or accelerations related to arterial pulsations, such as pulsations in or around the user's face and proximal pulsations of the user interface 124, and is configured to detect features such as shape, velocity, amplitude, or volume of the pulsations. In some implementations, the motion sensor 138 optionally or additionally generates one or more signals representing the user's body movements, which can, for example, obtain signals representing the user's sleep state through the user's respiratory movements.

[0125] The microphone 140 outputs audio data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The audio data generated by the microphone 140 can be played back during a sleep session as one or more sounds (e.g., sounds from cohort members such as the treatment user 210 and / or the treatment adjacent individual 220). The audio data from the microphone 140 can also be used (e.g., with the control system 110) to identify events experienced by cohort members during a sleep session, as will be described in more detail herein. The microphone 140 may be coupled to or integrated with the respiratory device 122, the user interface 124, the conduit 126, user device 170A, or user device 170B. For example, the microphone 140 may be located inside the respiratory therapy device 122, the user interface 124, the conduit 126, or other components. The microphone 140 may also be positioned adjacent to or connected to the outside of the respiratory therapy device 122, the outside of the user interface 124, the outside of the conduit 126, or any other component. The microphone 140 may also be a component of the user device 170 (for example, the microphone 140 is the microphone of a smartphone). The microphone 140 may be integrated with the user interface 124, the conduit 126, the respiratory therapy device 122, or any combination thereof. Generally, the microphone 140 may be positioned at any point within or adjacent to the air path of the respiratory therapy system 120, which includes at least the motor of the respiratory therapy device 122, the user interface 124, and the conduit 126. Thus, the air path is also called the acoustic path.

[0126] Speaker 142 outputs sound waves that can be normally heard by cohort members using system 100 (e.g., treatment user 210 in Figure 2). In one or more implementations, the sound waves may be audible to the user of system 100 or inaudible to the user of system (e.g., ultrasound). Speaker 142 can function, for example, as an alarm clock, or to play an alarm or message to a cohort member (e.g., in response to an event). In some implementations, speaker 142 may be used to transmit audio data generated by microphone 140 to a cohort member. Speaker 142 may be coupled to or integrated with the respiratory device 122, user interface 124, conduit 126, user device 170A, or user device 170B.

[0127] The microphone 140 and speaker 142 can be used as separate devices. In some embodiments, the microphone 140 and speaker 142 can be combined with an acoustic sensor 141, for example, as described in WO 2018 / 050913, which is incorporated herein by reference in its entirety. In such an implementation, the speaker 142 generates or emits sound waves at predetermined intervals, and the microphone 140 detects reflections of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have frequencies inaudible to the human ear (e.g., less than 20 Hz or more than about 18 kHz) so as not to disturb the sleep of the treatment user 210 or the adjacent treatment individual 220 (Figure 2). Based at least in part on data from microphone 140 and / or speaker 142, the control system 110 can determine the location of the treatment user 210 or the treatment adjacent individual 220 (Figure 2), and / or one or more sleep-related parameters described herein, such as respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event pattern, sleep stage, pressure setting of respiratory therapy device 122, mouth leak condition, or any combination thereof. In this context, the SONAR sensor may be understood as relating to active acoustic sensing, such as generating / transmitting an ultrasonic or low-frequency ultrasonic sensing signal (e.g., within a frequency range of approximately 17–23 kHz, 18–22 kHz, or 17–18 kHz) through the air. Such a system can be considered in relation to the aforementioned International Publication Nos. 2018 / 050913 and 2020 / 104465. In some implementations, speaker 142 is a bone conduction speaker. In some implementations, one or more sensors 130 include (i) a first microphone which is identical or similar to microphone 140 and integrated into acoustic sensor 141, and (ii) a second microphone which is identical or similar to microphone 140 but is independent of and separate from the first microphone integrated into acoustic sensor 141.

[0128] In some implementations, the sensor 130 includes (i) a first microphone identical or similar to the microphone 140 and integrated with the acoustic sensor 141, and (ii) a different second microphone identical or similar to the microphone 140 and separated from the first microphone integrated with the acoustic sensor 141.

[0129] The RF transmitter 148 generates and / or transmits radio waves (e.g., high-frequency band, low-frequency band, long-wave signal, short-wave signal, etc.) having a predetermined frequency and / or amplitude. The RF receiver 146 detects reflections of the radio waves transmitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the location of the cohort members and / or one or more sleep-related parameters described herein. The RF receiver (RF receiver 146 and RF transmitter 148 or another RF pair) may also be used for wireless communication between the control system 110, the respiratory device 122, one or more sensors 130, user device 170A, user device 170B, or any combination thereof. Although the RF receiver 146 and RF transmitter 148 are shown as separate distinct elements in Figure 1, in some implementations the RF receiver 146 and RF transmitter 148 are combined as part of an RF sensor 147. In some such embodiments, the RF sensor 147 includes a control circuit. The specific form of RF communication could be Wi-Fi, Bluetooth (registered trademark), etc.

[0130] In some embodiments, the RF sensor 147 is part of a mesh system. An example of a mesh system is a WiFi mesh system, which may include mesh nodes, mesh routers, and mesh gateways, each of which may be mobile / movable or fixed. In such embodiments, the WiFi mesh system includes WiFi routers and / or WiFi controllers, each of which includes an RF sensor identical or similar to the RF sensor 147, as well as one or more satellites (e.g., access points). The WiFi routers and satellites communicate with each other continuously using WiFi signals. The WiFi mesh system can be used to generate motion data based on changes in the WiFi signal between the routers and satellites (e.g., differences in received signal strength) caused by the movement of objects or people partially interfering with the signal. This motion data may represent exercise, respiration, heart rate, walking, falls, behavior, etc., or any combination thereof.

[0131] The camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, moving images, thermal images, or a combination thereof) that can be stored in the memory device 114. The control system 110 can use the image data from the camera 150 to determine one or more sleep-related parameters described herein. For example, the image data from the camera 150 can be used to identify the location of cohort members, determine the time when cohort members get into bed, and determine the time when cohort members get out of bed.

[0132] The infrared (IR) sensor 152 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 the memory device 114. Using the infrared data from the IR sensor 152, one or more sleep-related parameters during a sleep session can be determined, including the temperature and / or movement of the cohort members. The IR sensor 152 can also be used in combination with the camera 150 to measure the presence, location, and / or movement of the cohort members. For example, the IR sensor 152 can detect infrared light with wavelengths between approximately 700 nm and 1 mm, while the camera 150 can detect visible light with wavelengths between approximately 380 nm and 740 nm.

[0133] The PPG sensor 154 outputs physiological data related to the cohort member that can be used to determine one or more sleep-related parameters such as 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 154 can be worn by the cohort member, embedded in clothing and / or fabric worn by the cohort member, embedded in and / or coupled to the user interface 124 and / or its associated helmet (e.g., straps).

[0134] The ECG sensor 156 outputs physiological data related to the electrical activity of the cohort member's heart. In some implementations, the ECG sensor 156 includes one or more electrodes placed on or around a portion of the cohort member during a sleep session. The physiological data from the ECG sensor 156 can be used, for example, to determine one or more sleep-related parameters described herein.

[0135] The EEG sensor 158 outputs physiological data related to the electrical activity of the cohort member's brain. In some implementations, the EEG sensor 158 includes one or more electrodes placed on or around the cohort member's scalp during a sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine the sleep state of the cohort member at any given point in time during a sleep session. In some implementations, the EEG sensor 158 may be integrated into the user interface 124 and / or associated helmet (e.g., a strap).

[0136] The capacitance sensor 160, force sensor 162, and strain gauge sensor 164 output data that can be stored in the memory device 114 and used by the control system 110 to determine one or more sleep-related parameters as described herein. The EMG sensor 166 outputs physiological data related to the electrical activity produced by one or more muscles. The oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of the gas (e.g., in the conduit 126 or in the user interface 124). The oxygen sensor 168 may be, for example, an ultrasonic oxygen sensor, an electro-oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some implementations, one or more sensors 130 further include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, an oxygen concentration sensor, or any combination thereof.

[0137] The sample sensor 174 may be used to detect the presence of a sample in the exhalation of a cohort member (e.g., treatment user 210). The data output by the sample sensor 174 is stored in the memory device 114 and used by the control system 110 to determine the identifiability and concentration of any sample in the treatment user 210's respiration. In some implementations, the sample sensor 174 is positioned near the treatment user 210's mouth to detect a sample in respiration exhaled from the treatment user 210's mouth. For example, if the user interface 124 is a face mask covering the treatment user 210's nose and mouth, the sample sensor 174 can be positioned inside the face mask to monitor the treatment user 210's mouth breathing. In other implementations, for example, if the user interface 124 is a nasal mask or nasal pillow mask, the sample sensor 174 can be positioned near the treatment user 210's nose to detect a sample in respiration exhaled through the treatment user's nose. In other implementations, if the user interface 124 is a nasal mask or nasal pillow mask, the sample sensor 174 can be located near the mouth of the treatment user 210. In this implementation, the sample sensor 174 can be used to detect whether there is any unintentional air leaking from the mouth of the treatment user 210. In some implementations, the sample sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbonaceous chemicals or compounds. In some implementations, the sample sensor 174 may also be used to detect whether the treatment user 210 is breathing through their nose or mouth. For example, if the presence of a sample is detected by data output by the sample sensor 174 (located near the mouth of the treatment user 210 or inside the face mask) (in an implementation where the user interface 124 is a face mask), the control system 110 can use that data to indicate that the treatment user 210 is breathing through their mouth.

[0138] The humidity sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The humidity sensor 176 can be used to detect humidity in various areas around the treatment user (e.g., within the conduit 126 or user interface 124, near the face of the treatment user 210, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the breathing device 122, etc.). Thus, in some embodiments, the humidity sensor 176 can be placed within the user interface 124 or conduit 126 to monitor the humidity of the pressurized air from the breathing device 122. In other implementations, the humidity sensor 176 is placed near any area where the humidity level needs to be monitored. The humidity sensor 176 may also be used to monitor the ambient humidity of the environment 280 around the treatment user 210 and / or adjacent treatment individual 220, for example, the ambient humidity of the air in a bedroom.

