A gaming system according to some aspects of the invention includes a processor, and a memory device storing a plurality of instructions, when executed by the processor, cause the processor to cause a display, by a display device, of a first shape having a first location and a movable second shape having an initial location. The processor receives, player commands for dimensional change the second shapes, after which the second shape moves towards the first shape. The processor causes temporal dimensional changes of the first shape. At least one part of the second shape fits into at least one part of the first shape, at least in one temporary occurrence where the dimensions of the first shape and the second shape allow said fit. The processor checks whether the second shape reached the first shape and whether, if the second shape reached the first shape location, there exists a fit.

Systems and methods of the present invention provide for: receiving a digital image data; modifying the digital image data to reduce a width of a feature within the digital image data; executing a dimension reduction process on the feature; storing a feature vector comprising: at least one feature for each of the received digital image data, and a correct or incorrect label associated with each feature vector; selecting the feature vector from a data store; training a classification software engine to classify each feature vector according to the label; classifying the image data as correct or incorrect according to a classification software engine; and generating an output labeling a second digital image data as correct or incorrect.

An information processing apparatus (100) according to the present application includes two or more force sensors (110) that each detect a force of two or more different fingers of a user, and a processing unit (122) that executes information processing related to measurement of forces of the two or more different fingers based on detection results detected by each of the two or more force sensors (110).

Methods and systems for assessing a biological movement are described. The method comprises obtaining a sequence of samples of a signal from a sensor; determining a set of initial parameters from the signal, the initial parameters comprising trajectory, speed, acceleration and direction data for the sequence of samples; segmenting the signal into first layer segments based on changes in the direction of the trajectory of the sequence of samples; segmenting the first layer segments into second layer segments based on the acceleration data; determining target points of the second layer segments using parameters from the first layer segments and the second layer segments; segmenting the first layer segments into third layer segments based on changes in the trajectory or the acceleration of the sequence of samples; and determining a set of output parameters based on the segmenting and the target points, the output parameters comprising timing information and command information for the biological movement.

A powered communication system comprises an improved layout of keys configured to treat, mitigate, or delay the onset and reduce the severity of symptoms of carpal tunnel syndrome (CTS) and other pathologies by reducing movement of a user's fingers during typing. A layout of the powered communication system comprises the most-used letters on a home or center row while retaining a plurality of keys in the same placement as the QWERTY keyboard. The powered communication system further comprises customizable function keys to further reduce finger movement and at least one sensor to monitor a user's health while typing.

A system and method comprising several hardware and software components that work together to achieve the goal of performance prediction. The hardware components include neuroimaging collection hardware and a computing system. The neuroimaging hardware obtains the brain signals and sends this first set of data to a computational resource for further analysis. A second sent of data is the task performance scores of the participants. This second set is also set to the computational resource. The inventive system can predict a learning rate of target tasks for an individual from just a few minutes of off-task, resting-state neuroimaging data.

Disclosed is a system for associating a symptom with a medical condition. The system comprises a server arrangement, associated with a user device. The server arrangement is operable to: provide, to user device, a set of user activities; receive, from user device, information relating to selection of a user activity from set of user activities; receive, from user device, user input associated with selected user activity; analyse user input and assign performance score thereto; identify, based on performance score, one or more symptoms; and assign severity score to symptom for associating symptom with medical condition. Disclosed also is method for associating symptom with medical condition and computer program product to execute aforementioned method.

A method for facilitating a Parkinson's Disease (“PD”) assessment of a patient includes capturing first video of a patient performing first test movements while holding the mobile device; capturing second video of the patient performing second test movements while maintaining the mobile device on their person; capturing third video of the patient performing third test movements including standing and walking; capturing one or more IMU readings using an IMU of the mobile device; processing the first video, the second video, and the third video according to (i) a hand landmark model to generate one or more hand biomarkers, (ii) a face landmark model to generate one or more face biomarkers, and (iii) a body landmark model to generate one or more body biomarkers; and determining an assessment score based on a standardized PD assessment by processing the biomarkers.

A method and system of training a machine learning neural network (MLNN) in anatomical degenerative conditions in accordance with anatomical dynamics. The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, a first set of mmWave radar point cloud data representing a first gait characteristic of a subject in motion, comprising an arm swing velocity, receiving, in a second layer, a second set of mmWave radar point cloud data representing a second gait characteristic comprising a measure of dynamic postural stability, the input layers being interconnected with an output layer of the MLNN via an intermediate layer, and training a MLNN classifier in accordance with a classification that increases a correlation between a degenerative condition of the subject as generated at the output layer and the sets of mmWave point cloud data.

Systems and methods for objectively assessing physical performance of a subject and generating programs specifically tailored to correct any physical deficiencies identified during that assessment. Generally, the assessments may produce results in binary format so that the assessments may be conducted objectively. Additionally, the recommended exercise programs may be generated based on the results of those assessments so that the recommended exercise programs target the identified physical deficiencies.