Aspects of the subject disclosure may include, for example, receiving information about a task to be completed by a user, receiving information about the user and receiving information about a physical environment of the user. The subject disclosure may further include creating one or more immersion objects based on the information about the task, the information about the user and the information about the physical environment, creating an immersive environment including the one or more immersive objects and at least a portion of the physical environment of the user, and communicating to an extended reality (XR) device of the user information about the immersive environment to create an immersive experience for completion of the task by the user. Other embodiments are disclosed.

Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.

The present disclosure relates to systems and methods for characterizing a behavior change of a process. A behavior model that can include a set of behavior parameters can be generated based on behavior data characterizing a prior behavior change of a process. A stimulus parameter for a performance test can be determined based on the set of behavior parameters. An application of the performance test to the process can be controlled based on the stimulus parameter to provide a measure of behavior change of the process. Response data characterizing one or more responses associated with the process during the performance test can be received. The set of behavior parameters can be updated based on the response data to update the behavior model characterizing the behavior change of the process. In some examples, the behavior model can be evaluated to improve or affect a future behavior performance of the process.

The present disclosure relates to systems and methods for characterizing a behavior change of a process. A behavior model that can include a set of behavior parameters can be generated based on behavior data characterizing a prior behavior change of a process. A stimulus parameter for a performance test can be determined based on the set of behavior parameters. An application of the performance test to the process can be controlled based on the stimulus parameter to provide a measure of behavior change of the process. Response data characterizing one or more responses associated with the process during the performance test can be received. The set of behavior parameters can be updated based on the response data to update the behavior model characterizing the behavior change of the process. In some examples, the behavior model can be evaluated to improve or affect a future behavior performance of the process.

Methods, systems and wearable devices for real-time mental stress assessment are provided. The methods and systems employ deep learning using a Gated Recurrent Unit (GRU) gating mechanism in a recurrent neural network with a sliding window approach applied to raw EEG data.

The present invention relates to a cognitive function test server, including a communication interface, a memory; and a processor which is operably connected to the communication interface and the memory, and the processor is configured to provide a first sequence to acquire brainwave data of a user in a resting state by means of an HMD device, acquire baseline brainwave data of the user based on the first sequence, provide at least one second sequence related to a cognitive function by means of the HMD device, acquire input data and activated brainwave data based on the second sequence from the HMD device and an input device connected thereto, and generate a cognitive evaluation result of the user based on at least one of the reference brainwave data, the activated brainwave data, and the input data of the user.

Described herein are assistant robots that anticipate needs of one or more people (or animals). The assistant robots may recognize a current activity, knowledge of the person's routines, and contextual information. As such, the assistant robots can provide or offer to provide appropriate robotic assistance. The assistant robots can learn users' habits or be provided with knowledge regarding humans in its environment. The assistant robots develop a schedule and contextual understanding of the persons' behavior and needs. The assistant robots may interact, understand, and communicate with people before, during, or after providing assistance. The robot can combine gesture, clothing, emotional aspect, time, pose recognition, action recognition, and other observational data to understand people's medical condition, current activity, and future intended activities and intents.

The driving support apparatus includes a memory configured to store information representing a degree of familiarity with an environment for a driver of a vehicle; and a processor configured to detect an object existing around the vehicle based on a sensor signal representing a situation around the vehicle obtained by a sensor mounted on the vehicle, determine whether or not the object approaches the vehicle so that the object may collide with the vehicle, and notify the driver of the approach via a notification device mounted on the vehicle at a timing corresponding to the degree of familiarity with the environment for the driver of the vehicle, when it is determined that the object approaches the vehicle so that the object may collide with the vehicle.

To provide an electronic device capable of recognizing a user's emotion with a high accuracy. The electronic device includes a detection device, an arithmetic device, and a housing. The housing includes a space at a position overlapping with a user's nose when the user wears the electronic device. The detection device is located between the housing and the user's nose. The detection device has a function of obtaining user's data on an emotion of the user and outputting the user's data to the arithmetic device. The arithmetic device has a function of generating display data based on the user's data and outputting the display data.

A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.