An assignment system and method determine time-variable risk profiles for separate vehicle systems that are remotely controlled by operators located off-board the vehicle systems. The time-variable risk profiles represent risks to travel of the vehicle systems that change with respect to time. An operator staffing demand for the vehicle systems may be determined based on the time-variable risk profiles. The staffing demand represents how many operators are needed for remotely controlling the vehicle systems at different times and a required qualification of one or more operators. The system and method also assign operators to remotely monitor and/or control the vehicle systems based on the risk profiles and, optionally, the staffing demand. The operator assigned to one or more vehicle systems changes with respect to time while the vehicle systems are moving along routes.

An assignment system and method determine time-variable risk profiles for separate vehicle systems that are remotely controlled by operators located off-board the vehicle systems. The time-variable risk profiles represent risks to travel of the vehicle systems that change with respect to time. An operator staffing demand for the vehicle systems may be determined based on the time-variable risk profiles. The staffing demand represents how many operators are needed for remotely controlling the vehicle systems at different times and a required qualification of one or more operators. The system and method also assign operators to remotely monitor and/or control the vehicle systems based on the risk profiles and, optionally, the staffing demand. The operator assigned to one or more vehicle systems changes with respect to time while the vehicle systems are moving along routes.

A computer-implemented method for identifying tongue movement comprises detecting an electroencephalography (“EEG”) signal from an EEG sensor. The EEG sensor is configured to sense the EEG signal generated by a brain in association with a tongue movement. The method also comprises detecting the EMG signal from the EMG sensor. The EMG sensor is configured to sense the EMG signal generated by cranial nerve stimulation of muscles associated with the tongue movement. The method also includes identifying the tongue movement based on the EEG signal and the EMG signal. The method then includes correlating the tongue movement with one of a plurality of tongue location areas.

A computer-implemented method for identifying tongue movement comprises detecting an electroencephalography (“EEG”) signal from an EEG sensor. The EEG sensor is configured to sense the EEG signal generated by a brain in association with a tongue movement. The method also comprises detecting the EMG signal from the EMG sensor. The EMG sensor is configured to sense the EMG signal generated by cranial nerve stimulation of muscles associated with the tongue movement. The method also includes identifying the tongue movement based on the EEG signal and the EMG signal. The method then includes correlating the tongue movement with one of a plurality of tongue location areas.

Disclosed are a method for controlling a vehicle and an intelligent computing apparatus for controlling a vehicle. The method for controlling a vehicle includes obtaining state information related to a driver in the vehicle, generating information related to concentration on the basis of the state information related to the driver, and outputting information related to drowsiness prevention on the basis of the information related to concentration, whereby it is possible to prevent driver's concentration from being decreased and it is possible to prevent occurrence of an accident due to carelessness such as decreased concentration of the driver during driving in advance by recognizing a state in which the driver's concentration is significantly decreased and providing information related to drowsiness prevention to increase concentration again. One or more of the vehicle, user terminal, and server of the present invention may be associated with an Artificial Intelligence Intelligenfce module, a robot, an Augmented Reality (AR) device, a Virtual Reality (VR) device, a device associated with a 5G service, or the like. Can be.

Provided are tissue-mounted devices and methods for monitoring a thermal transport property (e.g., thermal conductivity, thermal diffusivity, heat capacity) of tissue, such as skin. The devices conformally mount to the tissue and have one or more thermal actuators and a plurality of sensors. The actuator applies heat to the tissue and the sensors to detect a spatio temporal distribution of a physiological tissue parameter or physical property resulting from the heating. This spatio temporal information may be correlated with a rate, velocity and/or direction of blood flow, the presence of a vascular occlusion, circulation changes due to inflammation, hydration level and other physiological parameters.

Provided are tissue-mounted devices and methods for monitoring a thermal transport property (e.g., thermal conductivity, thermal diffusivity, heat capacity) of tissue, such as skin. The devices conformally mount to the tissue and have one or more thermal actuators and a plurality of sensors. The actuator applies heat to the tissue and the sensors to detect a spatio temporal distribution of a physiological tissue parameter or physical property resulting from the heating. This spatio temporal information may be correlated with a rate, velocity and/or direction of blood flow, the presence of a vascular occlusion, circulation changes due to inflammation, hydration level and other physiological parameters.

This application relates to an epilepsy monitoring device. In one aspect, the device includes a first unit having a first body portion arranged on a user's head, a sensor unit that is connected to the first body portion and includes a first sensor for measuring a cranial nerve signal, and a stimulation unit that is connected to the first body portion and applies cranial nerve treatment stimulation to the brain according to a provided brain stimulation signal. The device may also include a second unit electrically connected to the first unit and having a second body portion arranged on a body other than the user's head, a battery unit that is arranged in the second body portion and supplies power to the first unit, and a communication unit for wireless communication with an external device.

This application relates to an epilepsy monitoring device. In one aspect, the device includes a first unit having a first body portion arranged on a user's head, a sensor unit that is connected to the first body portion and includes a first sensor for measuring a cranial nerve signal, and a stimulation unit that is connected to the first body portion and applies cranial nerve treatment stimulation to the brain according to a provided brain stimulation signal. The device may also include a second unit electrically connected to the first unit and having a second body portion arranged on a body other than the user's head, a battery unit that is arranged in the second body portion and supplies power to the first unit, and a communication unit for wireless communication with an external device.

Systems and methods of analyzing user feedback in response to a cognition training program, including training at least one machine learning algorithm with a predefined dataset to predict a training success rate, wherein the pre-defined dataset includes previously received user feedback for users with known characteristics, receiving new user feedback, and determining a prediction of the Database training success rate with the at least one machine learning algorithm based on the received new user feedback.