AIMC Topic: Electromyography

Clear Filters Showing 351 to 360 of 692 articles

Laser-Induced Graphene for Electrothermally Controlled, Mechanically Guided, 3D Assembly and Human-Soft Actuators Interaction.

Advanced materials (Deerfield Beach, Fla.)
Mechanically guided, 3D assembly has attracted broad interests, owing to its compatibility with planar fabrication techniques and applicability to a diversity of geometries and length scales. Its further development requires the capability of on-dema...

Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG.

Sensors (Basel, Switzerland)
Electromyography (EMG) is a measure of electrical activity generated by the contraction of muscles. Non-invasive surface EMG (sEMG)-based pattern recognition methods have shown the potential for upper limb prosthesis control. However, it is still ins...

A Myoelectric Control Interface for Upper-Limb Robotic Rehabilitation Following Spinal Cord Injury.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Spinal cord injury (SCI) is a widespread, life-altering injury leading to impairment of sensorimotor function that, while once thought to be permanent, is now being treated with the hope of one day being able to restore function. Surface electromyogr...

Assistance Robotics and Biosensors 2019.

Sensors (Basel, Switzerland)
This Special Issue is focused on breakthrough developments in the field of assistive and rehabilitation robotics. The selected contributions include current scientific progress from biomedical signal processing and cover applications to myoelectric p...

High-Density Surface EMG-Based Gesture Recognition Using a 3D Convolutional Neural Network.

Sensors (Basel, Switzerland)
High-density surface electromyography (HD-sEMG) and deep learning technology are becoming increasingly used in gesture recognition. Based on electrode grid data, information can be extracted in the form of images that are generated with instant value...

Feature Extraction of Surface Electromyography Based on Improved Small-World Leaky Echo State Network.

Neural computation
Surface electromyography (sEMG) is an electrophysiological reflection of skeletal muscle contractile activity that can directly reflect neuromuscular activity. It has been a matter of research to investigate feature extraction methods of sEMG signals...

Identification of the best strategy to command variable stiffness using electromyographic signals.

Journal of neural engineering
OBJECTIVE: In the last decades, many EMG-controlled robotic devices were developed. Since stiffness control may be required to perform skillful interactions, different groups developed devices whose stiffness is real-time controlled based on EMG sign...

Deep Learning in Physiological Signal Data: A Survey.

Sensors (Basel, Switzerland)
Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially expl...

Human arm weight compensation in rehabilitation robotics: efficacy of three distinct methods.

Journal of neuroengineering and rehabilitation
BACKGROUND: Arm weight compensation with rehabilitation robots for stroke patients has been successfully used to increase the active range of motion and reduce the effects of pathological muscle synergies. However, the differences in structure, perfo...

Estimation of absolute states of human skeletal muscle via standard B-mode ultrasound imaging and deep convolutional neural networks.

Journal of the Royal Society, Interface
The objective is to test automated estimation of active and passive skeletal muscle states using ultrasonic imaging. Current technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimatio...