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Knee

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A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
The commonly used classifiers for pattern recognition of human motion, like backpropagation neural network (BPNN) and support vector machine (SVM), usually implement the classification by extracting some hand-crafted features from the human biologica...

Deep Generative Adversarial Neural Networks for Compressive Sensing MRI.

IEEE transactions on medical imaging
Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS)...

Deep convolutional neural network for segmentation of knee joint anatomy.

Magnetic resonance in medicine
PURPOSE: To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tiss...

Super-resolution musculoskeletal MRI using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods.

Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.

Magnetic resonance in medicine
PURPOSE: To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilag...

Deep Learning in Medical Imaging: General Overview.

Korean journal of radiology
The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient...

A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking.

Journal of neuroengineering and rehabilitation
BACKGROUND: Carrying load alters normal walking, imposes additional stress to the musculoskeletal system, and results in an increase in energy consumption and a consequent earlier onset of fatigue. This phenomenon is largely due to increased work req...

LOPES II--Design and Evaluation of an Admittance Controlled Gait Training Robot With Shadow-Leg Approach.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Robotic gait training is gaining ground in rehabilitation. Room for improvement lies in reducing donning and doffing time, making training more task specific and facilitating active balance control, and by allowing movement in more degrees of freedom...