[0139] The LiDAR (Light Detection and Ranging) sensor 178 can be used for depth sensing. Such optical sensors (e.g., laser sensors) can be used to detect objects and create three-dimensional (3D) maps of the surrounding environment, such as living spaces. LiDAR generally uses pulsed lasers to measure time of flight. LiDAR is also called 3D laser scanning. In one use case of such a sensor, a stationary or mobile device (such as a smartphone) equipped with the LiDAR sensor 178 can measure and map an area more than 5 meters away from the sensor. For example, LiDAR data can be fused with point cloud data estimated by an electromagnetic RADAR sensor. The LiDAR sensor 178 can also use artificial intelligence (AI) to automatically create geofencing for a RADAR system by detecting and classifying features in space that may cause problems for the RADAR system, such as glass windows (which may be highly reflective to RADAR). For example, LiDAR can also be used to estimate a person's height, as well as changes in height that occur when a person is sitting, falling, etc. LiDAR can be used to form a 3D mesh representation of the environment. In further applications, LiDAR can reflect radio waves from solid surfaces (e.g., radio-transparent materials), thereby enabling the classification of different types of obstacles.

[0140] Although shown individually in Figure 1, any combination of one or more sensors 130 may be integrated and / or coupled to any one or more components of system 100, including the breathing device 122, user interface 124, conduit 126, humidification tank 129, control system 110, user device 170 (e.g., user devices 170A, 170B in Figure 2), or any combination thereof. For example, the microphone 140 and speaker 142 are integrated and / or coupled to user device 170, and the pressure sensor 130 and / or flow sensor 132 are integrated and / or coupled to breathing device 122. In some implementations, at least one of the one or more sensors 130 is not coupled to the respiratory device 122, the control system 110, or the user device 170, and is typically positioned near the therapeutic user 210 or therapeutic adjacent individual 220 during a sleep session (for example, positioned to or in contact with part of the therapeutic user 210 or therapeutic adjacent individual 220, and also worn by the therapeutic user 210 or therapeutic adjacent individual 220, coupled to or positioned on a nightstand, coupled to a mattress, coupled to the ceiling, etc.).

[0141] For example, as shown in Figure 2, one or more sensors 130 may be positioned at a first position 250a on the nightstand 240, adjacent to the bed 230 and the treatment user 210. Alternatively, one or more of the sensors 130 may be positioned at a second position 250B on and / or within the mattress 232 (e.g., the sensor is coupled to and / or integrated with the mattress 232). Another one or more of the sensors 130 may be positioned at a third position 250C on the bed 230 (e.g., the auxiliary sensor 140 is coupled to and / or integrated with the headboard, footboard, or other position on the frame of the bed 230). One or more of the sensors 130 may be positioned at a fourth position 250d on a wall or ceiling generally adjacent to the bed 230 and / or the user 210. One or more of the sensors 130 may be positioned at a fifth position, such that they are coupled to and / or positioned on and / or within the housing of the respiratory device 122 of the respiratory system 120. Furthermore, one or more sensors 130 may be positioned at the sixth position 250F so that the sensors are coupled to and / or placed on the treatment user 210 (for example, the sensors are embedded in or coupled to the fabric or clothing worn by the treatment user 210 during the sleep session). Similarly, one or more sensors 130 may be positioned at the seventh position so that the sensors are coupled to and / or placed on the treatment adjacent individual 220 (for example, the sensors are embedded in or coupled to the fabric or clothing worn by the treatment adjacent individual 220 during the sleep session). Furthermore, one or more sensors 130 may be positioned at the eight positions 250G of the nightstand adjacent to the bed 230, or on the treatment adjacent individual 220. More generally, one or more sensors 130 may be positioned at any appropriate position relative to the monitored cohort member so that the sensors 140 can generate physiological data related to the cohort member (e.g., the treatment user 210 and / or the treatment adjacent individual 220) during one or more sleep sessions.

[0142] The user device 170 (Figure 1) includes a display device 172. The user device 170 may be a mobile device such as a smartphone, tablet, or laptop. Alternatively, the user device 170 may 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®, Google Nest®, Amazon Echo®, Amazon Echo Show®, or an Alexa®-enabled device). In some implementations, the user device is a wearable device (e.g., a smartwatch). The display device 172 is generally used to display images including still images, moving images, or both. In some embodiments, the display device 172 functions as a human-machine interface (HMI) including a graphical user interface (GUI) and an input interface configured to display images. The display device 172 may be an LED display, an OLED display, an LCD display, etc. The input interface can be, for example, a touchscreen or touch-sensitive board, a mouse, a keyboard, or any sensor system configured to sense input from a human interacting with the user device 170. In some implementations, one or more user devices may be used by the system 100 and / or included in the system 100, such as a separate user device for each member of the sleep cohort.

[0143] The light source 180 is commonly used to emit light having intensity and wavelength (e.g., color). For example, the light source 180 can emit light with wavelengths between approximately 380 nm and approximately 700 nm (e.g., wavelengths in the visible light spectrum). The light source 180 may include, for example, one or more light-emitting diodes, one or more organic light-emitting diodes, light bulbs, lamps, incandescent bulbs, CFL bulbs, halogen bulbs, or any combination thereof. In some implementations, the intensity and / or wavelength (e.g., color) of the light emitted from the light source 180 may be modified by the control system 110. The light source 180 can also emit light in a predetermined emission pattern such as continuous emission, pulsed emission, periodic emission of different intensities (e.g., including emission cycles in which the intensity gradually increases and then decreases), or any combination thereof. The light emitted from the light source 180 may be directly observed by the cohort members, or it may be reflected or refracted before reaching the cohort members. In some implementations, the light source 180 includes one or more light pipes.

[0144] In some implementations, the light source 180 is physically coupled to or integrated with the respiratory therapy system 120. For example, the light source 180 may be physically coupled to or integrated with the respiratory device 122, the user interface 124, the conduit 126, the display device 128, or any combination thereof. In some implementations, the light source 180 is physically coupled to or integrated with the user device 170. In some other implementations, the light source 180 is separated and distinct from the respiratory therapy system 120, the user device 170, and the activity tracker 190. In such implementations, the light source 180 may be positioned facing the cohort members, for example, on a nightstand 240, a bed 230, other furniture, a wall, or a ceiling.

[0145] The activity tracker 190 is generally used to assist in generating physiological data for determining activity measurements relevant to a cohort member (e.g., a treatment user 210 or a treatment adjacent individual 220). Activity measurements may include, for example, steps taken, distance traveled, steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, mean respiratory rate, resting respiratory rate, maximum respiratory rate, respiratory rate variability, heart rate, mean heart rate, resting heart rate, maximum heart rate, heart rate variability, calories burned, blood oxygen saturation, skin electrical activity (also called skin electrical conductivity or skin electroreactivity), or any combination thereof. The activity tracker 190 includes one or more sensors 130 as described herein, for example, a motion sensor 138 (e.g., one or more accelerometers and / or gyroscopes), a PPG sensor 154, and / or an ECG sensor 156.

[0146] In some implementations, the activity tracker 190 is a wearable device that can be worn by a cohort member, such as a smartwatch, wristband, ring, or patch. For example, referring to Figure 2, the activity tracker 190 is worn on the wrist of the treatment user 210. The activity tracker 190 may also be coupled to or integrated with clothing or garments worn by the treatment user. In some cases, a similar activity tracker may be worn on the wrist of a treatment adjacency 220, or coupled to or integrated with clothing or garments worn by the treatment adjacency 220. Alternatively, the activity tracker 190 may be coupled to or integrated with user devices 170A and / or user devices 170B (e.g., within the same memory device). More generally, the activity tracker 190 may be communicatively coupled to the control system 110, memory 114, respiratory system 120, user devices 170A and / or user devices 170B, or physically integrated into them (e.g., within a memory device).

[0147] Referring back to Figure 1, the control system 110 and the memory device 114 are described and shown in Figure 1 as separate and distinct components of system 100, although in some implementations the control system 110 and / or the memory device 114 are integrated into the user device 170 and / or the breathing device 122. Alternatively, in some implementations the control system 110 or a part thereof (e.g., the processor 112) may reside in the cloud (e.g., embedded in a server, embedded in an IoT (Internet of Things) device (e.g., smart TV, smart thermostat, smart electrical appliance, smart lighting, etc.), connected to the cloud, and processed by an edge cloud), or in one or more servers (e.g., a remote server, a local server, or any combination thereof).

[0148] While System 100 is shown to include all of the above components, according to the implementations of this disclosure, a system for generating physiological data and determining recommended notifications or actions for cohort members may include more or fewer components. For example, a first alternative system includes a control system 110, a memory device 114, and at least one of one or more sensors 130. Another example is a second alternative system including a control system 110, a memory device 114, at least one of one or more sensors 130, and a user device 170. Yet another example is a third alternative system including a control system 110, a memory device 114, a respiratory system 120, at least one of one or more sensors 130, and the first and second user devices 170. Thus, various systems may be formed using any part of the components illustrated and described herein and / or in combination with one or more other components.

[0149] As used herein, a sleep session can be defined in various ways, for example, based on an initial start time and an end time. Referring to Figure 3, an exemplary timeline 301 for a sleep session is shown. Timeline 301 is based on bedtime (tベッド ), sleep onset time (t GTS ), initial sleep time ((t 睡眠 ), first minute awakening MA1 and second minute awakening MA2, awakening time (t 覚醒 ), wake-up time (t 起床 ) includes.

[0150] In some implementations, a sleep session is defined as the duration of sleep for a cohort member. In such implementations, a sleep session has a start time and an end time, and the cohort member remains awake until the end time. In other words, any period of time when the cohort member is awake is not included in the sleep session. According to the definition of a first sleep session, if a cohort member wakes up and falls back asleep multiple times during the same night, each sleep session separated by periods of wakefulness becomes a separate sleep session.

