AIMC Topic: Movement
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Motion-flow-guided recurrent network for respiratory signal estimation of x-ray angiographic image sequences.
Physics in medicine and biology
Dec 11, 2020
Motion compensation can eliminate inconsistencies of respiratory movement during image acquisitions for precise vascular reconstruction in the clinical diagnosis of vascular disease from x-ray angiographic image sequences. In x-ray-based vascular int...
A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding.
Neural plasticity
Dec 7, 2020
Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject's intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based...
Artificial fly visual joint perception neural network inspired by multiple-regional collision detection.
Neural networks : the official journal of the International Neural Network Society
Dec 5, 2020
The biological visual system includes multiple types of motion sensitive neurons which preferentially respond to specific perceptual regions. However, it still keeps open how to borrow such neurons to construct bio-inspired computational models for m...
Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic F-FDG PET Brain Studies.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Nov 27, 2020
This work set out to develop a motion-correction approach aided by conditional generative adversarial network (cGAN) methodology that allows reliable, data-driven determination of involuntary subject motion during dynamic F-FDG brain studies. Ten he...
EEG-based trial-by-trial texture classification during active touch.
Scientific reports
Nov 27, 2020
Trial-by-trial texture classification analysis and identifying salient texture related EEG features during active touch that are minimally influenced by movement type and frequency conditions are the main contributions of this work. A total of twelve...
Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control.
IEEE transactions on visualization and computer graphics
Nov 24, 2020
This paper introduces a new generative deep learning network for human motion synthesis and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial training for human motion modeling. We first describe an efficient method...
Spatio-Temporal Manifold Learning for Human Motions via Long-Horizon Modeling.
IEEE transactions on visualization and computer graphics
Nov 24, 2020
Data-driven modeling of human motions is ubiquitous in computer graphics and computer vision applications, such as synthesizing realistic motions or recognizing actions. Recent research has shown that such problems can be approached by learning a nat...
A Pilot Study on Convolutional Neural Networks for Motion Estimation From Ultrasound Images.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Nov 24, 2020
In recent years, deep learning (DL) has been successfully applied to the analysis and processing of ultrasound images. To date, most of this research has focused on segmentation and view recognition. This article benchmarks different convolutional ne...
Effects of robot viscous forces on arm movements in chronic stroke survivors: a randomized crossover study.
Journal of neuroengineering and rehabilitation
Nov 24, 2020
BACKGROUND: Our previous work showed that speed is linked to the ability to recover in chronic stroke survivors. Participants moving faster on the first day of a 3-week study had greater improvements on the Wolf Motor Function Test.
Combined virtual reality and haptic robotics induce space and movement invariant sensorimotor adaptation.
Neuropsychologia
Nov 21, 2020
Prism adaptation is a method for studying visuomotor plasticity in healthy individuals, as well as for rehabilitating patients suffering spatial neglect. We developed a new set-up based on virtual-reality (VR) and haptic-robotics allowing us to induc...
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