Abstract: 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.

Abstract: A method and device for predicting Alzheimer's disease based on voice characteristics are provided. The device for predicting Alzheimer's disease according to an embodiment includes: a voice input unit configured to generate a voice sample by recording a voice of a subject; a data input unit configured to receive demographic information of the subject; a voice characteristic extraction unit configured to extract voice characteristics from the generated voice sample; and a prediction model that is pre-trained to predict presence or absence of Alzheimer's disease in the subject, based on the voice characteristics and the demographic information.

Abstract: A device and a method for gaze tracking based on machine learning are proposed. In one aspect, the device includes an input unit configured to input an image including a face, and a feature point detection processor configured to detect first feature points in the image including the face. The device may also include a face direction detection processor configured to detect a direction of the face based on the detected first feature points, and an eye-ball direction detection processor configured to detect an eye-ball direction, which is a feature of an eye-ball, from the detected first feature points. The device may further include a model training processor configured to train a gaze tracking model by using the detected first feature points and the eye-ball direction, and a gaze tracking processor configured to perform gaze tracking by using a trained gaze tracking model.

Abstract: Disclosed are a psychiatric disorder screening method and apparatus based on conversation. The psychiatric disorder screening apparatus outputs stimulation including at least one of a story, a word, a sound, a picture, a motion, a color or a direction, and when receiving a response to the stimulation from a testee, determines the presence or absence of a psychiatric disorder by comparing a correct answer ratio of the response or a voice feature included in the response with correct answer ratios or voice features of a normal group and a disease group.

Abstract: This application relates to a device and a method for voice-based trauma screening using deep learning. The device and method for voice-based trauma screening using deep learning screen for trauma through voices that may be obtained in a non-contact manner without limitations of space or situation. In one aspect, the device includes a memory configured to store at least one program and a processor configured to perform an operation by executing the at least one program. The processor can obtain voice data, pre-process the voice data, convert pre-processed voice data into image data, and input the image data to a deep learning model and obtain a trauma result value as an output value of the deep learning model.

Abstract: Provided are a Parkinson's disease prediction apparatus and a Parkinson's disease prediction method. The Parkinson's disease prediction method is performed by a processor of the Parkinson's disease prediction apparatus and includes extracting a syntactic combination from audio data including a speaker's speech result, verifying accuracy of a Parkinson's disease prediction model by changing conditions for preprocessing the audio data and the syntactic combination, determining a syntactic combination ranked in a high rank as audio data for Parkinson's disease prediction, based on a result of verifying the accuracy of the Parkinson's disease prediction model, and inputting, to the Parkinson's disease prediction model, a speaker's speech result corresponding to the audio data for the Parkinson's disease prediction and obtaining a Parkinson's disease prediction result for the speaker as an output of the Parkinson's disease prediction model.

Abstract: Provided is a method of controlling an improved cognitive function training app, the method including determining whether the cognitive function training app has been initiated by an input from a user to a terminal, optimizing cognitive function training provided by the cognitive function training app to be appropriate for the user, based on a result of the determining of whether the cognitive function training app has been initiated, and information of the user of the terminal, and based on the cognitive function training app being initiated or resumed by an input from the user, providing the optimized cognitive function training through the terminal.

Abstract: 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.