AIMC Topic: Young Adult

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Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use.

Sensors (Basel, Switzerland)
Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing ...

A Real-Time Health 4.0 Framework with Novel Feature Extraction and Classification for Brain-Controlled IoT-Enabled Environments.

Neural computation
In this letter, we propose two novel methods for four-class motor imagery (MI) classification using electroencephalography (EEG). Also, we developed a real-time health 4.0 (H4.0) architecture for brain-controlled internet of things (IoT) enabled envi...

Classification of schizophrenia and normal controls using 3D convolutional neural network and outcome visualization.

Schizophrenia research
BACKGROUND: The recent deep learning-based studies on the classification of schizophrenia (SCZ) using MRI data rely on manual extraction of feature vector, which destroys the 3D structure of MRI data. In order to both identify SCZ and find relevant b...

Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity.

Scientific reports
Human age estimation is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activi...

Psychological reactions to human versus robotic job replacement.

Nature human behaviour
Advances in robotics and artificial intelligence are increasingly enabling organizations to replace humans with intelligent machines and algorithms. Forecasts predict that, in the coming years, these new technologies will affect millions of workers i...

IMU, sEMG, or their cross-correlation and temporal similarities: Which signal features detect lateral compensatory balance reactions more accurately?

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Falls are the leading cause of fatal and non-fatal injuries among seniors worldwide. While laboratory evidence supports the view that impaired ability to execute compensatory balance responses (CBRs) is linked to an increase...

Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction.

NeuroImage
Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, back...

Artificial neural networks reveal individual differences in metacognitive monitoring of memory.

PloS one
Previous work supports an age-specific impairment for recognition memory of pairs of words and other stimuli. The present study tested the generalization of an associative deficit across word, name, and nonword stimulus types in younger and older adu...

Performance of deep learning for differentiating pancreatic diseases on contrast-enhanced magnetic resonance imaging: A preliminary study.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to evaluate the ability of deep learning to differentiate pancreatic diseases on contrast-enhanced magnetic resonance (MR) images with the aid of generative adversarial network (GAN).

A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning.

IEEE journal of biomedical and health informatics
The ballistocardiography (BCG) signal is a measurement of the vibrations of the center of mass of the body due to the cardiac cycle and can be used for noninvasive hemodynamic monitoring. The seismocardiography (SCG) signals measure the local vibrati...