[0151] Alternatively, in some implementations, a sleep session has a start time and an end time, and a cohort member may end the sleep session if the sleep session does not end during the session, as long as the cohort member's continuous sleep duration falls below a sleep duration threshold. The wake duration threshold can be defined as a percentage of the sleep session. The wake duration threshold could 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, or any other threshold percentage. In some implementations, the wake duration threshold is defined as a fixed amount of time, such as about 1 hour, about 30 minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 2 minutes, or other amounts of time.

[0152] In some implementations, a sleep session is defined as the entire period of time from when a cohort member first gets into bed at night to when the cohort member finally gets out of bed the next morning. In other words, a sleep session may be defined as a period that starts at a first time (e.g., 10:00 PM), referred to as the current night of a first date (e.g., Monday, January 6, 2020), when the cohort member first gets into bed to attempt to sleep (this is not the case, for example, if the cohort member first intends to watch television or use a smartphone before going to sleep), and ends at a second time on a second date (e.g., Tuesday, January 7, 2020), referred to as the next morning (e.g., 7:00 AM), when the cohort member first gets out of bed and does not return to bed to attempt to sleep the next morning.

[0153] In some implementations, a cohort member may manually define the start of a sleep session and / or manually end a sleep session. For example, the cohort member may select (e.g., by clicking or tapping) a user-selectable element presented on a display device 172 of a user device 170 (FIG. 1) to manually start or end a sleep session.

[0154] Bedtime t ベッド is associated with the time when a cohort member first gets into bed (e.g., bed 230 in FIG. 2) before attempting to sleep (e.g., when the cohort member is lying down or sitting on the bed). Bedtime t ベッド may be identified based on a bed threshold duration to distinguish between times when the cohort member gets into bed to sleep and times when the cohort member gets into bed for other reasons (e.g., watching television). 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. Here, bedtime t ベッド is described herein with reference to a bed, but more generally, bedtime t ベッドThis can refer to the time when a cohort member first entered any position suitable for sleeping (e.g., sofa, chair, sleeping bag, etc.).

[0155] Sleep onset time (GTS) is related to the time it took for a cohort member to first fall asleep after getting into bed. ベッド For example, after getting into bed, cohort members 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 170, etc.). Early sleep time (t 睡眠 ) is the time when the cohort member first went to sleep. For example, the initial sleep time (t 睡眠 ) may also be the time when the cohort member first entered the non-REM sleep stage.

[0156] Awakening time t 覚醒 This is the time when a cohort member became awake without returning to sleep (for example, the opposite of the time when a cohort member woke up in the middle of the night and returned to sleep). After initially falling asleep, cohort members may experience one of several short periods of unconscious micro-awakening (e.g., micro-awakening MA1, MA2) (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.). The cohort member's wake time t 覚醒 Conversely, after each minor awakening in MA1 and MA2, they return to sleep. Similarly, after initially falling asleep (e.g., getting up to go to the toilet, caring for children or pets, or sleepwalking), cohort members may experience one or more conscious awakenings (e.g., awakening A). However, after waking, cohort members return to sleep again. Therefore, the awakening time t 覚醒 This can be defined, for example, based on the arousal threshold duration (e.g., at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).

[0157] Similarly, wake-up time t 起床This relates to the time a cohort member leaves their bed and remains outside of it to complete the sleep session (for example, the opposite of a cohort member getting up at night to go to the toilet, take care of children or pets, or sleepwalk). In other words, wake-up time t 起床 This is the time when a cohort member last wakes up without returning to bed before the next sleep session (e.g., the following evening). Therefore, wake-up time 起床 This can be defined, for example, based on the wake-up threshold duration (e.g., cohort members need at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). Bedtime t for the second and subsequent sleep sessions ベッド The time can also be defined based on the wake-up threshold duration (e.g., at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours, etc.) for the cohort member.

[0158] As described above, the cohort members were the first t ベッド from the last t 起床 During the night, the user can get up and out of bed at least once. In some embodiments, the last wake time t 覚醒 and / or last wake-up time t 起床 This is identified or determined based on a predetermined threshold duration after an event (e.g., falling asleep or getting out of bed). Such threshold durations can be customized for cohort members. For a typical cohort member who goes to sleep at night and wakes up and gets out of bed in the morning, it is approximately 12 to 18 hours (the cohort member's wakefulness (t)). 覚醒 ) or wake up (t 起床 ) and the bed occupancy of cohort members (t ベッド ), falling asleep (t 入眠 ) or sleep (t 睡眠 Any time period between ) can be used. For cohort members with long periods of bed rest, a shorter threshold time (e.g., approximately 8 to 14 hours) can be used. The threshold period may be initially selected and / or adjusted later based on a system that monitors the sleep behavior of cohort members.

[0159] Total time in bed (TIB) is calculated as the time spent in bed t ベッド From wake-up time 起床 This is the duration up to the initial sleep time. Total sleep time (TST) relates to the duration between the initial sleep time and the wake time, and does not include any conscious or unconscious awakenings and / or minute awakenings in between. Generally, total sleep time (TST) is shorter than total bedtime (TIB) (e.g., 1 minute shorter, 10 minutes shorter, 1 hour shorter, etc.). For example, referring to time axis 301 in Figure 3, total sleep time (TST) is from the initial sleep time t sleep and alarm time t wake Although it spans between these periods, the durations of the first micro-awakening MA1, the second micro-awakening MA2, and awakening A are excluded. As illustrated, in this example, total sleep time (TST) is shorter than total time spent asleep (TIB).

[0160] In some implementations, total sleep time (TST) may be defined as total continuous sleep time (PTST). In such implementations, total continuous sleep time does not include a predetermined initial portion or period of the first non-REM stage (e.g., light sleep stage). For example, a predetermined initial portion could be approximately 30 seconds to 20 minutes, 1 minute to 10 minutes, 3 minutes to 5 minutes, etc. Total continuous sleep time is one measure of continuous sleep and smooths the sleep-wake sleep progression diagram. For example, when a cohort member first falls asleep, the cohort member may be in the first non-REM stage for a very short time (e.g., about 30 seconds), then return to the wakeful stage for a short time (e.g., 1 minute), and then return to the first non-REM stage. In this example, total continuous sleep time excludes the first instance of the first non-REM stage (e.g., about 30 seconds).

[0161] In some embodiments, the sleep session is defined by the time of bedtime (t ベッド It starts from ) and wake-up time (t 起床 ) is defined as the time ending at t, i.e., total time in bed (TIB). In some embodiments, a sleep session is defined as the initial sleep time (t 睡眠 ) begins, and the wake-up time (t 覚醒It is defined as ending at ). In some embodiments, a sleep session is defined as total sleep time (TST). In some embodiments, a sleep session is defined as the time of sleep onset (t GTS ) begins, and the wake-up time (t 覚醒 It is defined as ending at the time of sleep onset (t). In some embodiments, a sleep session is defined as ending at the time of sleep onset (t). GTS It starts from ) and wake-up time (t 起床 It is defined as ending at bedtime (t). In some embodiments, a sleep session is defined as ending at bedtime (t). ベッド ) begins, and the wake-up time (t 覚醒 It is defined as ending at the initial sleep time (t). In some implementations, a sleep session is defined as ending at the initial sleep time (t). 睡眠 It starts with ) and wake-up time (t 起床 It is defined as ending in ).

[0162] Referring to Figure 4, exemplary sleep progression diagrams 400 corresponding to timeline 400 (Figure 4) in several implementation forms are shown. As shown in the figure, the sleep progression diagram 400 includes a sleep-wake signal 401, an arousal stage axis 410, a REM stage axis 420, a light sleep stage axis 430, and a deep sleep stage axis 440. The intersections between the sleep-wake signal 401 and one of axes 410, 420, 430, and 440 indicate any predetermined sleep stage during a sleep session.

[0163] The sleep-wake signal 401 can be generated based on physiological data associated with the cohort members (e.g., generated by one or more sensors 130 (Figure 1) described herein). The sleep-wake signal can represent one or more sleep states or stages, including wakefulness, relaxed wakefulness, micro-wakefulness, REM stage, first non-REM stage, second non-REM stage, third non-REM stage, or any combination thereof. In some implementations, one or more of the first non-REM stage, second non-REM stage, and third non-REM stage may be grouped together and classified into light sleep stages or deep sleep stages. For example, a light sleep stage may include the first non-REM stage, while a deep sleep stage may include the second and third non-REM stages. The sleep progression diagram 400 shown in Figure 4 includes a light sleep stage axis 430 and a deep sleep stage axis 440, but in some embodiments, the sleep progression diagram 400 may include axes for each of the first non-REM stage, second non-REM stage, and third non-REM stage. In other embodiments, the sleep-wake signal may represent respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, event patterns, or any combination thereof. Information describing the sleep-wake signal can be stored in the memory device 114.

[0164] The sleep progression diagram 400 can be used to determine one or more sleep-related parameters such as sleep latency (SOL), post-sleep wakefulness onset (WASO), sleep efficiency (SE), sleep fragmentation index, sleep block, or any combination thereof.

[0165] Sleep latency (SOL) is the time it takes to fall asleep (t 入眠 ) and initial sleep time (t 睡眠It is defined as the time between the time of falling asleep and the time of sleep onset. In other words, sleep latency indicates the time required for a cohort member to actually fall asleep after they first attempt to fall asleep. In some embodiments, sleep latency is defined as sustained sleep latency (PSOL). The difference between sustained sleep latency and sleep latency is that sustained sleep latency is defined as the duration between the time of falling asleep and a given amount of sustained sleep. In some embodiments, a given amount of sustained sleep may include, for example, at least 10 minutes of sleep within the second non-REM stage, the third non-REM stage and / or REM stage, and 2 minutes or less of awake REM stage, the first non-REM stage and / or movement in between. In other words, sustained sleep latency requires, for example, up to 8 minutes of sustained sleep within the second non-REM stage, the third non-REM stage and / or REM stage. In other embodiments, a given amount of sustained sleep may include at least 10 minutes of sleep within the first non-REM stage, the second non-REM stage and / or REM stage after the initial sleep time. In this implementation, a predetermined amount of sustained sleep can eliminate any minor awakenings (for example, a 10-second minor awakening will not trigger a 10-minute period of rest).

