A computer-implemented cognitive assessment tool is provided for assessing cognitive ability of an individual while multi-tasking. In one embodiment, a computer processing system on which the tool is implemented may receive form the individual first responses to a first task and second responses to a second task, where the first task and the second task are presented to the individual simultaneously. The system may determine that the first task and the second task are performed by the individual based on the first responses and the second responses, and compute a cognitive measure using one or both of the first responses and the second responses. Further, computing the cognitive measure may be based on performance measures of one or both of the first responses and the second responses. Based on the cognitive measure, the system may output a cognitive assessment to the individual.

A computer-implemented cognitive assessment tool is provided for assessing cognitive ability of an individual while multi-tasking. In one embodiment, a computer processing system on which the tool is implemented may receive form the individual first responses to a first task and second responses to a second task, where the first task and the second task are presented to the individual simultaneously. The system may determine that the first task and the second task are performed by the individual based on the first responses and the second responses, and compute a cognitive measure using one or both of the first responses and the second responses. Further, computing the cognitive measure may be based on performance measures of one or both of the first responses and the second responses. Based on the cognitive measure, the system may output a cognitive assessment to the individual.

This application relates to a method of constructing a training program for improving symptoms of a mild cognitive impairment patient. In one aspect, the method includes arranging, on designated days, direct training algorithms regarding at least one of visualization, fusion, or semantic word fluency directly related to a cognitive function area and controlling the arranged direct training algorithms to be output to a user terminal according to the designated days, and receiving a result value from the user terminal. The method may also include calculating an achievement level for each direct training algorithm, based on the result value, and determining one of the direct training algorithms based on the calculated achievement level. The method may further include rearranging the direct training algorithms, together with the determined direct training algorithm based on both the calculated achievement level and a training algorithm matched to the determined direct training algorithm.

Some implementations provide input-triggered user verification. This may involve trigging a user verification (e.g., capture of an image, sound, fingerprint, etc.) to verify a user's identity based on input (e.g., typing) received ad the device. Triggering the user-verification based on receiving input may help ensure that the image, sound, fingerprint, etc. is captured at a time when the user is close to the device, touching the finger-print sensor, and/or in view of the camera during the capturing. Some implementations provide user verification based on a user-identification of a previously selected image. This may involve using an inmate-selected picture or other image to recover a forgotten alphanumeric reference. Some implementations of the invention disclosed herein provide user verification based on a computer-vision identification of a wearable identification tag. This may involve using an image of the user's identification tag worn on the user's wrist to verify the user's identity.

Some implementations provide input-triggered user verification. This may involve trigging a user verification (e.g., capture of an image, sound, fingerprint, etc.) to verify a user's identity based on input (e.g., typing) received ad the device. Triggering the user-verification based on receiving input may help ensure that the image, sound, fingerprint, etc. is captured at a time when the user is close to the device, touching the finger-print sensor, and/or in view of the camera during the capturing. Some implementations provide user verification based on a user-identification of a previously selected image. This may involve using an inmate-selected picture or other image to recover a forgotten alphanumeric reference. Some implementations of the invention disclosed herein provide user verification based on a computer-vision identification of a wearable identification tag. This may involve using an image of the user's identification tag worn on the user's wrist to verify the user's identity.

A system 80 for personalized e-leaming includes a Hierarchical Knowledge Tracing (HKT) model unit 81 which includes two sequential models comprising of a lower level model and a higher level model, wherein the lower level model estimates and updates the estimate of knowledge state of a learner from a question-response data of the learner while the learner is active (in-session) on the e-leaming application and predicts the probability of answering a question within the domain using the estimate of knowledge state, and the higher level model updates the knowledge state estimate of the lower level model when a new session starts.

A system 80 for personalized e-leaming includes a Hierarchical Knowledge Tracing (HKT) model unit 81 which includes two sequential models comprising of a lower level model and a higher level model, wherein the lower level model estimates and updates the estimate of knowledge state of a learner from a question-response data of the learner while the learner is active (in-session) on the e-leaming application and predicts the probability of answering a question within the domain using the estimate of knowledge state, and the higher level model updates the knowledge state estimate of the lower level model when a new session starts.

An augmented reality system including: a physical anatomic model; a display unit via which a user is adapted to receive first and second optic feedbacks, the first optic feedback emanating from the physical anatomic model and passing through the display unit, and the second optic feedback emanating from the display unit and including a virtual anatomic model; a tracking system adapted to track a position and orientation of the display unit; and a computing device adapted to: receive a first signal from the tracking system relating to the position and orientation of the display unit, and send a second signal to cause the display unit to overlay the second optic feedback on the first optic feedback, the second signal being based on the first signal. In some embodiments, the second optic feedback further includes ancillary virtual graphics such as medical data, instructional steps, expert demonstrations, didactic content, and exigent circumstances.

An augmented reality system including: a physical anatomic model; a display unit via which a user is adapted to receive first and second optic feedbacks, the first optic feedback emanating from the physical anatomic model and passing through the display unit, and the second optic feedback emanating from the display unit and including a virtual anatomic model; a tracking system adapted to track a position and orientation of the display unit; and a computing device adapted to: receive a first signal from the tracking system relating to the position and orientation of the display unit, and send a second signal to cause the display unit to overlay the second optic feedback on the first optic feedback, the second signal being based on the first signal. In some embodiments, the second optic feedback further includes ancillary virtual graphics such as medical data, instructional steps, expert demonstrations, didactic content, and exigent circumstances.

An information processing system comprising a processor programmed to: receive a question asked by a questioner, an answer provided by an answerer to the question, and a rating by a rater with respect to at least one of the question and the answer; manage relationship information, the relationship information being related to the questioner, the answerer, and the rating by the rater; acquire attribute information about each of the questioner, the answerer, and the rater; and present rating information based on the relationship information and in response to a condition specified by a requester with respect to the attribute information.