Abstract: The present disclosure relates to a method for determining a mouth leak status associated with a user of a respiratory device is disclosed. Airflow data associated with the user of the respiratory device is received. The respiratory device is configured to supply pressurized air to an airway of the user during a therapy session. The airflow data includes pressure data. The airflow data associated with the user is analyzed. Based at least in part on the analysis, the mouth leak status associated with the user is determined. The mouth leak status is indicative of whether or not air is leaking from a mouth of the user.

Abstract: A system includes a respiratory therapy device, a sensor, and a control system. The respiratory therapy device supplies pressurized air to an airway of a user during a plurality of sleep sessions. The sensor generates data including current data that is associated with a current sleep session of the plurality of sleep sessions and historical data that is associated with one or more prior sleep sessions of the plurality of sleep sessions. The control system includes one or more processors configured to execute machine-readable instructions to: analyze the historical data to determine a behavior pattern associated with the user; analyze the current data to determine a condition of the user interface in relation to the user; and generate an alarm based at least in part on the behavior pattern associated with the user and the condition of the user interface in relation to the user.

Abstract: A system includes a sensor configured to generate data associated with movements of a resident for a period of time, a memory storing machine-readable instructions, and a control system arranged to provide control signals to one or more electronic devices. The control system also includes one or more processors configured to execute the machine-readable instructions to analyze the generated data associated with the movement of the resident, determine, based at least in part on the analysis, a likelihood for a fall event to occur for the resident within a predetermined amount of time, and responsive to the determination of the likelihood for the fall event satisfying a threshold, cause an operation of the one or more electronic devices to be modified.

Abstract: A system for predicting adoption of a prescribed treatment plan by an individual includes a data repository, a memory storing instruction, and a control system to execute the instructions. The data repository is communicatively coupled to a network and includes a plurality of storage devices storing data. The control system receives at least a portion of the data stored in the data repository. The at least a portion of the data is associated with the individual. The control system uses the machine learning adoption prediction algorithm to process the received at least a portion of the data to determine a likelihood that the individual will adopt the prescribed treatment plan. Based at least in part on (i) the prescribed treatment plan and (ii) the determined likelihood that the individual will adopt the prescribed treatment plan, the control system generates a personalized treatment adoption plan for the individual.

Abstract: A method includes receiving, from a first sensor, first physiological data associated with a first sleep session of a user. The method also includes receiving, from a sensor, second physiological data associated with a first sleep session of a user. The method also includes determining a first set of sleep-related parameters associated with the first sleep session of the user based at least in part on the first physiological data. The method also includes determining a second set of sleep-related parameters associated with the first sleep session of the user based at least in part on the second physiological data. The method also includes calibrating the second sensor based at least in part on a comparison between the first set of sleep-related parameters and the second set of sleep-related parameters.