AI Medical Compendium Topic

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

Hand

Showing 31 to 40 of 589 articles

Clear Filters

Brain Activation Pattern Caused by Soft Rehabilitation Glove and Virtual Reality Scenes: A Pilot fNIRS Study.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Clinical studies have proved significant improvements in hand motor function in stroke patients when assisted by robotic devices. However, there were few studies on neural activity changes in the brain during execution. This study aimed to investigat...

Prediction and Elimination of Physiological Tremor During Control of Teleoperated Robot Based on Deep Learning.

Sensors (Basel, Switzerland)
Currently, teleoperated robots, with the operator's input, can fully perceive unknown factors in a complex environment and have strong environmental interaction and perception abilities. However, physiological tremors in the human hand can seriously ...

Improved Surface Electromyogram-Based Hand-Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning.

Sensors (Basel, Switzerland)
Deep neural networks (DNNs) and transfer learning (TL) have been used to improve surface electromyogram (sEMG)-based force estimation. However, prior studies focused mostly on applying TL within one joint, which limits dataset size and diversity. Her...

A Multi-Scale CNN for Transfer Learning in sEMG-Based Hand Gesture Recognition for Prosthetic Devices.

Sensors (Basel, Switzerland)
Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generali...

Enhancing systematic review efficiency in hand surgery using artificial intelligence (natural language processing) for abstract screening.

The Journal of hand surgery, European volume
The aim of the present study was to train a natural language processing model to recognize key text elements from research abstracts related to hand surgery, enhancing the efficiency of systematic review screening. A sample of 1600 abstracts from a s...

NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation.

Science robotics
To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, such spatial awareness involves estimating the object's pose and shape. T...

Classification of hand movements from EEG using a FusionNet based LSTM network.

Journal of neural engineering
. Accurate classification of electroencephalogram (EEG) signals is crucial for advancing brain-computer interface (BCI) technology. However, current methods face significant challenges in classifying hand movement EEG signals, including effective spa...

HarDNet-based deep learning model for osteoporosis screening and bone mineral density inference from hand radiographs.

Bone
PURPOSE: Osteoporosis, affecting over 200 million individuals, often remains unrecognized and untreated, increasing the risk of fractures in older adults. Osteoporosis is typically diagnosed with bone mineral density (BMD) measured by dual-energy X-r...

In good hands: A case for improving robotic dexterity.

Science (New York, N.Y.)
Twenty-first-century roboticists envision robots capable of sorting objects and packaging them, of chopping vegetables and folding clothes. But although many today believe that the only factors necessary for robots to achieve dexterous manipulation a...

Classification algorithms trained on simple (symmetric) lifting data perform poorly in predicting hand loads during complex (free-dynamic) lifting tasks.

Applied ergonomics
The performance of machine learning (ML) algorithms is dependent on which dataset it has been trained on. While ML algorithms are increasingly used for lift risk assessment, many algorithms are often trained and tested on controlled simulation datase...