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Electromyography

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Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification fa...

Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.

Automatic discovery of resource-restricted Convolutional Neural Network topologies for myoelectric pattern recognition.

Computers in biology and medicine
Convolutional Neural Networks (CNNs) have been subject to extensive attention in the pattern recognition literature due to unprecedented performance in tasks of information extraction from unstructured data. Whereas available methods for supervised t...

Robot-Driven Locomotor Perturbations Reveal Synergy-Mediated, Context-Dependent Feedforward and Feedback Mechanisms of Adaptation.

Scientific reports
Humans respond to mechanical perturbations that affect their gait by changing their motor control strategy. Previous work indicates that adaptation during gait is context dependent, and perturbations altering long-term stability are compensated for e...

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...