[0166] Post-sleep wakefulness onset (WASO) is related to the total duration of wakefulness for a cohort member between the initial sleep time and the wakefulness time. Therefore, post-sleep wakefulness includes short and minute wakefulnesses during a sleep session, whether conscious or unconscious (e.g., minute wakefulnesses MA1, MA2 shown in Figure 4). In some embodiments, post-sleep wakefulness onset (WASO) is defined as persistent post-sleep wakefulness onset (PWASO), which includes only the total duration of wakefulness of a predetermined length (e.g., greater than 10 seconds, greater than 30 seconds, greater than 60 seconds, greater than approximately 5 minutes, greater than approximately 10 minutes, etc.).

[0167] 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 for that sleep session is 93.75%. Sleep efficiency indicates the sleep hygiene of a cohort member. For example, if a cohort member goes to bed before bedtime and spends time on other activities (such as watching television), sleep efficiency decreases (the cohort member may be penalized). In some implementations, sleep efficiency (SE) may be calculated based on total time in bed (TIB) and the total time a cohort member attempts to sleep. In such implementations, the total time a cohort member attempts to sleep is defined as the duration between the time of sleep onset (GTS) and the time of wake-up as described herein. For example, if the total sleep time is 8 hours (e.g., between 11 p.m. and 7 a.m.), the time of sleep onset is 10:45 p.m., and the time of wake-up is 7:15 a.m., the sleep efficiency parameter is calculated to be approximately 94%.

[0168] The fragmentation index is determined at least partially based on the number of awakenings during a sleep session. For example, if a cohort member has two minor awakenings (e.g., minor awakening MA1 and minor awakening MA2 as shown in Figure 4), the fragmentation index can be expressed as 2. In some implementations, the fragmentation index is scaled within a predetermined integer range (e.g., between 0 and 10).

[0169] Sleep blocks relate to the transition between any stage of sleep (e.g., the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or REM) and the wakefulness stage. Sleep blocks can be calculated, for example, with a resolution of 30 seconds.

[0170] In some embodiments, the systems and methods described herein generate or analyze sleep time diagrams including sleep-wake signals to determine bedtime (t ベッド ), sleep onset time (t GTS ), initial sleep time (t 睡眠 ), one or more first minute awakenings (e.g., MA1 and MA2), awakening time (t 覚醒 ), wake-up time (t 起床This may include determining or recognizing, at least in part, the sleep-wake signals in a sleep progression chart, or any combination thereof.

[0171] In other embodiments, one or more sensors 130 determine the time of going to bed (t ベッド ), sleep onset time (t GTS ), initial sleep time (t 睡眠 ), one or more first minute awakenings (e.g., MA1 and MA2), awakening time (t 覚醒 ), wake-up time (t 起床 ), or a combination thereof, can be used to determine or recognize sleep sessions. For example, bedtime t ベッド The sleep duration may be determined based on data generated by, for example, the exercise sensor 138, the microphone 140, the camera 150, or any combination thereof. The sleep duration may be determined based on, for example, data from the exercise sensor 138 (e.g., data indicating that the cohort member is not exercising), data from the camera 150 (e.g., data indicating that the cohort member is not exercising and / or that the cohort member has turned off the lights), data from the microphone 140 (e.g., data indicating that the TV is off), data from the user device 170 (e.g., data indicating that the cohort member is no longer using the user device 170), data from the pressure sensor 132 and / or the flow sensor 134 (e.g., data indicating that the treatment user turns on the respiratory device 122, data indicating that the treatment user is wearing the user interface 124, etc.), or any combination thereof.

[0172] Sleep progression diagram 400 depicts REM phases gradually shortening as a sleep session progresses, but this is not always the case. In some cases, the duration of REM phases gradually increases as a sleep session progresses (for example, the first REM phase is shorter than the last).

[0173] Figure 5 is a perspective view of a pair of cohort members, including first and second cohort members 510, 520, according to some aspects of the present disclosure. In some cases, aspects and features of the present disclosure can be used between two or more cohorts 510, 520 without the use of respiratory therapy devices or other sleep-related therapy devices. In this case, the system (e.g., system 100 in Figure 1) can continue to monitor the cohort members 510, 520 and determine sleep performance metrics, which can be used to determine coordinated sleep performance metrics, assess individual or cohort goals, generate individual or cohort coaching suggestions, or improve the sleep quality of the cohort members 510, 520.

[0174] Cohort members 510 and 520 both sleep in bed 530 on mattress 532. A system for tracking the sleep sessions of cohort members 510 and 520 can be implemented via a first user device 570A and a second user device 570B. User device 570A may be a smartphone associated with cohort member 510, and user device 570B may be a smartphone associated with cohort member 520, or it may be another device such as a sonar-enabled and / or radar-enabled (optionally including a microphone) bedside device configured to monitor physiological signals (e.g., heart, respiration, and / or motor signals).

[0175] The distance between the first user device 570A and the second user device 570B can be estimated based on sensor data collected by one or more sensors within each user device 570A, 570B. In some cases, the distance can be estimated based on the signal strength of a radio signal, such as a Bluetooth® signal, transmitted between user devices 570A, 570B. In some cases, the distance can be estimated based on echoes detected by the microphones of user devices 570A, 570B.

[0176] The distance between user devices 570A and 570B can be used to estimate whether cohort member 520 is sleeping in the same environment 500 as cohort member 510. For example, as shown in Figure 5, if the distance is determined to be relatively small, it can be estimated that cohort member 520 is sleeping in the same bed as cohort member 510. If the distance is slightly greater, it can be estimated that cohort members 510 and 520 are sleeping in the same room. In some cases, this distance can indicate that cohort members 510 and 520 are sleeping in adjacent rooms. In some cases, the distance can indicate that cohort members 510 and 520 are sleeping in the same house (e.g., the same building). In some cases, this distance can indicate that cohort member 520 is not sleeping in the same environment 500 as cohort member 510.

[0177] In some cases, the distance between user devices 570A and 570B can be used to better identify the location of one or two of the cohort members 510 and 520, for example, by echolocation or detection by other sensors. For example, knowledge of the distance between user devices 570A and 570B can be combined with knowledge of the distance between cohort member 510 and each user device 570A and 570B to accurately identify cohort member 510 within the environment 500.

[0178] Figure 6 is a flowchart of a process 600 for generating and presenting sleep performance metrics for a sleep cohort, according to several aspects of the present disclosure. The process 600 can be performed by the system 100 of Figure 1 or its components, or by several examples or components of the system 100 of Figure 1.

[0179] In block 602, sensor data is received. Sensor data may be received from one or more sensors. The sensor data received in block 602 is related to the sleep sessions of individuals in the environment. In other words, the sensor data received in block 602 includes sensor data acquired from individuals participating in the sleep session (e.g., first cohort members such as treatment-adjacent individuals). In some cases, the sensor data may also include sensor data acquired before or after the sleep session.

[0180] The environment may be a bed, a room, a set of adjacent rooms, or a house or other building. In some cases, the sensor data received in block 602 may relate to the sleep session of a second individual in the environment (e.g., a second cohort member such as a therapeutic user).

[0181] In block 604, sensor data is used to determine the first sleep performance data. The first sleep performance data includes data relating to the sleep session performance of the first cohort member. Determining the sleep performance data may include analyzing the sensor data to identify various metrics, such as sleep quality data, related to the sleep session of the first cohort member. Where appropriate, the sleep performance data may include sleep stage information, sleep state information, and / or sleep performance data.

[0182] In block 606, secondary sleep performance data may be received. The secondary sleep performance data relates to the sleep sessions of the second cohort member, more specifically, the sleep sessions of the second cohort member in the same environment as the first cohort member. The sleep sessions of the second cohort member may completely or partially overlap with the sleep sessions of the first cohort member.

[0183] In some cases, when second sleep performance data is received in block 606, the second sleep performance data is determined from individual sensor data associated with the second cohort member. In some cases, receiving second sleep performance data in block 606 may also include determining second sleep performance data from sensor data (e.g., sensor data from block 602 or other sensor data).

[0184] In block 608, one or more sleep performance metrics may be generated from the first sleep performance data and the second sleep performance data. The sleep performance metrics may be any useful metrics used to measure the performance of a sleep session. In some cases, one or more sleep performance metrics may include i) a coordinated sleep performance score, ii) an individual sleep performance score related to the first cohort member, iii) an individual sleep performance score related to the second cohort member, iv) a sleep progression chart related to the first cohort member, v) a sleep progression chart related to the second cohort member, vi) a treatment score for treating the user, vii) a resonance score, or viii) any combination of i-vii.

[0185] In some cases, the sleep performance metric may be a coordinated sleep performance score calculated using a first sleep performance score associated with the first cohort member and a second sleep performance score associated with the second cohort member.

[0186] In some cases, generating sleep performance metrics in block 608 may include synchronizing first sensor data and second sensor data. The first sensor data may be sensor data received in block 602 from one or more sensors of a first set associated with a first cohort member. The second sensor data may be sensor data received from one or more sensors of a second set associated with a second cohort member.

[0187] In block 610, sleep performance metrics may be presented. Presentation in block 610 may include presenting one or more sleep performance metrics to the first cohort members, the second cohort members, the third party, or any combination thereof. Presentation may include presenting component and / or minor component scores related to the sleep performance metrics, and optionally, presenting the contribution of the component and / or minor component scores to the given sleep performance metrics.

[0188] In some cases, presenting sleep performance metrics in block 610 may involve generating entries in a feed associated with the cohort members or the cohort. This feed may be a social media feed or a similar feed. The feed may include summary information, sleep performance metrics, or other such information. The feed may also be interactive to allow other cohort members or third parties to interact with the entries on the feed, thereby encouraging cohort members to improve their sleep quality.

