AIMC Topic: Hip Joint

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Investigation of Trajectory Tracking Control in Hip Joints of Lower-Limb Exoskeletons Using SSA-Fuzzy PID Optimization.

Sensors (Basel, Switzerland)
The application of lower-limb exoskeleton robots in rehabilitation is becoming more prevalent, where the precision of control and the speed of response are essential for effective movement tracking. This study tackles the challenge of optimizing both...

Deep learning based screening model for hip diseases on plain radiographs.

PloS one
INTRODUCTION: The interpretation of plain hip radiographs can vary widely among physicians. This study aimed to develop and validate a deep learning-based screening model for distinguishing normal hips from severe hip diseases on plain radiographs.

A deep learning approach for automatic 3D segmentation of hip cartilage and labrum from direct hip MR arthrography.

Scientific reports
The objective was to use convolutional neural networks (CNNs) for automatic segmentation of hip cartilage and labrum based on 3D MRI. In this retrospective single-center study, CNNs with a U-Net architecture were used to develop a fully automated seg...

Synthetic data generation in motion analysis: A generative deep learning framework.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Generative deep learning has emerged as a promising data augmentation technique in recent years. This approach becomes particularly valuable in areas such as motion analysis, where it is challenging to collect substantial amounts of data. The objecti...

Predictive estimation of ovine hip joint centers: Neural networks vs. linear regression.

Journal of biomechanics
The purpose of this study was to investigate the utility of neural networks to estimate the hip joint center location in sheep and compare the accuracy of neural networks to previously developed linear regression models. CT scans from 16 sheep of var...

Development of a Deep Learning Model for Automating Implant Position in Total Hip Arthroplasty.

The Journal of arthroplasty
BACKGROUND: Novel methods for annotating antero-posterior pelvis radiographs and fluoroscopic images with deep-learning models have recently been developed. However, their clinical use has been limited. Therefore, the purpose of this study was to dev...

Artificial neural networks' estimations of lower-limb kinetics in sidestepping: Comparison of full-body vs. lower-body landmark sets.

Journal of biomechanics
Artificial neural networks (ANNs) offers potential for obtaining kinetics in non-laboratory. This study compared the estimation performance for ground reaction forces (GRF) and lower-limb joint moments during sidestepping between ANNs fed with full-b...

Predicting lower body joint moments and electromyography signals using ground reaction forces during walking and running: An artificial neural network approach.

Gait & posture
BACKGROUND: This study leverages Artificial Neural Networks (ANNs) to predict lower limb joint moments and electromyography (EMG) signals from Ground Reaction Forces (GRF), providing a novel perspective on human gait analysis. This approach aims to e...

Robot-Aided Training of Propulsion: Effects of Torque Pulses Applied to the Hip and Knee Joint Under User-Driven Treadmill Control.

IEEE transactions on bio-medical engineering
OBJECTIVE: to establish whether torque pulses applied by an exoskeleton to the hip and knee joint modulate propulsion mechanics and whether changes in propulsion mechanics are sustained after exposure to torque pulses under user-driven treadmill cont...

Optimizing hip MRI: enhancing image quality and elevating inter-observer consistency using deep learning-powered reconstruction.

BMC medical imaging
BACKGROUND: Conventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. L...