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Hand

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Adaptive Multi-Dimensional Weighted Network With Category-Aware Contrastive Learning for Fine-Grained Hand Bone Segmentation.

IEEE journal of biomedical and health informatics
Accurately delineating and categorizing individual hand bones in 3D ultrasound (US) is a promising technology for precise digital diagnostic analysis. However, this is a challenging task due to the inherent imaging limitations of the US and the insig...

Deep Learning for hand tracking in Parkinson's Disease video-based assessment: Current and future perspectives.

Artificial intelligence in medicine
BACKGROUND: Parkinson's Disease (PD) demands early diagnosis and frequent assessment of symptoms. In particular, analysing hand movements is pivotal to understand disease progression. Advancements in hand tracking using Deep Learning (DL) allow for t...

Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis.

RMD open
OBJECTIVES: To train, test and validate the performance of a convolutional neural network (CNN)-based approach for the automated assessment of bone erosions, osteitis and synovitis in hand MRI of patients with inflammatory arthritis.

Post-stroke hand gesture recognition via one-shot transfer learning using prototypical networks.

Journal of neuroengineering and rehabilitation
BACKGROUND: In-home rehabilitation systems are a promising, potential alternative to conventional therapy for stroke survivors. Unfortunately, physiological differences between participants and sensor displacement in wearable sensors pose a significa...

Deep learning classification of EEG-based BCI monitoring of the attempted arm and hand movements.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: The primary objective of this research is to improve the average classification performance for specific movements in patients with cervical spinal cord injury (SCI).

Dual Stream Long Short-Term Memory Feature Fusion Classifier for Surface Electromyography Gesture Recognition.

Sensors (Basel, Switzerland)
Gesture recognition using electromyography (EMG) signals has prevailed recently in the field of human-computer interactions for controlling intelligent prosthetics. Currently, machine learning and deep learning are the two most commonly employed meth...

Task-Oriented Training by a Personalized Electromyography-Driven Soft Robotic Hand in Chronic Stroke: A Randomized Controlled Trial.

Neurorehabilitation and neural repair
BACKGROUND: Intensive task-oriented training has shown promise in enhancing distal motor function among patients with chronic stroke. A personalized electromyography (EMG)-driven soft robotic hand was developed to assist task-oriented object-manipula...

Evaluating initial usability of a hand augmentation device across a large and diverse sample.

Science robotics
The advancement of motor augmentation and the broader domain of human-machine interaction rely on a seamless integration with users' physical and cognitive capabilities. These considerations may markedly fluctuate among individuals on the basis of th...

A quality and readability comparison of artificial intelligence and popular health website education materials for common hand surgery procedures.

Hand surgery & rehabilitation
INTRODUCTION: ChatGPT and its application in producing patient education materials for orthopedic hand disorders has not been extensively studied. This study evaluated the quality and readability of educational information pertaining to common hand s...