AI Medical Compendium Topic:
Motion

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Performance Analysis of a Head and Eye Motion-Based Control Interface for Assistive Robots.

Sensors (Basel, Switzerland)
Assistive robots support people with limited mobility in their everyday life activities and work. However, most of the assistive systems and technologies for supporting eating and drinking require a residual mobility in arms or hands. For people with...

Artificial Intelligence-Assisted motion capture for medical applications: a comparative study between markerless and passive marker motion capture.

Computer methods in biomechanics and biomedical engineering
We aimed to determine whether artificial intelligence (AI)-assisted markerless motion capture software is useful in the clinical medicine and rehabilitation fields. Currently, it is unclear whether the AI-assisted markerless method can be applied to ...

The realization of robotic neurorehabilitation in clinical: use of computational intelligence and future prospects analysis.

Expert review of medical devices
: Although there is a need for rehabilitation treatment with the increase in the aging population, the shortage of skilled physicians frustrates this necessity. Robotic technology has been advocated as one of the most viable methods with the potentia...

Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training.

Sensors (Basel, Switzerland)
Using standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise games (exergames). We need to investigate to what extent such DL-based systems can provide satisfyi...

Retrospective respiratory motion correction in cardiac cine MRI reconstruction using adversarial autoencoder and unsupervised learning.

NMR in biomedicine
The aim of this study was to develop a deep neural network for respiratory motion compensation in free-breathing cine MRI and evaluate its performance. An adversarial autoencoder network was trained using unpaired training data from healthy volunteer...

Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation.

IEEE transactions on medical imaging
Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications. However, this success is heavily dependent on the quality of the image that is being segmented. A commonly negl...

A highly intuitive and ergonomic redundant joint master device for four-degrees of freedom flexible endoscopic surgery robot.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Studies have been conducted on slave-specific master devices with a similar kinematic structure to the slave to enhance intuitiveness. However, for the four-degrees of freedom (DOFs) slave of a flexible endoscopic surgery robot, only four...

Dynamic MRI reconstruction with end-to-end motion-guided network.

Medical image analysis
Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is informative and important to understand motion mechanisms of body regions. Modeling such information into the MRI reconstruction process produces temporally coh...

Ultra-sensitive and resilient compliant strain gauges for soft machines.

Nature
Soft machines are a promising design paradigm for human-centric devices and systems required to interact gently with their environment. To enable soft machines to respond intelligently to their surroundings, compliant sensory feedback mechanisms are ...

Motion Inference Using Sparse Inertial Sensors, Self-Supervised Learning, and a New Dataset of Unscripted Human Motion.

Sensors (Basel, Switzerland)
In recent years, wearable sensors have become common, with possible applications in biomechanical monitoring, sports and fitness training, rehabilitation, assistive devices, or human-computer interaction. Our goal was to achieve accurate kinematics e...