[0189] In some cases, the first cohort members are treatment-adjacent individuals and the second cohort members are treatment users, but in some cases, the reverse may be true. In some cases, both the first and second cohort members are treatment users. When cohort members are treatment users, sensor data may include data from one or more sensors associated with the treatment user's treatment device, and sleep performance data may include treatment data associated with the use of the treatment device.

[0190] In an exemplary case, process 600 may be performed by a user device of a first cohort member (e.g., a smartphone). The user device may receive sensor data from one or more sensors on the user device and / or from one or more sensors actionably coupled to the user device. The user device may then determine first sleep performance data from the sensor data. The user device may then receive second sleep performance data. In some cases, the user device may receive sensor data and use the sensor data to determine second sleep performance data. However, in some cases, the user device may receive second sleep performance data determined from sensor data (e.g., on a user device of a second cohort member).

[0191] In another exemplary case, process 600 may be performed by a server (e.g., a cloud server) that can receive sensor data and / or sleep performance data from one or more user devices.

[0192] In some cases, an incentive may be provided in any block 612. Providing an incentive may include determining that a sleep performance metric has reached a threshold, or determining that a cohort member or cohort has achieved a goal (e.g., they may set, track and / or report goals according to process 700), or completing a coaching proposal for a number of thresholds (e.g., they may set, track and / or report goals according to process 800). Providing an incentive may also include initiating the transfer of an incentive (e.g., a prize, gift card, gift, etc.) to a cohort member or cohort associated with the incentive. In some cases, providing an incentive in block 612 may include providing a first cohort member and a second cohort member with an individual incentive for i) a coaching proposal to satisfy their threshold sleep performance metric and achieve their goal and / or number of achievement thresholds, ii) a coaching proposal to satisfy their threshold sleep performance metric and achieve their goal and / or number of achievement thresholds, or iii) any combination of i or ii to another cohort member.

[0193] In some cases, in any block 614, parameters of a therapeutic device (e.g., a respiratory therapeutic device) may be adjusted based on sensor data from block 602. The therapeutic device can be used by a second cohort member. Thus, parameters of the therapeutic device can be adjusted based on sleep session data related to the sleep sessions of the adjacent therapeutic individual. In some cases, adjusting therapeutic parameters in block 614 may include, though not always, dynamically adjusting parameters during the sleep sessions of the therapeutic user. In some cases, therapeutic parameters are adjusted in block 614 after receiving second sleep performance data in block 606. In some cases, therapeutic parameters are adjusted in block 614 after generating sleep performance metrics in block 608.

[0194] Figure 7 is a flowchart of a process 700 for tracking sleep cohort goals according to several aspects of this disclosure. Process 700 may be performed by the system 100 of Figure 1, or by several examples or components of the system 100 of Figure 1.

[0195] In block 702, target information is received. The target information may include information indicating the target and associations between the target and sleep sessions with the first cohort member, sleep sessions with the second cohort member, cohort sleep sessions, or any combination thereof.

[0196] In some cases, the target achievement date can be included in the goal information. In some cases, the user can select the target achievement date. In some cases, the target achievement date can be automatically determined based on any combination of sleep performance data (e.g., current or historical) from one or more cohort members. The automatically determined target achievement date may be automatically set for the goal, or it may be presented to the cohort members and set when they confirm or select it.

[0197] In some cases, goal information can be received directly from user input. In some cases, goal information can be received based on one or more generated goals. In block 704, one or more proposed goals can be generated. In block 706, a goal selection can be received that indicates one or more of the one or more proposed goals to be used as goals.

[0198] The generation of proposed goals in block 704 may be performed automatically in response to the receipt of responses to one or more prompts (e.g., questionnaires), or in response to the receipt of sleep performance data and / or generated sleep performance metrics. These responses can be used to generate a set of proposed goals.

[0199] In some cases, generating proposed goals may involve identifying factors that influence the cohort members or the cohort's historical sleep performance metrics, and then determining proposed actions (e.g., future instances of the historical sleep performance metrics) that can be taken to improve future sleep performance metrics. Based on the proposed actions, a set of proposed goals can then be generated.

[0200] In some cases, generating proposed goals may involve receiving demographic information about cohort members and then generating one or more proposed goals based on that demographic information. For example, if the received demographic information about cohort members indicates that the cohort members may be suffering from some factor that may affect sleep performance metrics, then one or more proposed goals may be generated based on these factors to improve sleep performance metrics. In some cases, generating one or more proposed goals based on demographic information may involve accessing a database containing proposed goals related to individuals who share demographic information with the cohort members.

[0201] In some cases, generating proposed goals involves receiving therapeutic device usage history information (e.g., respiratory therapeutic device usage history) and generating proposed goals using the received therapeutic device usage history information. Thus, the generated goals can be customized to the therapeutic user's history of therapeutic device usage (determined from therapeutic device usage data, such as component data used to generate myAir® scores as described herein).

[0202] In some cases, generating proposed goals involves receiving subjective feedback related to historical sleep sessions and using that subjective feedback to generate proposed goals. Therefore, the generated goals may be based on the cohort members' own subjective interpretations of their previous sleep durations.

[0203] In block 708, a target state update may be generated. The target state update may include information about the progress of cohort members or the cohort in achieving the goal. Generating a target state update may include evaluating the goal using sleep performance data (e.g., first sleep performance data and / or second sleep performance data from process 600 in Figure 6). In some cases, the goal evaluation may include the use of sensor data. Evaluating the goal using sensor data may include determining sleep quality, determining sleep-related metrics (e.g., sleep performance metrics), or determining other information using sensor data. For example, sensor data indicating the distance between cohort members (or the user devices of the cohort members) may be used to evaluate a goal based on the distance between cohort members while they are sleeping.

[0204] In block 710, a target state update may be output. Outputting a target state update may include sending the state update to another computing device or displaying the target state update on a display (e.g., the display of a user device). Other techniques may be used.

[0205] Figure 8 is a flowchart of a process 800 for generating coaching suggestions for a sleep cohort according to several aspects of the present disclosure. The process 800 can be performed by the system 100 of Figure 1 or its components, or by several examples or components of the system 100 of Figure 1.

[0206] In block 802, coaching suggestions can be identified. Coaching suggestions may be identified to improve the future sleep performance metrics of cohort members or the cohort.

[0207] In some cases, coaching suggestions can be received directly from user input (e.g., user input from other cohort members). In other cases, coaching suggestions can be automatically identified in response to receiving responses (e.g., subjective feedback) to one or more prompts (e.g., questionnaires). These responses can then be used to generate suggestions.

[0208] In some cases, the identification proposed by the instructor may include identifying factors that influence the cohort members or the cohort's historical sleep performance metrics, and then determining proposed actions (e.g., future instances of the historical sleep performance metrics) that can be taken to improve future sleep performance metrics. Coaching recommendations can then be generated based on the proposed actions.

[0209] In block 804, a coaching suggestion can be presented. Presenting a coaching suggestion may include sending the suggestion to another computing device or presenting it on a display (e.g., the display of a user device). Other techniques may be used.

[0210] Figure 9 is a flowchart illustrating a process 900 for generating sounds that simulate a respiratory therapy device, according to several aspects of the present disclosure. Process 900 can be performed by the system 100 of Figure 1 or its components, or by several examples or components of the system 100 of Figure 1.

[0211] In block 902, simulated respiratory therapy device sounds can be generated. Generation of simulated respiratory therapy device sounds may include electronically generating the sounds or accessing a file containing recordings of simulated respiratory therapy device sounds. The simulated respiratory therapy device sounds generated in block 902 may be based on a selected model of the respiratory therapy device, selected accessories (e.g., user interface and / or conduit), and / or selected settings of the respiratory therapy device. In some cases, simulated respiratory therapy device sounds can be generated based on the specifications or actual settings of a future therapy user or a therapy user's respiratory therapy device.

[0212] In block 906, a simulated respiratory therapy device sound may be output. Outputting a simulated respiratory therapy device sound may include playing the simulated respiratory therapy device sound through a speaker. In block 908, the simulated respiratory therapy device sound can be monitored. Monitoring the simulated respiratory therapy device sound may include monitoring the simulated respiratory therapy device sound using a microphone.

[0213] In block 910, the simulated respiratory therapy device sound can be adjusted based on the monitored simulated respiratory therapy device sound. Adjusting the simulated respiratory therapy device sound based on the monitored simulated respiratory therapy device sound may include adjusting the volume or other characteristics of the outputted simulated respiratory therapy device sound. In some cases, the adjustment in block 910 may include applying one or more filters to the outputted simulated respiratory therapy device sound. Adjusting the simulated respiratory therapy device sound may be performed in block 910 to ensure that the monitored simulated respiratory therapy device sound matches the desired or expected respiratory therapy device sound.

[0214] In any block 912, respiratory therapy recommendations can be made. Respiratory therapy recommendations may include recommendations for a specific respiratory therapy device model, a specific conduit model or type, a specific user interface model or type, one or more settings for a respiratory therapy device, or any combination thereof. In some cases, providing respiratory therapy recommendations may include receiving adjustment commands to adjust the volume of a sound simulating a respiratory therapy device. In this case, the respiratory therapy recommendations are based on the adjusted volume of the simulated respiratory therapy device sound.

[0215] In any block 914, sleep performance data of a cohort member or cohort can be monitored. The monitored sleep performance data can be used to determine sleep performance metrics of a cohort member or cohort. In some cases, monitoring sleep performance data in block 914 may include modifying simulated respiratory therapy device sounds using the sleep performance data in an optional block 904. In some cases, monitoring sleep performance data in block 914 may include notifying respiratory therapy recommendations using the sleep performance data in block 912.

[0216] The foregoing description of the implementations, including the illustrated implementations, is provided for illustrative and explanatory purposes only and is not intended to outline or limit the exact implementations disclosed. Many modifications can be made to the disclosed implementations in accordance with the disclosure herein without departing from the spirit or scope of this disclosure, but many of these modifications, changes and uses will be obvious to those skilled in the art. Therefore, the breadth and scope of this disclosure should not be limited by any of the implementations described above.

