International Business Machines Corporation

Erhan Bilal

Gustavo Alejandro Stolovitzky

Stefan Harrer

Jeffrey L Rogers

Jianbin Tang

Stefan Von Cavallar

Bo Wen

Umar Asif

Stefan Renard Maetschke

Tian Hao

Deval Samirbhai Mehta

Chen Wang

Jenna Reinen

A computer implemented method synthesizes electroencephalograph signals. A number of processor units creates a training dataset comprising real electroencephalograph signals, speech signals correlating to the real electroencephalograph signals, and a set of human characteristics for the real electroencephalograph signals. The number of processor units trains a generative adversarial network using the training dataset to create a trained generative adversarial network. The trained generative adversarial network generates synthetic electroencephalograph signals in response to receiving new speech signals.

Electroencephalograph signal generation speech in a generative adversarial network

A method, computer system, and a computer program product are provided for testing functional mobility and memory. Information is about a participant to an artificial intelligence (AI) engine and then instructions are provided to the participant to memorize a random set of steps. The participant is then asked to recall the random set of instruction steps and then to perform the steps memorized. The participant is asked to recall the steps again after the physical performance. The result of the test is analyzed and a final participant assessment is rendered based on the overall performance analysis. The participant is provided instructions and further monitored and recorded for the performance of the test using smart devices, video or similar means.

Remote concurrent assessment of functional mobility and working memory

A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include identifying one or more joints of a user based on collected data and generating one or more 3D representations of the one or more joints of the user. Embodiments may further include anonymizing the one or more 3D representations, classifying one or more actions of the user based on the one or more 3D representations, wherein the classifying outputs an action score, and exporting at least one of the one or more actions and the action score.

Privacy-preserving motion analysis

A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include collecting data corresponding to a user with one or more sensors and identifying one or more joints of the user based on the data. Embodiments may additionally include generating one or more 3D representations of the one or more joints of the user and anonymizing the one or more 3D representations by applying thereto a joint-centering and a random shuffling. Embodiments may further include classifying one or more actions of the user based on analysing the one or more 3D representations, and exporting at least one of the data and the one or more actions.

Disclosed is a novel system, and method to evaluate a prediction of a possibly unknown outcome out of a plurality of predictions of that outcome. The method begins with accessing a particular prediction of an outcome out of a plurality of predictions of that outcome in which the outcome may be unknown. Next, a subsample of the plurality of predictions of the outcome is accessed. The subsample can possibly include the particular prediction. A consensus prediction of the outcome based on the subsample of the plurality of predictions is determined. A proximity of the particular prediction to the consensus prediction is determined Each prediction is ranked out of the plurality of predictions in an order of a closest in proximity to the consensus prediction to a farthest in proximity to the consensus prediction.

Evaluation of predictions in the absence of a known ground truth

Disclosed is a novel system, and method to evaluate a prediction of a possibly unknown outcome out of a plurality of predictions of that outcome. The method begins with accessing a particular prediction of an outcome out of a plurality of predictions of that outcome in which the outcome may be unknown. Next, a subsample of the plurality of predictions of the outcome is accessed. The subsample can possibly include the particular prediction. A consensus prediction of the outcome based on the subsample of the plurality of predictions is determined. A proximity of the particular prediction to the consensus prediction is determined. Each prediction is ranked out of the plurality of predictions in an order of a closest in proximity to the consensus prediction to a farthest in proximity to the consensus prediction.

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Astoria, NY, United States of America

Erhan Bilal