The present disclosure relates to computing technologies for diagnosis and therapy of language-related disorders. Such technologies enable computer-generated diagnosis and computer-generated therapy delivered over a network to at least one computing device. The diagnosis and therapy are customized for each patient through a comprehensive analysis of the patient's production and reception errors, as obtained from the patient over the network, together with a set of correct responses at each phase of evaluation and therapy.

A method of making an electrode for a wearable electronic includes providing an adhesive print media layer. A surface treatment is performed to a top surface of the print media layer. An elastic conductive ink is deposited onto the print media layer. The elastic conductive ink comprises a conductive particulate disposed in a binder. A diffusion bond is formed between the top surface of the print media layer and the elastic conductive ink. The diffusion bond forming is facilitated by the surface treatment and even after the diffusion bond is formed, the top surface of the diffusion bonded electrode remains conductive.

A dexterity testing apparatus includes a housing and a sensor. The housing is configured to be manipulated with digits of one hand of a user. The sensor is supported by the housing and configured to generate user dexterity data based upon changes in acceleration and orientation of the housing as the housing moves relative to the digits of the one hand of the user. The sensor is configured to convert the user dexterity data into an output signal indicative of the user's dexterity.

Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

A machine learning system (110) for determining at least one analysis model for predicting at least one target variable indicative of a disease status is proposed. The machine learning system (110) comprises: at least one communication interface (114) configured for receiving input data, wherein the input data comprises a set of historical digital biomarker feature data, wherein the set of historical digital biomarker feature data comprises a plurality of measured values indicative of the disease status to be predicted; at least one model unit (116) comprising at least one machine learning model comprising at least one algorithm; at least one processing unit (112), wherein the processing unit (112) is configured for determining at least one training data set and at least one test data set from the input data set, wherein the processing unit (112) is configured for determining the analysis model by training the machine learning model with the training data set, wherein the processing unit (112) is configured for predicting the target variable on the test data set using the determined analysis model, wherein the processing unit (112) is configured for determining performance of the determined analysis model based on the predicted target variable and a true value of the target variable of the test data set.

A method capturing eye movement data for detection of cognitive anomalies, includes a computer application displaying a frame on a display, capturing and displaying a video image of a user's face and eyes, while the user aligns the face to the frame, capturing and processing the face image to initiate an image eye movement capture process, outputting an indication on a display and moving the indication spatially to one of a plurality of images, capturing a video of each user eye, to track the position of the indication of the display, the image of each eye comprising a sclera portion, an iris portion, and a pupil portion, parsing the video to determine a reference images corresponding to eye positions, capturing the user's eyes while the user views familiar and novel images, and correlating the images of the user's eyes to the familiar or novel images using the reference images.

This disclosure relates generally to detection of mild cognitive impairments in subjects. The method and system proposed provides a continuous/seamless monitoring platform for MCI detection in subjects by continuously monitoring routine activities of subjects (Activities of Daily Living (ADL)) in a smart environment using plurality of passive, unobtrusive, binary, unobtrusive non-intrusive sensors embedded in living infrastructure. The proposed method and system detects symptoms of MCI at the onset of the disease, while also addressing issue of sensor failures that causes gaps in the data. The collected sensor data is pre-processed in several stages which includes which includes pre-processing of sensor data, behavior deviation detection, and abnormality detection and so on. Further, the disclosure also proposes an autoencoder based technique, to reduce the dimension of the data to find personalized deviations in behavior of a subject which is used to detect if a subject could be a potential case of MCI.

Disclosed herein are platforms, systems, devices, methods and media for model optimization and data analysis using machine learning. Input data can be processed and analyzed to identify relevant discriminating features, which can be modeled using a plurality of machine learning models. A computing device can be configured with one or more optimized models for categorizing input data.

Embodiments of the present disclosure relate systems and methods for detecting cognitive decline of a subject using passively obtained data from at least one mobile device. In an exemplary embodiment, a computer-implemented method comprises receiving passively obtained data from at least one mobile device. The method further comprises generating digital biomarker data from the passively obtained data. The method further comprises analyzing the digital biomarker data to determine whether the subject is exhibiting signs of cognitive decline.

A system and a method measure volumetric changes of brain structures. The method includes initializing an intensity value of all voxels of a 3D voxel dataset representing the brain of a subject to an initial value preferentially equal to 0. For all voxels that belong to a segmented brain structure for which reference data of a longitudinal reference model exists, automatically executing the following steps: calculating a deviation of a volume change for the segmented brain structure from the longitudinal reference model, normalizing the deviation to obtain a quantitative value of the volume change on a same scale for voxel's belonging to different brain structures; and setting the intensity value of the voxels to the previously obtained quantitative value Q. The voxels of the 3D voxel dataset are displayed in a form of a longitudinal deviation map.