[0217] While some aspects of this disclosure have been described and illustrated in relation to one or more implementations, equivalent changes and modifications will occur or will be known to those skilled in the art after reading and understanding this specification and the drawings. Furthermore, certain features of one aspect of this disclosure may be disclosed in relation to only one of several implementations, but such features can be combined with one or more other features of other implementations that are desirable and advantageous for any given or particular application.

[0218] One or more elements, aspects, or steps, or any part thereof, from any one or more of the following claims 1 to 87 can be combined with one or more elements, aspects, or steps, or any part thereof, from any one or more of the other claims 1 to 87 to form one or more additional implementations and / or claims of the present disclosure. The following are additional notes to this disclosure. (Additional note 1) The steps include receiving sensor data from one or more sensors related to an individual's sleep session in the environment, The steps include determining first sleep performance data from the aforementioned sensor data, The steps include receiving second sleep performance data related to the sleep sessions of users of respiratory therapy devices in the environment, A step of generating one or more sleep performance metrics using the first sleep performance data and the second sleep performance data, A method comprising the step of presenting one or more sleep performance metrics. (Additional note 2) The method according to Appendix 1, wherein the one or more sleep performance metrics include i) a coordinated sleep performance score, ii) an individual sleep performance score related to the individual, iii) an individual sleep performance score related to the user, iv) a sleep progression chart related to the individual, v) a sleep progression chart related to the user, vi) the user's treatment score, vii) a resonance score, or viiii) any combination of i to vii. (Additional note 3) The method according to Appendix 1 or 2, wherein the individual's sleep session and the user's sleep session overlap in time. (Additional note 4) The method according to any one of the appendices 1 to 3, wherein the first sleep performance data includes sleep stage information or sleep state information, and the second sleep performance data includes respiratory therapy device usage information. (Additional note 5) A step of receiving goal information related to a first user and a second user, wherein the goal information indicates a goal related to i) the individual's sleep session, ii) the user's sleep session, or iii) a combination of i and ii, A step of generating a target state update, comprising: i) evaluating the target information using the first sleep performance data, ii) the second sleep performance data, or iii) a combination of i and ii; The method according to any one of the appendices 1 to 4, further comprising the step of outputting the aforementioned target state update. (Additional note 6) The step of receiving target information is: A step of generating a set of one or more proposed objectives, The method described in Appendix 5, comprising the step of accepting the selection of a target selected from the set of proposed targets. (Additional note 7) The step of generating the set of proposed objectives is: The steps include submitting a questionnaire that includes one or more questions, The steps include receiving response information in response to the presentation of the aforementioned questionnaire, The method according to Appendix 6, comprising the step of generating a set of proposed targets using the received response information. (Additional note 8) The step of generating the set of proposed objectives is: Steps to access historical sleep performance data related to historical sleep performance metrics, The steps include identifying one or more factors that influence the historical sleep performance metrics, For each of one or more factors, the step is to determine proposed actions that are estimated to improve future sleep performance metrics, The method according to Appendix 6 or 7, comprising the step of generating a set of proposed objectives using proposed actions for each of one or more factors. (Additional note 9) The step of generating the set of proposed objectives is: The steps include receiving demographic information related to the aforementioned individual or user, The method according to any one of appendices 6 to 8, comprising the step of generating a set of proposed targets using the received demographic information. (Additional note 10) The step of generating the set of proposed objectives is: The steps include receiving historical respiratory therapy device usage information related to the user, The method according to any one of appendices 6 to 9, comprising the step of generating a set of proposed targets using the received historical respiratory therapy device usage information. (Additional note 11) The step of generating the set of proposed objectives is: The steps include receiving subjective feedback related to multiple historical sleep sessions, The method according to any one of appendices 6 to 10, comprising the step of generating a set of proposed objectives using the subjective feedback. (Additional note 12) The method according to any one of appendices 5 to 11, further comprising the step of using the sensor data, wherein the step of evaluating the objective is to evaluate the objective. (Additional note 13) The method according to Appendix 12, wherein the step of evaluating the target using the sensor data includes the step of estimating the distance between the individual and the user using the sensor data. (Additional note 14) The method according to any one of the appendices 5 to 13, wherein the step of receiving the target information includes a step of receiving a target achievement date related to the target, and the step of receiving the target achievement date includes a step of automatically determining the target achievement date using i) the first sleep performance data, ii) the second sleep performance data, iii) the historical sleep performance data, or iv) any combination of i to iii. (Additional note 15) The method according to any one of appendices 5 to 14, wherein the target information includes targets related to the start time of the individual's future sleep sessions and the start time of the user's future sleep sessions. (Additional note 16) The method described in any one of Appendix 5 to 15, including the aforementioned target information, which includes a target relating to the distance between the individual and the user in future sleep sessions. (Additional note 17) The method described in any one of the appendices 5 to 16, including objectives related to the future use of the respiratory therapy device. (Additional note 18) The step of evaluating the target information is a step of determining that the target has been achieved, and the target state update includes a step of indicating that the target has been achieved, and the method is In response to determining that the aforementioned goal has been achieved, one or more proposals will be made based at least partially on i) the achieved goal, ii) the time taken to achieve the achieved goal, iii) sleep performance data, iv) the user's subjective data, or v) any combination of i-iv. The steps to determine the subsequent goals, The method described in any one of the appendices 5 to 17, further comprising the step of presenting one or more proposed subsequent objectives. (Additional note 19) A step of accepting a selection of a successor target indicating one of the successor targets from among the one or more proposed successor targets, in response to the presentation of one or more proposed successor targets, The steps include receiving subsequent target information related to the aforementioned subsequent target, A step of generating a subsequent target state update, which includes a step of evaluating the subsequent target information, The method according to Appendix 18, further comprising the step of outputting the subsequent target state update. (Additional note 20) The step of generating one or more sleep performance metrics includes the step of generating a coordinated sleep performance score, and the step of generating the coordinated sleep performance score includes, A step of generating a first sleep performance score using the first sleep performance data, A step of generating a second sleep performance score using the second sleep performance data, The method according to any one of appendices 1 to 19, comprising the step of generating a coordinated sleep performance score using the first sleep performance score and the second sleep performance score. (Additional note 21) Steps to identify coaching suggestions for improving future sleep performance metrics, The method according to any one of Appendix 1 to 20, further comprising the step of providing the coaching suggestion after the first sleep session. (Additional note 22) The step of identifying the coaching proposal is: The steps include receiving subjective feedback related to multiple historical sleep sessions, The method according to Appendix 21, comprising the step of generating the coaching proposal using the subjective feedback. (Additional note 23) The step of identifying the coaching proposal is: Steps to access historical sleep performance data related to historical sleep performance metrics, The steps include identifying one or more factors that influence the historical sleep performance metrics, For each of one or more factors, the step is to determine proposed actions that are estimated to improve future sleep performance metrics, The method according to Appendix 21 or 22, comprising the step of generating the coaching proposal using the proposed changes for each of one or more factors. (Additional note 24) The method according to any one of the appendices 1 to 23, further comprising the step of providing an incentive based on the first sleep performance data and the second sleep performance data. (Additional note 25) The method according to Appendix 24, wherein the step of providing the incentive further depends on a comparison between the one or more sleep performance metrics and the historical sleep performance metrics. (Additional note 26) The step of generating one or more sleep performance metrics includes the step of generating a coordinated sleep performance score, and the step of generating the coordinated sleep performance score includes, A step of generating a first sleep performance score using the first sleep performance data, A step of generating a second sleep performance score using the second sleep performance data, The step of generating a coordinated sleep performance score using the first sleep performance score and the second sleep performance score is included, The method according to appendix 24 or 25, wherein the incentive is provided if the first sleep performance score exceeds a first threshold and the second sleep performance score exceeds a second threshold. (Additional note 27) The method according to Appendix 26, wherein the step of providing the incentive includes a step of providing a first individual incentive related to the individual and a step of providing a second individual incentive related to the user. (Additional note 28) If the first sleep performance score exceeds the first threshold, the step of providing a first personal incentive related to the individual, The method according to Appendix 26, further comprising the step of providing a second personal incentive related to the user if the second sleep performance score exceeds the second threshold. (Additional note 29) If the first sleep performance score exceeds the first threshold, the step of providing a first personal incentive related to the user, The method according to Appendix 26, further comprising the step of providing a second personal incentive related to the individual if the second sleep performance score exceeds the second threshold. (Additional note 30) The method according to any one of Appendix 1 to 29, further comprising the step of transmitting summary information based on the first sleep performance data, wherein the summary information, when received by a user device associated with the user, is available to generate an entry in a feed of historical summary information associated with the individual. (Additional note 31) The method according to Appendix 30, further comprising the step of receiving responsive feedback in response to the generation of an entry. (Additional note 32) The method according to appendix 30 or 31, wherein the step of generating one or more sleep performance metrics includes the step of generating a first sleep performance score using the first sleep performance data, and the summary information includes the first sleep performance score. (Additional note 33) A step of receiving summary information on a user device associated with the said individual, wherein the summary information is based on the second sleep performance data, The method according to any one of appendices 1 to 32, further comprising the step of generating an entry in a feed of historical summary information related to the user using the summary information received. (Additional note 34) The step of generating one or more sleep performance metrics is the second sleep The method according to Appendix 33, comprising the step of generating a second sleep performance score using performance data, wherein the summary information includes the second sleep performance score. (Additional note 35) The method according to any one of Appendix 1 to 34, wherein the second sleep performance data is determined using the sensor data, and the sensor data is further related to the user's sleep session in the environment. (Additional note 36) The method described in any one of Appendix 1 to 35, wherein the second sleep performance data is determined using second sensor data from one or more sensors in a second set, and the second sensor data is related to the user's sleep session in the environment. (Additional note 37) A step of using a respiratory therapy device to receive data related to the air supplied to a user interface worn by a user in an environment participating in a sleep session, The steps include receiving sleep session data related to the sleep sessions of an individual in an environment different from the aforementioned user, A method comprising the step