Provided is a device for the implementation of serious games, i.e. for the presentation of digital games, which do not serve the purpose of entertainment, but the mediation of therapeutic content in the form of images, films, colors, sounds, etc., but may well contain such elements, for the treatment of mental disorders, whereby an authentic and credible, but also entertaining learning experience is the focus of interest in order to achieve a therapeutic result.

Provided is a device for the implementation of serious games, i.e. for the presentation of digital games, which do not serve the purpose of entertainment, but the mediation of therapeutic content in the form of images, films, colors, sounds, etc., but may well contain such elements, for the treatment of mental disorders, whereby an authentic and credible, but also entertaining learning experience is the focus of interest in order to achieve a therapeutic result.

Various embodiments are described herein for a controller for controlling the operation of a breathing assistance device that provides breathing assistance to a user. The controller comprises a processor that generates a respiratory index value that is determined during a current monitoring time period to detect a respiratory failure, or predict the respiratory failure when at least one PSG signal is measured. The respiratory index value is compared to a threshold to determine if the control signal needs to be updated to reduce or eliminate respiratory failure that the user is currently experiencing or to prevent a predicted respiratory failure from occurring.

Provided is an artificial intelligence-based noninvasive brain circuit control therapy system for sleep enhancement, the system including a wearable device including a first wearable member and a second wearable member formed to be wearable on a body of a user, a first sensor unit disposed on the first wearable member to detect an electroencephalogram (EEG), a second sensor unit disposed on the second wearable member to detect a biometric signal different from the EEG, and a stimulation means disposed on the first wearable member to stimulate the brain according to a stimulation signal provided thereto; a learning unit configured to machine-learn a criterion for determination of a sleep stage of the user based on a first sensing signal generated by the first sensor unit and a second sensing signal generated by the second sensor unit; and a determination unit configured to determine a current sleep stage of the user based on the criterion for determination, generate a stimulation signal corresponding to a determined sleep stage, and provide the stimulation signal to the stimulation means.

Provided is an artificial intelligence-based noninvasive brain circuit control therapy system for sleep enhancement, the system including a wearable device including a first wearable member and a second wearable member formed to be wearable on a body of a user, a first sensor unit disposed on the first wearable member to detect an electroencephalogram (EEG), a second sensor unit disposed on the second wearable member to detect a biometric signal different from the EEG, and a stimulation means disposed on the first wearable member to stimulate the brain according to a stimulation signal provided thereto; a learning unit configured to machine-learn a criterion for determination of a sleep stage of the user based on a first sensing signal generated by the first sensor unit and a second sensing signal generated by the second sensor unit; and a determination unit configured to determine a current sleep stage of the user based on the criterion for determination, generate a stimulation signal corresponding to a determined sleep stage, and provide the stimulation signal to the stimulation means.

The present disclosure pertains to a system and method for providing sensory stimulation (e.g., tones and/or other sensory stimulation) during sleep. The delivery of the sensory stimulation is timed based on a combination of output from a trained time dependent sleep stage model and output from minimally obtrusive sleep monitoring devices (e.g. actigraphy devices, radar devices, video actigraphy devices, an under mattress sensor, etc.). The present disclosure describes determining whether a user is in deep sleep based on this information and delivering sensory stimulation responsive to the user being in deep sleep. In some embodiments, the system comprises one or more sensory stimulators, one or more hardware processors, and/or other components.

The present disclosure pertains to a system and method for providing sensory stimulation (e.g., tones and/or other sensory stimulation) during sleep. The delivery of the sensory stimulation is timed based on a combination of output from a trained time dependent sleep stage model and output from minimally obtrusive sleep monitoring devices (e.g. actigraphy devices, radar devices, video actigraphy devices, an under mattress sensor, etc.). The present disclosure describes determining whether a user is in deep sleep based on this information and delivering sensory stimulation responsive to the user being in deep sleep. In some embodiments, the system comprises one or more sensory stimulators, one or more hardware processors, and/or other components.

A high flow therapy system for delivering heated and humidified respiratory gas to an airway of a patient, the system including a respiratory gas flow pathway for delivering the respiratory gas to the airway of the patient by way of a non-sealing respiratory interface; wherein flow rate of the pressurized respiratory gas is controlled by a microprocessor.

A method for reducing discomfort when viewing virtual reality (VR) content for use in head mounted displays (HMDs). The method includes accessing a model that identifies a plurality of learned patterns associated with the generation of corresponding baseline VR content that is likely to cause discomfort. The method includes processing a first application to generate data associated with simulated user interactions with first VR content of the first application. The method includes comparing the data to the model to identify a pattern in the data matching at least one of the learned patterns, such that the identified pattern is likely to cause discomfort. The method includes identifying a zone in the first application corresponding to identified pattern. The method includes applying a discomfort reduction filter effect within the zone for purposes of reducing potential discomfort in a user.

A method for reducing discomfort when viewing virtual reality (VR) content for use in head mounted displays (HMDs). The method includes accessing a model that identifies a plurality of learned patterns associated with the generation of corresponding baseline VR content that is likely to cause discomfort. The method includes processing a first application to generate data associated with simulated user interactions with first VR content of the first application. The method includes comparing the data to the model to identify a pattern in the data matching at least one of the learned patterns, such that the identified pattern is likely to cause discomfort. The method includes identifying a zone in the first application corresponding to identified pattern. The method includes applying a discomfort reduction filter effect within the zone for purposes of reducing potential discomfort in a user.