AIMC Topic: Task Performance and Analysis

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A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory.

Medical image analysis
In this study, a deep neural network (DNN) is proposed to reduce the noise in task-based fMRI data without explicitly modeling noise. The DNN artificial neural network consists of one temporal convolutional layer, one long short-term memory (LSTM) la...

Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE.

Australasian physical & engineering sciences in medicine
Timely prediction of cardiovascular diseases with the help of a computer-aided diagnosis system minimizes the mortality rate of cardiac disease patients. Cardiac arrhythmia detection is one of the most challenging tasks, because the variations of ele...

Regression-based machine-learning approaches to predict task activation using resting-state fMRI.

Human brain mapping
Resting-state fMRI has shown the ability to predict task activation on an individual basis by using a general linear model (GLM) to map resting-state network features to activation z-scores. The question remains whether the relatively simplistic GLM ...

Determining motions with an IMU during level walking and slope and stair walking.

Journal of sports sciences
This study investigated whether using an inertial measurement unit (IMU) can identify different walking conditions, including level walking (LW), descent (DC) and ascent (AC) slope walking as well as downstairs (DS) and upstairs (US) walking. Thirty ...

A machine-learning method for classifying and analyzing foot placement: Application to manual material handling.

Journal of biomechanics
Foot placement strategy is an essential aspect in the study of movement involving full body displacement. To get beyond a qualitative analysis, this paper provides a foot placement classification and analysis method that can be used in sports, rehabi...

Decoding Brain States From fMRI Signals by Using Unsupervised Domain Adaptation.

IEEE journal of biomedical and health informatics
With the development of deep learning in medical image analysis, decoding brain states from functional magnetic resonance imaging (fMRI) signals has made significant progress. Previous studies often utilized deep neural networks to automatically clas...

Multi optimized SVM classifiers for motor imagery left and right hand movement identification.

Australasian physical & engineering sciences in medicine
EEG signal can be a good alternative for disabled persons who cannot perform actions or perform them improperly. Brain computer interface (BCI) is an attractive technology which permits control and interaction with a computer or a machine using EEG s...

Development of a biological signal-based evaluator for robot-assisted upper-limb rehabilitation: a pilot study.

Australasian physical & engineering sciences in medicine
Bio-signal based assessment for upper-limb functions is an attractive technology for rehabilitation. In this work, an upper-limb function evaluator is developed based on biological signals, which could be used for selecting different robotic training...

Models of the effect of teleoperation transmission delay on robot movement time.

Ergonomics
Recent research of [Scholcover and Gillan ( 2018 )] has shown experimentally that system transmission delay has a linear effect on the time taken to perform a complex tracking task with a simple teleoperated robot. This note shows that, for the case ...