The invention relates to an immersive device (1), characterised in that it comprises: an attachment element (10) for attaching the device to the head of a user (2), a system for broadcasting video sequences, and an observation system for capturing, broadcasting and/or recording the reactions of the user (2).

A method of diagnosing a neurodegenerative disorder (ND) in a patient comprising: (a) obtaining MRI image(s) of the patient's brain, (b) using the MRI image(s) of the patient's brain to segment sub-cortical structures associated with the ND into sub-regions, based on structural connectivity to cortical sub-regions, (c) extracting one or more MRI features from each of the sub-regions generated by the segmentation, and (d) using one or more machine learning techniques to classify the patient as being ND positive or ND negative based on comparisons of the one or more MRI features to at least one training data set that includes MRI features of each of the sub-regions generated by the segmentation of known ND positive controls and MRI features of each of the sub-regions generated by the segmentation of ND negative controls, thereby diagnosing ND. Also computer-based or cloud-based systems to diagnose a ND in a subject.

In some examples, a system may include a plurality of electrodes, electrical stimulation circuitry, and a controller. The controller may be configured to select one or more parameters of therapy to be delivered to a brain of a patient and to control the electrical stimulation circuitry to deliver the therapy to the brain of the patient based on the selected parameters and via a first one or more electrodes of the plurality of electrodes. The parameters may be defined based on a first plurality of electrical signals sensed at a plurality of different positions within the brain of the patient when electrical stimulation is not delivered at each of the positions and a second plurality of electrical signals sensed at each of the plurality of different positions within the brain of the patient in response to electrical stimulation delivered at each of the positions at a plurality of different intensities.

A method for predicting clinical severity of a neurological disorder includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image of a brain, brain image regions each of which contains a respective portion of diffusion index values of a diffusion index, which results from image processing performed on the MRI image; b) for one of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) calculating a severity score that represents the clinical severity of the neurological disorder of the brain based on the characteristic parameter of the one of the brain image regions via a prediction model associated with the neurological disorder.

Example systems, methods, and apparatus, including cognitive platforms, are provided for applying signal detection metrics in computer-implemented adaptive response-deadline procedures to data collected based at least in part on user interaction(s) with computerized tasks and/or interferences. The apparatus can include a response classifier for generating a quantifier of the cognitive abilities of an individual. The apparatus also can be configured to adapt the tasks and/or interferences to enhance the individual's cognitive abilities.

The server is a learning apparatus including a data acquisition unit and a learning unit. The data acquisition unit acquires profile data and a selected assistance level as learning data. The profile data indicates a profile related to a trainee before executing rehabilitation regarding rehabilitation executed using a walking training apparatus as a rehabilitation support system. The selected assistance level is an assistance level selected at the time of executing the rehabilitation. The learning unit learns to determine a recommended assistance level recommended to be selected when the trainee uses the rehabilitation support system based on the learning data. Further, the learning unit generates a trained model that receives the profile data and outputs the recommended assistance level based on the learning.

A method and system for remote diagnosis of a congestive heart failure in humans is presented. The method includes receiving a facial image of a patient, wherein the facial image is retrieved from a data store and captured from the patient; extracting, from the facial image, at least one facial feature indicative of a heart condition; classifying the extracted at least one facial feature using a classifier, wherein the classifier maps a plurality of candidate facial features to a plurality of scores indicating a stage of the heart condition; and determining a positive diagnosis and the stage of the heart condition of the patient based on the plurality of scores.

Many embodiments of the invention include systems and methods for evaluating motion from a video, the method includes identifying a target individual in a set of one or more frames in a video, analyzing the set of frames to determine a set of pose parameters, generating a 3D body mesh based on the pose parameters, identifying joint positions for the target individual in the set of frames based on the generated 3D body mesh, predicting a motion evaluation score based on the identified join positions, providing an output based on the motion evaluation score.

A grip gauge is configured to measure grip force from a single hand of a user. The grip gauge comprises a shell and a force sensor housed within the shell. The force sensor is configured to measure grip forces applied to the shell. The grip gauge also includes a control unit housed within the shell and communicatively connected to the force sensor, and a wireless transmitter communicatively connected to the control unit and configured to transmit measured grip forces to one or more external devices.

Systems for tracking biomarkers in subjects. The biomarker tracking system has a sensory array including an RGB-D camera or RGB camera, a memory, and an electronic processor. The microphone captures voice data, including but not limited to tremor detection data, speech volume and pronunciation data, speech strength data, changes in tonality, hesitance in voice, and changes in speed or verbiage. A stored baseline biomarker model may comprise a voice data profile which may be pre-stored in the memory of a server and include a plurality of benchmarks. This electronic processor is configured to use this pre-stored voice data and compare it to the voice data captured with the microphone. The electronic processor is further configured to determine a set of attributes for the voice data, and generates a speech data deviation model based, at least in part, on the comparison of the speech data to the stored baseline biomarker model.