AIMC Topic: Electromyography

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Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke.

Journal of neural engineering Mar 9, 2022
The central-to-peripheral voluntary motor effort (VME) in the affected limb is a dominant force for driving the functional neuroplasticity on motor restoration post-stroke. However, current rehabilitation robots isolated the central and peripheral in...
Robotic Surgical Procedures Wrist Humans Robotics Upper Extremity Stroke Rehabilitation Electromyography Stroke
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Deep learning for predicting respiratory rate from biosignals.

Computers in biology and medicine Mar 2, 2022
In the past decade, deep learning models have been applied to bio-sensors used in a body sensor network for prediction. Given recent innovations in this field, the prediction accuracy of novel models needs to be evaluated for bio-signals. In this pap...
Deep Learning Electrocardiography Neural Networks, Computer Electromyography Respiratory Rate
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A generic neural network model to estimate populational neural activity for robust neural decoding.

Computers in biology and medicine Mar 1, 2022
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...
Muscle, Skeletal Electromyography Humans Neural Networks, Computer Fingers Motor Neurons
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Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks.

Sensors (Basel, Switzerland) Feb 22, 2022
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...
Electromyography Upper Extremity Neural Networks, Computer Gestures
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Hammerstein-Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals.

Biosensors Feb 13, 2022
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 ...
Neural Networks, Computer Electromyography Muscles
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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 Jan 28, 2022
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...
Fingers Movement Hand Electromyography Gestures Humans Neural Networks, Computer Algorithms Reproducibility of Results
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Feature Fusion-Based Improved Capsule Network for sEMG Signal Recognition.

Computational intelligence and neuroscience Jan 21, 2022
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...
Humans Electromyography Neural Networks, Computer Recognition, Psychology Gestures
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Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography.

Sensors (Basel, Switzerland) Jan 16, 2022
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 ...
Brain-Computer Interfaces Electromyography Amputees Wrist Deep Learning Humans Electroencephalography Algorithms
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Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques.

Medical & biological engineering & computing Jan 14, 2022
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...
Humans Machine Learning Algorithms Electromyography Muscles Support Vector Machine Lower Extremity
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Learning to teleoperate an upper-limb assistive humanoid robot for bimanual daily-living tasks.

Biomedical physics & engineering express Dec 16, 2021
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...
Activities of Daily Living Upper Extremity Self-Help Devices Robotics Electromyography Humans
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