Technologies for providing a cognitive capacity test for autonomous driving include a compute device. The compute device includes circuitry that is configured to display content to a user, prompt a message to the user to turn user's attention to another activity that needs situational awareness, receive a user response, and analyze the user response to determine an accuracy of the user response and a response time, wherein the accuracy and response time are indicative of a cognitive capacity of the user to assume control of an autonomous vehicle when the autonomous vehicle encounters a situation that the vehicle is unable to navigate.

Apparatuses, systems, methods, and computer program products are disclosed for medical assessment based on voice. A query module is configured to audibly question a user from an electronic display screen and/or a speaker of a computing device with one or more open ended questions. A response module is configured to receive a conversational verbal response of a user from a microphone of a computing device in response to one or more open ended questions. A detection module is configured to provide a machine learning assessment for a user of a medical condition based on a machine learning analysis of a received conversational verbal response of the user.

A system for displaying the results of TMT test can acquire time-dependent changes in the position of a contact part on a coordinate plane as time-series coordinate data on the basis of an electric detection signal from a sensor, acquire an elapsed time associated with movement of the contact part as time data from a timer on the basis of the detection signal from the sensor, calculate a test value relating to the movement of the contact part on the basis of the data, and generate and output a characteristic image indicating a time-dependent change in the test value.

An event prediction system according to an aspect includes an acquisition unit and a symptom detection unit. The acquisition unit acquires body movement data about a subject's body movement from a measuring device that outputs the body movement data. The symptom detection unit makes, based on a subset, acquired during a past reference period, of the body movement data, a decision about whether or not there are any symptoms of an onset of a particular event related to the subject.

Examples herein include systems for detecting cheating and changes in mental performance in card games, including bridge. The system can acquire board data for multiple events, the events including bridge games. For each event, determining performance values for a player based on the board and play data, wherein the performance values may additionally be based in part on timing data. The system can detect a deviation of by comparing the performance values against a threshold, wherein the threshold is based on past performance of known cheating players or the individual player. Then the system can alert a user when the likelihood of cheating or other changes exceeds a threshold.

Provided is a technique for supporting a diagnosis in determining disease by using various types of measured values acquired by a medical image acquisition apparatus. An image diagnosis support device includes a measured-value receiving unit configured to receive various types of measured values at a plurality of positions within a living body, a group generator configured to generate groups of the measured values depending on the position or the type of the measured value, an intermediate index calculator configured to calculate an intermediate index from the measured values included in the group on a per-group basis, and a comprehensive index calculator configured to calculate a comprehensive index from values of the intermediate index calculated on a per-group basis. The intermediate index and the comprehensive index are displayed on a display unit in a display mode such as numerical values and in the form of an image.

Applicants have discovered a new low-cost and accessible method and apparatus that allowing for quicker and more precise state determinations based on medical images. The method and apparatus are non-intrusive and require providing a medical image of a subject and determining a state based on a comparison to a training image set comprising two or more states. There is provided, according to the present invention, a computer-implemented method for processing medical images comprising calculating non-local means patch-based weights comparing patches surrounding pixels of interest in a test image with a number of patches of pixels surrounding a corresponding number of pixels in reference images; and calculating for the pixels of interest at least one state estimation using a given state assigned to said reference images and said weights.

Example systems, methods, and apparatus, including cognitive platforms, are provided for computing performance metrics of an individual based at least in part on user interaction(s) with computerized tasks and/or interference and at least one physiological measure of the individual, where the performance metric provides an indication of the cognitive abilities of the individual. The apparatus can be coupled to at least one physiological component to perform the physiological measurement of the individual. The apparatus also can be configured to adapt the tasks and/or interferences to enhance the individual's cognitive abilities.

Systems and methods are described for computing a quantitative index that characterizes Alzheimer's disease (“AD”) risk events based on a temporally ordered sequence of biomarker events. In general, the systems and methods described here implement a modified event-based probabilistic (“EBP”) model to calculate the risk index from biomarker data.

System, methods and electronic devices aimed at motivating users to perform physical activity are disclosed herein. A user may be presented with a series of physical and cognitive exercises. For example, an exercise may be locked until completion of an unlocked exercise. Performance in each exercise may be tracked to display feedback to a user and track the user's improvement over time. Physical exercises may comprise challenges to move a particular distance or take a particular number of steps. Cognitive exercises comprise techniques test or train the cognitive abilities of the user in: attention, memory, processing speed, logical reasoning, numerical reasoning, spatial reasoning, verbal reasoning, language, and cognitive executive functions. Performance in one exercise may modify the difficulty or parameters of a subsequent exercise. The tracking of user improvement may comprise a progress or reward system such as the increasing of a user's “level” or awarding of “badges.”