of determining the adjustment of parameters of the respiratory therapy device in response to the received sleep session data. (Additional note 38) The adjustment of the parameters is dynamically determined between the user's sleep session and the individual's sleep session, as described in Appendix 37. (Additional note 39) The method according to Appendix 37 or 38, wherein the sleep session data includes the individual's sleep stage data, and the step of determining the adjustment of the parameters of the respiratory therapy device is based on the sleep stage data. (Additional note 40) The aforementioned step of determining the adjustment of parameters of a respiratory therapy device is: If the sleep session data indicates that the individual is awake, the step is to determine the first setting of the parameter. The method according to any one of appendices 37 to 39, comprising the step of determining a second setting of the parameter if the sleep session data indicates that the individual is asleep, wherein the respiratory therapy device is quieter when using the first setting of the parameter than when using the second setting of the parameter. (Additional note 41) The steps include receiving first sensor data related to the user's sleep session, A step of receiving second sensor data related to the individual's sleep session, wherein the sleep session data related to the second sleep session is determined using the second sensor data. The method according to any one of the appendices 37 to 40, further comprising the step of synchronizing the first sensor data with the second sensor data. (Additional note 42) The method according to appendix 41, further comprising the step of improving the signal-to-noise ratio of the signal of the first sensor data using the synchronized second sensor data. (Additional note 43) The steps include detecting possible events using the first sensor data, The method according to appendix 41 or 42, further comprising the step of confirming the event using the synchronized second sensor data. (Additional note 44) The procedure further includes the step of estimating the user's posture using the synchronized first sensor data and the synchronized second sensor data, as described in any one of the appendices 41 to 43. Method of loading. (Additional note 45) A step of establishing a wireless connection with a user device associated with the said individual, wherein the sleep session data is received using the wireless connection, The steps include measuring the characteristics of the wireless connection, The method according to any one of appendices 37 to 44, further comprising the step of determining the location information of the individual based on the measured characteristics of the wireless connection. (Additional note 46) The method according to Appendix 45, further comprising the step of determining the adjustment of the parameters of the respiratory therapy device based on the positional information. (Additional note 47) The wireless connection is a Bluetooth connection, as described in Appendix 45. (Additional note 48) The aforementioned environment is a building, as described in any one of the appendices 1 to 47. (Additional note 49) The environment is a pair of adjacent rooms, as described in any one of Appendix 1 to 47. (Additional note 50) The environment is a room, as described in any one of the appendices 1 to 47. (Additional note 51) The aforementioned environment is related to sleep, as described in any one of the appendices 1 to 47. (Additional note 52) A respiratory therapy device for supplying air, A user interface fluidically coupled to the respiratory therapy device to guide the supplied air to the user, A control system including one or more processors, A memory coupled to the control system, which, when executed by one or more processors, Receiving data related to a respiratory therapy device that supplies air to the user interface while a user in an environment participating in a sleep session is wearing it, Receiving sleep session data related to an individual's sleep session in an environment different from the user's, A system including a memory containing instructions that cause one or more processors to perform an operation including determining to adjust the parameters of the respiratory therapy device in response to the received sleep session data. (Additional note 53) The adjustment of the aforementioned parameters is dynamically determined between the user's sleep session and the individual's sleep session, as described in Appendix 52. (Additional note 54) The system according to appendix 52 or 53, wherein the sleep session data includes the individual's sleep stage data, and the step of determining the adjustment of the parameters of the respiratory therapy device is based on the sleep stage data. (Additional note 55) The step of determining the adjustment of the parameters of the respiratory therapy device is: If the aforementioned sleep session data indicates that the individual is awake, then the first setting of the parameters is determined. The system according to any one of the appendices 52 to 54, comprising the step of determining a second setting of the parameter if the sleep session data indicates that the individual is asleep, wherein the respiratory therapy device is quieter when using the first setting of the parameter than when using the second setting of the parameter. (Additional note 56) The aforementioned operation is, Receiving first sensor data related to the user's sleep session, Receiving second sensor data related to the individual's sleep session, wherein the sleep session data related to the second sleep session is determined using the second sensor data. The system according to any one of appendices 52 to 55, further comprising synchronizing the first sensor data with the second sensor data. (Additional note 57) The system according to Appendix 56, further comprising using the synchronized second sensor data to improve the signal-to-noise ratio of the first sensor data signal. (Additional note 58) The aforementioned operation is, To detect possible events using the first sensor data, The system according to appendix 56 or 57, further comprising confirming the event using the synchronized second sensor data. (Additional note 59) The system according to any one of appendices 56 to 58, further comprising estimating the user's posture using the synchronized first sensor data and the synchronized second sensor data. (Additional note 60) The aforementioned operation is, Establishing a wireless connection with a user device associated with the aforementioned individual, wherein the reception of the sleep session data is performed using the wireless connection. To measure the characteristics of the aforementioned wireless connection, The system according to any one of appendices 52 to 59, further comprising determining the location information of the individual based on the measured characteristics of the wireless connection. (Additional note 61) The system according to Appendix 60, wherein the operation further includes determining the adjustment of parameters of the respiratory therapy device based on the position information. (Additional note 62) The aforementioned wireless connection is a Bluetooth connection, as described in Appendix 60 of the system. (Additional note 63) The aforementioned environment is a building, or a system as described in any one of the appendices 52 to 62. (Additional note 64) The aforementioned environment is a pair of adjacent rooms, as described in any one of the appendices 52 to 62. (Additional note 65) The aforementioned environment is a room, and the system is as described in any one of the appendices 52 to 62. (Additional note 66) The aforementioned environment is related to sleep, and is a system described in any one of the appendices 52 to 62. (Additional note 67) A step to generate a simulated respiratory therapy device sound, The steps include outputting the sound of the simulated respiratory therapy device, The steps include monitoring the outputted simulated respiratory therapy device sound using a microphone, and A method comprising the step of adjusting the output of the simulated respiratory therapy device sound based on the monitored output of the simulated respiratory therapy device sound. (Additional note 68) The step further includes accessing a set of predetermined respiratory therapy settings, wherein the step of generating the simulated respiratory therapy device sounds is based on the set of predetermined respiratory therapy settings, Appendix 6 The method described in 7. (Additional note 69) The method according to appendix 67 or 68, further comprising the step of accessing a set of treatment settings for the respiratory therapy device, wherein the step of generating the simulated respiratory therapy device sound is based on the set of treatment settings for the respiratory therapy device. (Additional note 70) Steps to receive adjustment commands, The steps include adjusting the volume of the simulated respiratory therapy device in response to receiving the adjustment command, The method according to any one of the appendices 67 to 69, further comprising the step of providing a respiratory therapy recommendation based on the adjusted volume of the simulated respiratory therapy device. (Additional note 71) The respiratory therapy recommendation is as described in Appendix 70, including i) respiratory therapy device model, ii) user interface type, iii) user interface model, iv) conduit type, v) conduit model, or vi) any combination of i to v. (Additional note 72) A step of receiving sensor data from one or more sensors related to a user participating in a sleep session, wherein the simulated respiratory therapy device sound is output during the sleep session. A step of determining the sleep performance information using the sensor data, The method according to any one of the appendices 67 to 71, further comprising the step of outputting the aforementioned sleep performance information. (Additional note 73) The aforementioned sensor data relates to a user in the environment participating in a sleep session, and the method is The step of receiving additional sleep performance information related to an individual's sleep session in an environment different from the aforementioned user, wherein the simulated respiratory therapy device sound is output during the individual's sleep session. The method according to Appendix 72, further comprising the step of outputting the aforementioned additional sleep performance information. (Additional note 74) The step of receiving the aforementioned additional sleep performance information is: The steps include receiving additional sensor data related to the individual's sleep session from one or more sensors, The method according to Appendix 73, comprising the step of determining the additional sleep performance information using the additional sensor data. (Additional note 75) The step of receiving the aforementioned additional sleep performance information is: The steps include receiving additional sensor data related to the individual's sleep session from one or more additional sensors different from the one or more sensors mentioned above, The method according to Appendix 73, comprising the step of determining the additional sleep performance information using the additional sensor data. (Additional note 76) The method according to any one of appendices 73 to 75, further comprising the step of modifying the output of the simulated respiratory therapy device sound during the sleep session based at least in part on the determined sleep performance information and the received additional sleep performance information. (Additional note 77) The method according to any one of appendices 72 to 75, further comprising the step of modifying the output of the simulated respiratory therapy device sound during the sleep session. (Additional note 78) The method according to Appendix 77, wherein the step of modifying the output of the simulated respiratory therapy device sound is based on the determined sleep performance information. (Additional note 79) The generation of the simulated respiratory therapy device sound is related to the first respiratory therapy device model, and the method is A step of acquiring historical sleep performance information related to the historical sleep session, wherein the historical sleep session occurs during the output of additional simulated respiratory device sounds related to the second respiratory therapy device model, The method according to any one of appendices 72 to 78, further comprising the step of generating a comparison between the aforementioned sleep performance information and the aforementioned historical sleep performance information. (Additional note 80) The method according to Appendix 79, further comprising the step of generating recommendations for the first respiratory therapy device model or the second respiratory therapy device model based on the comparison generated. (Additional note 81) Steps to receive medical information related to the individual, The method according to any one of the appendices 67 to 80, further comprising the step of modifying the output of the simulated respiratory therapy device sound based on the medical information received. (Additional note 82) Memory that stores machine-readable instructions, Includes a memory for storing machine-readable instructions, A system in which the control system is coupled to the memory, and when a machine-executable instruction in the memory is executed by at least one of the one or more processors of the control system, the method described in any one of the appendices 1 to 51 or 67 to 81 is performed. (Additional note 83) A system for shared sleep scoring, comprising a control system configured to perform the method described in any one of the appendices 1 to 36 or 48 to 51. (Additional note 84) A system for controlling respiratory therapy, comprising a control system configured to perform the method described in any one of the appendices 37 to 51. (Additional note 85) A system for simulating respiratory therapy, comprising a control system configured to perform the method described in any one of the appendices 67 to 81. (Additional note 86) A computer program product that, when executed by a computer, includes instructions that cause the computer to perform any of the actions described in any one of the appendices 1 to 51 or 67 to 81. (Additional note 87) A computer program product as described in Appendix 86, which is a non-temporary computer-readable medium.

