AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Motion

Showing 261 to 270 of 845 articles

Clear Filters

A Novel Velocity-Based Control in a Sensor Space for Parallel Manipulators.

Sensors (Basel, Switzerland)
It is a challenging task to track objects moving along an unknown trajectory. Conventional model-based controllers require detailed knowledge of a robot's kinematics and the target's trajectory. Tracking precision heavily relies on kinematics to infe...

Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI.

Neuroradiology
INTRODUCTION: Deep learning-based MRI reconstruction has recently been introduced to improve image quality. This study aimed to evaluate the performance of deep learning reconstruction in pediatric brain MRI.

Choreography Controlled (ChoCo) brain MRI artifact generation for labeled motion-corrupted datasets.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
MRI is a non-invasive medical imaging modality that is sensitive to patient motion, which constitutes a major limitation in most clinical applications. Solutions may arise from the reduction of acquisition times or from motion-correction techniques, ...

Human Arm Motion Prediction for Collision Avoidance in a Shared Workspace.

Sensors (Basel, Switzerland)
Industry 4.0 transforms classical industrial systems into more human-centric and digitized systems. Close human-robot collaboration is becoming more frequent, which means security and efficiency issues need to be carefully considered. In this paper, ...

Assessment of deep learning pose estimates for sports collision tracking.

Journal of sports sciences
Injury assessment during sporting collisions requires estimation of the associated kinematics. While marker-based solutions are widely accepted as providing accurate and reliable measurements, setup times are lengthy and it is not always possible to ...

A Deep Sequence Learning Framework for Action Recognition in Small-Scale Depth Video Dataset.

Sensors (Basel, Switzerland)
Depth video sequence-based deep models for recognizing human actions are scarce compared to RGB and skeleton video sequences-based models. This scarcity limits the research advancements based on depth data, as training deep models with small-scale da...

Cofopose: Conditional 2D Pose Estimation with Transformers.

Sensors (Basel, Switzerland)
Human pose estimation has long been a fundamental problem in computer vision and artificial intelligence. Prominent among the 2D human pose estimation (HPE) methods are the regression-based approaches, which have been proven to achieve excellent resu...

Topographic design in wearable MXene sensors with in-sensor machine learning for full-body avatar reconstruction.

Nature communications
Wearable strain sensors that detect joint/muscle strain changes become prevalent at human-machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics w...

Lite-3DCNN Combined with Attention Mechanism for Complex Human Movement Recognition.

Computational intelligence and neuroscience
Three-dimensional convolutional network (3DCNN) is an essential field of motion recognition research. The research work of this paper optimizes the traditional three-dimensional convolution network, introduces the self-attention mechanism, and propos...

Frame-rate up-conversion detection based on convolutional neural network for learning spatiotemporal features.

Forensic science international
With the advance in user-friendly and powerful video editing tools, anyone can easily manipulate videos without leaving prominent visual traces. Frame-rate up-conversion (FRUC), a representative temporal-domain operation, increases the motion continu...