AIMC Topic: Electromyography

Clear Filters Showing 271 to 280 of 692 articles

A generic neural network model to estimate populational neural activity for robust neural decoding.

Computers in biology and medicine
BACKGROUND: Robust and continuous neural decoding is crucial for reliable and intuitive neural-machine interactions. This study developed a novel generic neural network model that can continuously predict finger forces based on decoded populational m...

Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks.

Sensors (Basel, Switzerland)
In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography is often ad...

Hammerstein-Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals.

Biosensors
This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein-Wiener model, the first part of this study outlines the ...

Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, add...

Feature Fusion-Based Improved Capsule Network for sEMG Signal Recognition.

Computational intelligence and neuroscience
This paper proposes a feature fusion-based improved capsule network (FFiCAPS) to improve the performance of surface electromyogram (sEMG) signal recognition with the purpose of distinguishing hand gestures. Current deep learning models, especially co...

Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography.

Sensors (Basel, Switzerland)
Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based ...

Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques.

Medical & biological engineering & computing
The main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface...

Learning to teleoperate an upper-limb assistive humanoid robot for bimanual daily-living tasks.

Biomedical physics & engineering express
Bimanual humanoid platforms for home assistance are nowadays available, both as academic prototypes and commercially. Although they are usually thought of as daily helpers for non-disabled users, their ability to move around, together with their dext...

Advanced Compliant Anti-Gravity Robot System for Lumbar Stabilization Exercise Using Series Elastic Actuator.

IEEE journal of translational engineering in health and medicine
: The lumbar stabilization exercise is one of the most recommended treatments in medical professionals for patients suffering from low back pain. However, because lumbar stabilization exercise is calisthenics, it is challenging to perform because of ...

The Impact of Load Style Variation on Gait Recognition Based on sEMG Images Using a Convolutional Neural Network.

Sensors (Basel, Switzerland)
Surface electromyogram (sEMG) signals are widely employed as a neural control source for lower-limb exoskeletons, in which gait recognition based on sEMG is particularly important. Many scholars have taken measures to improve the accuracy of gait rec...