Claims

1. A control system including one or more processors from a first user device associated with an individual receives sensor data related to an individual's sleep session from one or more sensors, The control system provides the individual with a first sleep performance data, which includes sleep stage information or sleep state information, from the sensor data. The control system receives second sleep performance data relating to the sleep sessions of users of respiratory therapy devices placed in the environment, which includes respiratory therapy device usage information indicating one or more time intervals in which the respiratory therapy device is active. The control system includes the steps of estimating the distance between the first user device and a second user device associated with the user of the respiratory therapy device, The control system determines, at least partially, that the estimated distance satisfies a threshold corresponding to the same environment, that the individual and the user are in the same environment for at least a portion of the user's sleep session. The control system determines that the individual and the user are in the same environment, and generates one or more sleep performance metrics using the first sleep performance data and the second sleep performance data, based at least partially on the time interval during which the respiratory therapy device usage information indicates that the respiratory therapy device is operating. A method comprising the step of presenting one or more sleep performance metrics using the control system.

2. The method according to claim 1, wherein the individual's sleep session and the user's sleep session overlap in time.

3. The control system receives target information relating to the first user and the second user, wherein the target information includes i) the individual's sleep session, and ii) the user's A step indicating the goals related to the sleep session, or the combination of i and ii, The control system provides a step of generating a target state update, which includes i) evaluating the target information using the first sleep performance data, ii) the second sleep performance data, or iii) a combination of i and ii, The method according to claim 1 or 2, further comprising the step of outputting the aforementioned target state update.

4. The step of receiving target information is: A step of generating a set of one or more proposed objectives, The step includes accepting the selection of a target from the aforementioned set of proposed targets, The step of generating one or more sets of proposed objectives is: (i) The steps of submitting a questionnaire containing one or more questions, receiving response information in response to the presentation of the questionnaire, and generating the set of proposed objectives using the received response information; (ii) Accessing historical sleep performance data related to historical sleep performance metrics, identifying one or more factors that influence the historical sleep performance metrics, determining proposed actions for each of the one or more factors that are estimated to improve future sleep performance metrics, and generating a set of proposed goals using the proposed actions for each of the one or more factors; (iii) The step of receiving demographic information relating to the individual or user, and using the received demographic information to generate the set of proposed targets; (iv) Receiving historical respiratory therapy device usage information related to the user, and using the received historical respiratory therapy device usage information to generate the proposed set of objectives; (v) receiving subjective feedback related to multiple historical sleep sessions and using said subjective feedback to generate the set of proposed goals; or The method according to claim 3, comprising (vi) any combination of steps (i) to (v).

5. The method according to claim 3 or 4, wherein the step of evaluating the target further includes the step of using the sensor data.

6. The method according to claim 5, wherein the step of evaluating the target using the sensor data includes the step of estimating the distance between the individual and the user using the sensor data.

7. The method according to any one of claims 3 to 6, wherein the target information includes a target relating to the distance between the individual and the user in a future sleep session.

8. The method according to any one of claims 3 to 7, wherein the target information includes targets related to the future use of the respiratory therapy device.

9. The step of evaluating the target information is a step of determining that the target has been achieved, and the target state update includes a step of indicating that the target has been achieved, and the method is In response to determining that the aforementioned goal has been achieved, the process involves determining one or more proposed successor goals based at least partially on i) the achieved goal, ii) the time taken to achieve the achieved goal, iii) sleep performance data, iv) the user's subjective data, or v) any combination of i to iv. The method according to any one of claims 3 to 8, further comprising the step of presenting one or more proposed subsequent goals.

10. In response to the presentation of one or more proposed successor goals, the step of accepting a selection of a successor goal indicating one of the one or more proposed successor goals, The steps include receiving subsequent target information related to the aforementioned subsequent target, A step of generating a subsequent target state update, which includes a step of evaluating the subsequent target information, The method according to claim 9, further comprising the step of outputting the subsequent target state update.

11. The step of generating one or more sleep performance metrics includes the step of generating a coordinated sleep performance score, and the step of generating the coordinated sleep performance score is A step of generating a first sleep performance score using the first sleep performance data, A step of generating a second sleep performance score using the second sleep performance data, The method according to any one of claims 1 to 10, comprising the step of generating a coordinated sleep performance score using the first sleep performance score and the second sleep performance score.

12. The control system includes the steps of identifying coaching suggestions for improving future sleep performance metrics, The method according to any one of claims 1 to 11, further comprising the step of providing the user with the coaching suggestion after the user's sleep session using the control system.

13. The step of identifying the coaching proposal is: The steps include receiving subjective feedback related to multiple historical sleep sessions, The method according to claim 12, comprising the step of generating the coaching proposal using the subjective feedback.

14. The step of identifying the coaching proposal is: Steps to access historical sleep performance data related to historical sleep performance metrics, The steps include identifying one or more factors that influence the historical sleep performance metrics, For each of one or more factors, the step is to determine proposed actions that are estimated to improve future sleep performance metrics, The method according to claim 12 or 13, comprising the step of generating the coaching proposal using the proposed changes for each of one or more factors.

15. The method according to any one of claims 1 to 14, further comprising the step of providing an incentive based on the first sleep performance data and the second sleep performance data by the control system.

16. The method according to claim 15, wherein the step of providing the incentive further depends on a comparison between the one or more sleep performance metrics and historical sleep performance metrics.

17. The step of generating one or more sleep performance metrics includes the step of generating a coordinated sleep performance score, and the step of generating the coordinated sleep performance score is A step of generating a first sleep performance score using the first sleep performance data, A step of generating a second sleep performance score using the second sleep performance data, The step of generating a coordinated sleep performance score using the first sleep performance score and the second sleep performance score is included, The method according to claim 15 or 16, wherein the incentive is provided if the first sleep performance score exceeds a first threshold and the second sleep performance score exceeds a second threshold.

18. The method according to claim 17, wherein the step of providing the incentive includes the step of providing a first individual incentive related to the individual and the step of providing a second individual incentive related to the user.

19. If the first sleep performance score exceeds the first threshold, the step of providing a first personal incentive related to the individual, The method according to claim 17, further comprising the step of providing a second personal incentive related to the user if the second sleep performance score exceeds the second threshold.

20. If the first sleep performance score exceeds the first threshold, the step of providing a first personal incentive related to the user, The method according to claim 17, further comprising the step of providing a second personal incentive related to the individual if the second sleep performance score exceeds the second threshold.

21. The method according to any one of claims 1 to 20, further comprising the step of transmitting summary information based on the first sleep performance data by the control system, wherein the summary information is available to generate an entry in a feed of historical summary information related to the individual when received by a user device related to the user.

22. The method according to claim 21, further comprising the step of receiving a response-indicating feedback in response to the generation of an entry by the control system.

23. The method according to claim 21 or 22, wherein the step of generating one or more sleep performance metrics includes the step of generating a first sleep performance score using the first sleep performance data, and the summary information includes the first sleep performance score.

24. The control system provides a step of receiving summary information on a user device associated with the individual, wherein the summary information is based on the second sleep performance data. The method according to any one of claims 1 to 23, further comprising the step of using the received summary information to generate an entry in a feed of historical summary information related to the user, using the control system.

25. The method according to claim 24, wherein the step of generating one or more sleep performance metrics includes the step of generating a second sleep performance score using the second sleep performance data, and the summary information includes the second sleep performance score. 。

26. The method according to any one of claims 1 to 25, wherein the second sleep performance data is determined using the sensor data, and the sensor data is further related to the user's sleep session in the environment.

27. The method according to any one of claims 1 to 26, wherein the second sleep performance data is determined using second sensor data from one or more sensors of a second set, and the second sensor data is related to a user's sleep session in the environment.

28. A control system including one or more processors, Includes a memory for storing machine-readable instructions, The control system is coupled to the memory, and when machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system, The steps include receiving sensor data related to an individual's sleep session from one or more sensors from a first user device associated with an individual, For the aforementioned individual, the steps include determining first sleep performance data from the sensor data, which includes sleep stage information or sleep state information; The steps include receiving second sleep performance data relating to the sleep sessions of users of respiratory therapy devices placed in the environment, which include respiratory therapy device usage information indicating one or more time intervals in which the respiratory therapy device is active, A step of estimating the distance between the first user device and a second user device associated with the user of the respiratory therapy device, The steps include determining that the individual and the user are in the same environment for at least a portion of the user's sleep session, based at least partially on the estimated distance meeting a threshold corresponding to the same environment, The steps of generating one or more sleep performance metrics using the first sleep performance data and the second sleep performance data, based at least partially on the determination that the individual and the user are in the same environment, and on the respiratory therapy device usage information indicating that the respiratory therapy device is operating during a time interval, A system in which the steps of presenting one or more sleep performance metrics are performed.

29. When executed by a computer, The steps include receiving sensor data related to an individual's sleep session from one or more sensors from a first user device associated with an individual, For the aforementioned individual, the steps include determining first sleep performance data from the sensor data, which includes sleep stage information or sleep state information; The steps include receiving second sleep performance data relating to the sleep sessions of users of respiratory therapy devices placed in the environment, which include respiratory therapy device usage information indicating one or more time intervals in which the respiratory therapy device is active, A step of estimating the distance between the first user device and a second user device associated with the user of the respiratory therapy device, Based at least partially on the fact that the estimated distance satisfies the threshold corresponding to the same environment, the individual and the user shall, for at least a portion of the user's sleep session, The steps to determine if you are in the same environment, The steps of generating one or more sleep performance metrics using the first sleep performance data and the second sleep performance data, based at least partially on the determination that the individual and the user are in the same environment, and on the respiratory therapy device usage information indicating that the respiratory therapy device is operating during a time interval, A computer program comprising the steps of presenting one or more sleep performance metrics and an instruction to cause a computer to perform the following.

30. A computer-readable recording medium on which the computer program described in Claim 29 is recorded.

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