The present disclose generally relates to systems and methods for active titration of one or more cranial or peripheral nerve stimulators to treat obstructive sleep apnea. The active titration can be accomplished in an automated fashion by a closed-loop process. The closed-loop process can be executed by a computing device that includes a non-transitory memory storing instructions and a processor to execute the instructions to perform operations. The operations can include defining initial parameters for the one or more cranial or peripheral nerve stimulators for a patient; receiving sensor data from sensors associated with the patient based on a stimulation with the one or more cranial or peripheral stimulators programmed according to the initial parameters; and adjusting the initial parameters based on the sensor data.

The present disclose generally relates to systems and methods for active titration of one or more cranial or peripheral nerve stimulators to treat obstructive sleep apnea. The active titration can be accomplished in an automated fashion by a closed-loop process. The closed-loop process can be executed by a computing device that includes a non-transitory memory storing instructions and a processor to execute the instructions to perform operations. The operations can include defining initial parameters for the one or more cranial or peripheral nerve stimulators for a patient; receiving sensor data from sensors associated with the patient based on a stimulation with the one or more cranial or peripheral stimulators programmed according to the initial parameters; and adjusting the initial parameters based on the sensor data.

A system for transportation includes a vehicle configured to have a rider located therein or thereon, and an expert system to produce a recommendation for a configuration of the vehicle, wherein the recommendation includes at least one recommended parameter of configuration for the expert system that controls a parameter selected from the group consisting of a vehicle parameter, a rider experience parameter, and combinations thereof.

A vehicle includes a display disposed to facilitate presenting an augmentation of content in an environment of a rider of the vehicle; a circuit for registering at least one of location and orientation of the vehicle; a machine learning circuit that determines at least one augmentation parameter by processing at least one input relating to at least one of the rider and the vehicle; and a reality augmentation circuit that, responsive to the at least one of the location or the orientation of the vehicle, generates an augmentation element for presenting in the display, the generating based at least in part on the at least one augmentation parameter.

A motorcycle helmet includes a data processor configured to facilitate communication between a rider wearing the helmet and a motorcycle, the motorcycle and the helmet communicating location and orientation of the motorcycle. An augmented reality system with a display is disposed to facilitate presenting an augmentation of content in an environment of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation of the motorcycle At least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider and the motorcycle.

Systems are provided that employ dynamic modeling of heartbeats to process electroencephalography (EEG) signals for the suppression of BCG artifacts. The system may be configured to generate an instantaneous EEG correction for ballistocardiogram (BCG) artifact subtraction, the correction being modeled for a selected latency within a selected cardiac cycle. Cardiac cycles with similar EKG signals at the selected latency to that of the selected cardiac cycle are identified and the EEG signals from these similar cardiac cycles, at the selected latency, are employed to generate a modeled EEG signal that represents the instantaneous contribution from the BCG artifact. Accordingly, the system models BCG artifacts by pooling EEG signals at time instants with similar cardiac dynamics. The resulting modeled EEG signal is taken as the estimated BCG artifact and subtracted from the measured EEG signals to generate artifact-suppressed EEG signals.

Systems are provided that employ dynamic modeling of heartbeats to process electroencephalography (EEG) signals for the suppression of BCG artifacts. The system may be configured to generate an instantaneous EEG correction for ballistocardiogram (BCG) artifact subtraction, the correction being modeled for a selected latency within a selected cardiac cycle. Cardiac cycles with similar EKG signals at the selected latency to that of the selected cardiac cycle are identified and the EEG signals from these similar cardiac cycles, at the selected latency, are employed to generate a modeled EEG signal that represents the instantaneous contribution from the BCG artifact. Accordingly, the system models BCG artifacts by pooling EEG signals at time instants with similar cardiac dynamics. The resulting modeled EEG signal is taken as the estimated BCG artifact and subtracted from the measured EEG signals to generate artifact-suppressed EEG signals.

An operating method of a ketogenic dietary evaluation system includes steps as follows. The electroencephalogram data of a responder group and the electroencephalogram data of a non-responder group are preloaded, in which each electroencephalogram datum includes electroencephalograms of channels. The electroencephalograms of the channels are preprocessed to obtain the preprocessed electroencephalograms of the channels. A connectivity matrix is obtained on a basis of the phase synchronization between each two of the preprocessed electroencephalograms of the channels. The connectivity matrix is sampled and analyzed through different frequency bands and different proportion threshold values to obtain graphical parameters. A predictive model is established on a basis of a reduction rate of a predetermined event of the responder group, a reduction rate of the predetermined event of the non-responder group and the parameters.

An operating method of a ketogenic dietary evaluation system includes steps as follows. The electroencephalogram data of a responder group and the electroencephalogram data of a non-responder group are preloaded, in which each electroencephalogram datum includes electroencephalograms of channels. The electroencephalograms of the channels are preprocessed to obtain the preprocessed electroencephalograms of the channels. A connectivity matrix is obtained on a basis of the phase synchronization between each two of the preprocessed electroencephalograms of the channels. The connectivity matrix is sampled and analyzed through different frequency bands and different proportion threshold values to obtain graphical parameters. A predictive model is established on a basis of a reduction rate of a predetermined event of the responder group, a reduction rate of the predetermined event of the non-responder group and the parameters.

A supply tray for a surgical procedure is selected based on the surgical procedure and patient data retrieved from an electronic health records database. Multiple steps of the surgical procedure are retrieved from the electronic health records database. A message is sent to a first manipulator to move a supply from the supply tray to a staging area for performing a step. A first indication is received from a first sensor that the supply is needed at a present time. A position where the supply is needed in an operating area proximate to the staging area is determined using a second sensor. A second message is sent to a second manipulator to move the supply from the staging area to the position. A second indication is received from a third sensor that the step is complete. A third message is sent to a third manipulator to remove the supply.