Methods, systems and apparatus for inducing and verifying the level of neural entrainment at any target frequency, through a combination of biofeedback mechanisms, data analysis, and modulation of synergistic combinations of adaptable stimuli.

The present disclosure describes a sleep staging system. The system comprises: one or more sensors configured to generate output signals conveying information related to breathing parameters of subject during a respiratory therapy session; and one or more physical computer processors configured by computer readable instructions to: determine, based on the output signals, one or more breathing features of individual breaths of the subject; determine a distribution of the one or more breathing features over a plurality of time windows, at least one of the time windows having a length of at least 60 seconds; determine sleep states of the subject by mapping the distribution of the breathing features to one or more sleep states using a sleep stage classifier model, the sleep stage classifier model configured to determine the sleep states; and provide feedback indicating the sleep states during the respiratory sleep session.

Techniques for robotic control using profiles are disclosed. Cognitive state data for an individual is obtained. A cognitive state profile for the individual is learned using the cognitive state data that was obtained. Further cognitive state data for the individual is collected. The further cognitive state data is compared with the cognitive state profile. Stimuli are provided by a robot to the individual based on the comparing. The robot can be a smart toy. The cognitive state data can include facial image data for the individual. The further cognitive state data can include audio data for the individual. The audio data can be voice data. The voice data augments the cognitive state data. Cognitive state data for the individual is obtained using another robot. The cognitive state profile is updated based on input from either of the robots.

Techniques for robotic control using profiles are disclosed. Cognitive state data for an individual is obtained. A cognitive state profile for the individual is learned using the cognitive state data that was obtained. Further cognitive state data for the individual is collected. The further cognitive state data is compared with the cognitive state profile. Stimuli are provided by a robot to the individual based on the comparing. The robot can be a smart toy. The cognitive state data can include facial image data for the individual. The further cognitive state data can include audio data for the individual. The audio data can be voice data. The voice data augments the cognitive state data. Cognitive state data for the individual is obtained using another robot. The cognitive state profile is updated based on input from either of the robots.

Systems and methods for generating sound elements in a vehicle are present. In one example, a method comprises selecting a sound element, the sound element corresponding to a natural environment; and broadcasting the sound element via one or more speakers of a vehicle. In this way, a sound environment may be provided to a vehicle user based on the at least one vehicle state.

Ventilation system (having a ventilator and having a diagnostic device, wherein the ventilator comprises a ventilation unit for generating a respiratory gas flow for ventilation and a detection unit (for detecting a ventilation signal characteristic for the respiratory gas flow over time. The diagnostic device comprises a sensor unit for detecting a diagnostic signal over time. The synchronization unit is operationally connected to the detection unit and the sensor unit and is suitable and configured for studying a time curve of the ventilation signal and a time curve of the diagnostic signal respectively for a signal change caused by the same event and bringing the curve of the ventilation signal and the curve of the diagnostic signal into chronological correspondence so that the event occurs simultaneously in both signal curves.

A bed has a mattress. A sensor system is configured to sense at least one physical phenomenon through a sleep session. A controller may include at least one processor and memory, the controller configured to receive, through the sleep session, the sensor data; select, from a plurality of optional algorithms, a selected algorithm based on at least one of the sensor data, user input, and checking a clock, where the optional algorithms include (i) a state-based algorithm and (ii) a schedule-based algorithm; update, through the sleep session, using the selected algorithm, a current sleep state of the sleeper; track the sleep session based on the update of the current sleep state of the sleeper through the sleep session; update, through the sleep session, using the tracking of the sleep session, a target environmental-parameter; and send, through the sleep session, automation instructions to an environmental controller.

A bed has a mattress. A sensor system is configured to sense at least one physical phenomenon through a sleep session. A controller may include at least one processor and memory, the controller configured to receive, through the sleep session, the sensor data; select, from a plurality of optional algorithms, a selected algorithm based on at least one of the sensor data, user input, and checking a clock, where the optional algorithms include (i) a state-based algorithm and (ii) a schedule-based algorithm; update, through the sleep session, using the selected algorithm, a current sleep state of the sleeper; track the sleep session based on the update of the current sleep state of the sleeper through the sleep session; update, through the sleep session, using the tracking of the sleep session, a target environmental-parameter; and send, through the sleep session, automation instructions to an environmental controller.

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.

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.