Provided are a test method and apparatus for evaluating cognitive function decline. The test method for evaluating cognitive function decline includes performing a testing stage of testing how many missions presented for each of a plurality of test items a user performs, and calculating a test score for each of the plurality of test items, performing a category classifying stage of classifying at least one test item from among the plurality of test items as one neurocognitive category from among a plurality of neurocognitive categories, and performing an evaluating stage of evaluating whether there is cognitive function decline in the one neurocognitive category, based on a test score of the at least one test item classified as the one neurocognitive category.

Provided are a test method and apparatus for evaluating cognitive function decline. The test method for evaluating cognitive function decline includes performing a testing stage of testing how many missions presented for each of a plurality of test items a user performs, and calculating a test score for each of the plurality of test items, performing a category classifying stage of classifying at least one test item from among the plurality of test items as one neurocognitive category from among a plurality of neurocognitive categories, and performing an evaluating stage of evaluating whether there is cognitive function decline in the one neurocognitive category, based on a test score of the at least one test item classified as the one neurocognitive category.

An olfactory testing platform for testing for olfactory performance and for prompting olfactory training. The kit contains a plurality of inhaler sticks, each of which includes a first indicia that is computer readable, and a second indicia that is human readable. Each inhaler stick provides a scent that is different than every other inhaler stick in smell or concentration. A computer application running on a smart device having a camera is used to identify each inhaler stick in the kit using the first indicia and to prompt a user to select a detected scent from a plurality of options in response to presentation of the first indicia to the camera of the smart device. The smart devise also captures cognitive functions and integrates performance of various cognitive domains with olfactory performance.

The invention relates to a computer-implemented method for identifying clinically meaningful representations of speech data for monitoring or diagnosis of a health condition, the method comprising: providing a main model comprising a trained neural network, trained to map an input representation encoding input speech data from a speaker to an output representation for use in providing a health condition prediction, the neural network comprising one or more internal network layers each comprising an internal representation which is passed to a subsequent network layer; inputting speech data from a speaker into the main model to form the internal representations of the input speech data; training a probe comprising a machine learning model, independently to the training of the main model, to map an internal representation of the input speech data an internal network layer of the main model to an independently determined measure of a clinically relevant feature of the input speech data or the speaker, where a clinically relevant feature is a property of the input speech or speaker that is impacted by a health condition. By training a probe externally to the main model, to map an internal representation to an independently determined measure of a clinically relevant feature, it is possible to identify associations within the internal representations that otherwise might not be found by the main model and to build improved representations based on these associations.

The present disclosure relates to a method for providing biomarker for early detection of Alzheimer's Disease (AD), and particularly to a method that is able to enhance the accuracy of predicting AD from Mild Cognitive Impairment (MCI) patients using the Hippocampus magnetic resonance imaging (MRI) scans and Mini-Mental State Examination (MMSE) data. The providing MRI images containing the anatomical structure of Hippocampus biomarker and MMSE data as a training data set; training a processor using the training data set, and the training comprising acts of receiving MRI images and MMSE data as a testing data set from a target; and classifying the test data by the trained processor to include aggregating predictions.

A cognitive function evaluation device includes: an instruction unit that instructs quick pronunciation of pseudoword in which a predetermined syllable is repeated; an obtainment unit that obtains voice data indicating a voice of an evaluatee who has received an instruction; a calculation unit that calculates a feature from the voice data obtained by the obtainment unit; an evaluation unit that evaluates a cognitive function of the evaluatee from the feature calculated by the calculation unit; and an output unit that outputs a result of the evaluation by the evaluation unit.

The purpose of the present invention is to diagnose a neurological disease such as dementia by using virtual reality, and the present invention receives a neurological disease-diagnosing problem from an external device, displays a virtual reality image through a display on the basis of the received neurological disease-diagnosing problem, and performs an examination using the virtual reality image according to user actions detected through various sensors. This process can be performed when linked with an external device such as a PC or a smart phone. Particularly, with respect to the neurological disease-diagnosing problem, the present invention includes: a first screen for introducing at least one object in a virtual reality space and hiding the same; and a second screen having a question on the hidden object. According to the present invention, neurological disease such as dementia can be conveniently examined without inconveniencing or burdening a patient.

Systems and methods for screening a subject for neuropathology associated with COVID-19. The method can include providing neurophysiological stimuli to the subject, measuring response by the subject to the neurophysiological stimuli, comparing the measured responses to a reference, determining whether the subject demonstrates abnormal or changed responses to the neurophysiological stimuli according to whether the measured responses differ from the individual's baseline or from the reference by one or more thresholds, and providing an alert according to whether the subject has abnormal or changed responses to the neurophysiological stimuli, wherein the alert comprises an intervention associated with COVID-19.

A brain impact sensor apparatus comprises a sensor attached to a processor/controller that is held close to the top of a user's head. The sensor records vibrations caused by movement of the user. Specifically, as a user walks, runs, or otherwise carries on daily activities, the impacts of the user's gait is recorded in the sensor apparatus held close to the top of the user's head. Thus, the sensor and processor/controller are configured to record repetitive vibrations caused by the user's gait. Analysis of the recorded vibrations may aid in diagnosing disease or may aid in recovery via physical therapy. Methods of using the same are further provided.

Provided is a method for classifying severe cognitive impairment patients by analyzing electroencephalogram (EEG) data. The method of classifying severe cognitive impairment patients by analyzing EEG data includes a brainwave collection step of collecting EEG data on a plurality of users, a first classification step of classifying the plurality of users into a severe cognitive impairment group or a non-severe cognitive impairment group by analyzing the collected EEG data, a second classification step of classifying users included in the non-severe cognitive impairment group into a normal group or an amnestic mild cognitive impairment (aMCI) group, and a third classification group of classifying users included in the normal group into a within normal limits (WNL) group or a preclinical Alzheimer's disease (AD) group and classifying users included in the aMCI group into a non-AD MCI group or a prodromal AD group.