AIMC Topic: Motion

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Machine learning techniques demonstrating individual movement patterns of the vertebral column: the fingerprint of spinal motion.

Computer methods in biomechanics and biomedical engineering
Surface topography systems enable the capture of spinal dynamic movement; however, it is unclear whether vertebral dynamics are unique enough to identify individuals. Therefore, in this study, we investigated whether the identification of individuals...

Open source Vicon Toolkit for motion capture and Gait Analysis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The Vicon motion capture system is a popular tool for biomechanics, gait analysis, and robotics. The ASCII files produced are large and complex, making them difficult to read and analyze.

Static Modeling of Soft Reinforced Bending Actuator Considering External Force Constraints.

Soft robotics
Soft robots are utilized in various operations such as rehabilitation, manipulation, and locomotion. These robots are sometimes excited by means of rubber based bending actuators, which are highly nonlinear and hyperelastic. In addition, in contrast ...

Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks.

Sensors (Basel, Switzerland)
Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently. However, deep neural networks are usually overparameterized, i.e., most of the connectio...

Multi-Phase Joint-Angle Trajectory Generation Inspired by Dog Motion for Control of Quadruped Robot.

Sensors (Basel, Switzerland)
Quadruped robots are receiving great attention as a new means of transportation for various purposes, such as military, welfare, and rehabilitation systems. The use of four legs enables a robustly stable gait; compared to the humanoid robots, the qua...

An attention-based deep learning model for predicting microvascular invasion of hepatocellular carcinoma using an intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging.

Physics in medicine and biology
The intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging (IVIM-DWI) with a series of images with different-values has great potential as a tool for detecting, diagnosing, staging, and monitoring disease progression or ...

A Spatiotemporal Deep Learning Approach for Automatic Pathological Gait Classification.

Sensors (Basel, Switzerland)
Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art perf...

Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
We propose a multiscale spatio-temporal graph neural network (MST-GNN) to predict the future 3D skeleton-based human poses in an action-category-agnostic manner. The core of MST-GNN is a multiscale spatio-temporal graph that explicitly models the rel...

Modality-agnostic self-supervised deep feature learning and fast instance optimisation for multimodal fusion in ultrasound-guided interventions.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Fast and robust alignment of pre-operative MRI planning scans to intra-operative ultrasound is an important aspect for automatically supporting image-guided interventions. Thus far, learning-based approaches have failed to t...

Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks.

Sensors (Basel, Switzerland)
Optical motion capture is a mature contemporary technique for the acquisition of motion data; alas, it is non-error-free. Due to technical limitations and occlusions of markers, gaps might occur in such recordings. The article reviews various neural ...