A system for evaluating an operator in a training mission on a training element, including a first processing unit configured to receive and process first data to generate converted data according to an operating frequency; a second processing unit configured to receive and process the converted data and mission support data according to the operating frequency to generate second data; and a third processing unit configured to receive and compare the converted data with the second data according to the operating frequency such to generate, based on said comparison, response data. Response data comprise visualisation data and the evaluation system further includes a graphic process unit configured to receive visualisation data and generate a map of the operating environment wherein the training element operates.

A system for evaluating an operator in a training mission on a training element, including a first processing unit configured to receive and process first data to generate converted data according to an operating frequency; a second processing unit configured to receive and process the converted data and mission support data according to the operating frequency to generate second data; and a third processing unit configured to receive and compare the converted data with the second data according to the operating frequency such to generate, based on said comparison, response data. Response data comprise visualisation data and the evaluation system further includes a graphic process unit configured to receive visualisation data and generate a map of the operating environment wherein the training element operates.

In an embodiment, the disclosed technologies receive flight simulation data from a flight simulator computer, input the flight simulation data into a machine learning time-series classifier to identify a plurality of maneuvers from the flight simulation data, evaluate a plurality of performance metrics associated with the identified maneuvers to evaluate a pilot's proficiency of the plurality of maneuvers. In one aspect of the embodiment, the performance metrics associated with the identified maneuvers are used to evaluate a plurality of competency indicators measuring a pilot's aviation competency.

In an embodiment, the disclosed technologies receive flight simulation data from a flight simulator computer, input the flight simulation data into a machine learning time-series classifier to identify a plurality of maneuvers from the flight simulation data, evaluate a plurality of performance metrics associated with the identified maneuvers to evaluate a pilot's proficiency of the plurality of maneuvers. In one aspect of the embodiment, the performance metrics associated with the identified maneuvers are used to evaluate a plurality of competency indicators measuring a pilot's aviation competency.

Systems and methods for adjusting focal distances of images displayed to a user at a designated eye point of a simulator are provided. An image may be generated for display by a screen, wherein the image is reflected by a mirror to the designated eye point. A simulated distance from the designated eye point to an object in the image may be determined. A focal distance for the image may be determined based on the simulated distance. A simulated size of the object may be determined based on the simulated distance. An adjustor may alter a distance between the screen and the mirror to achieve the focal distance. A size of the object may be adjusted in the image based on the simulated size.

Systems and methods for adjusting focal distances of images displayed to a user at a designated eye point of a simulator are provided. An image may be generated for display by a screen, wherein the image is reflected by a mirror to the designated eye point. A simulated distance from the designated eye point to an object in the image may be determined. A focal distance for the image may be determined based on the simulated distance. A simulated size of the object may be determined based on the simulated distance. An adjustor may alter a distance between the screen and the mirror to achieve the focal distance. A size of the object may be adjusted in the image based on the simulated size.

Debriefing a session from a user in a system. During the session, while the user performs actions on one or more tangible instruments of the system, dynamic data is logged in relation to the system along a session timeline. The dynamic data covers the actions of the user on tangible instrument(s). A graphical user interface depicting a debriefing scene, related to the session, is displayed from a first point of view starting at a first time within the session timeline. The debriefing scene is generated starting at the first time from at least a first image feed. Upon detection of a predetermined event in the dynamic data at a second time along the session timeline, a second point of view different from the first point of view is defined and the debriefing scene is generated therefrom after the second time using at least a second image feed.

Debriefing a session from a user in a system. During the session, while the user performs actions on one or more tangible instruments of the system, dynamic data is logged in relation to the system along a session timeline. The dynamic data covers the actions of the user on tangible instrument(s). A graphical user interface depicting a debriefing scene, related to the session, is displayed from a first point of view starting at a first time within the session timeline. The debriefing scene is generated starting at the first time from at least a first image feed. Upon detection of a predetermined event in the dynamic data at a second time along the session timeline, a second point of view different from the first point of view is defined and the debriefing scene is generated therefrom after the second time using at least a second image feed.

Described is a system for training and assessment. In operation, the system classifies a subject's baseline brain state and behavioral performance. Training goals are assessed to specify tasks the subject is to perform and a desired level of performance. The subject is subjected to neurological stimulation while the subject performs specified tasks. Behavioral data is assessed to determine if the subject has achieved the training goals. If the subject has achieved the training goals, the system stops. Alternatively, if the individual has not achieved the training goals, then neurological data is reviewed to identify activation states and values of the neurological stimulation that resulted in increased performance values from the baseline behavioral performance. The activation states and values of the neurological stimulation are adjusted to match those that resulted in increased performance values. The process is repeated until the subject has achieved the training goals.

Described is a system for training and assessment. In operation, the system classifies a subject's baseline brain state and behavioral performance. Training goals are assessed to specify tasks the subject is to perform and a desired level of performance. The subject is subjected to neurological stimulation while the subject performs specified tasks. Behavioral data is assessed to determine if the subject has achieved the training goals. If the subject has achieved the training goals, the system stops. Alternatively, if the individual has not achieved the training goals, then neurological data is reviewed to identify activation states and values of the neurological stimulation that resulted in increased performance values from the baseline behavioral performance. The activation states and values of the neurological stimulation are adjusted to match those that resulted in increased performance values. The process is repeated until the subject has achieved the training goals.