AIMC Topic: Young Adult
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Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity.
Scientific reports
Aug 6, 2019
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...
Humans
Female
Reproducibility of Results
Aged
Exercise
Deep Learning
Wearable Electronic Devices
Mobile Applications
Nutrition Surveys
Young Adult
Adult
Male
Aging
Middle Aged
Accelerometry
Anthropometry
Datasets as Topic
Proportional Hazards Models
Locomotion
Kaplan-Meier Estimate
Health Status
Monitoring, Physiologic
Models, Biological
Telemedicine
Psychological reactions to human versus robotic job replacement.
Nature human behaviour
Aug 5, 2019
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
Aug 1, 2019
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
Aug 1, 2019
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
Jul 31, 2019
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
Jul 30, 2019
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
Jul 29, 2019
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...
Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.
International journal of neural systems
Jul 29, 2019
Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is ...
Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions.
Scientific reports
Jul 29, 2019
A neural network model was previously developed to predict melatonin rhythms accurately from blue light and skin temperature recordings in individuals on a fixed sleep schedule. This study aimed to test the generalizability of the model to other slee...
Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation.
Diagnostic and interventional imaging
Jul 26, 2019
PURPOSE: The purpose of this study was to report procedures developed to annotate abdominal computed tomography (CT) images from subjects without pancreatic disease that will be used as the input for deep convolutional neural networks (DNN) for devel...
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