AIMC Topic: Electromyography

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Hip contact forces can be predicted with a neural network using only synthesised key points and electromyography in people with hip osteoarthritis.

Osteoarthritis and cartilage
OBJECTIVE: To develop and validate a neural network to estimate hip contact forces (HCF), and lower body kinematics and kinetics during walking in individuals with hip osteoarthritis (OA) using synthesised anatomical key points and electromyography. ...

Specific Instructions Are Important: A Cross-sectional Study on Device Parameters and Instruction Types While Walking With a Robot in Children and Adolescents.

American journal of physical medicine & rehabilitation
OBJECTIVE: The aim of the study is to evaluate how gait kinematics and muscle activity during robot-assisted gait training are affected by different combinations of parameter settings and a number of instruction types, ranging from no instructions to...

Deep learning approach to improve the recognition of hand gesture with multi force variation using electromyography signal from amputees.

Medical engineering & physics
Variations in muscular contraction are known to significantly impact the quality of the generated EMG signal and the output decision of a proposed classifier. This is an issue when the classifier is further implemented in prosthetic hand design. Ther...

Classification of Action Potentials With High Variability Using Convolutional Neural Network for Motor Unit Tracking.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The reliable classification of motor unit action potentials (MUAPs) provides the possibility of tracking motor unit (MU) activities. However, the variation of MUAP profiles caused by multiple factors in realistic conditions challenges the accurate cl...

Teleoperation of an Anthropomorphic Robot Hand with a Metamorphic Palm and Tunable-Stiffness Soft Fingers.

Soft robotics
Teleoperation in soft robotics can endow soft robots with the ability to perform complex tasks through human-robot interaction. In this study, we propose a teleoperated anthropomorphic soft robot hand with variable degrees of freedom (DOFs) and a met...

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography.

Medical & biological engineering & computing
Challenges arise in accessing archived signal outputs due to proprietary software limitations. There is a notable lack of exploration in open-source mandibular EMG signal conversion for continuous access and analysis, hindering tasks such as pattern ...

The Impact of Feature Extraction on Classification Accuracy Examined by Employing a Signal Transformer to Classify Hand Gestures Using Surface Electromyography Signals.

Sensors (Basel, Switzerland)
Interest in developing techniques for acquiring and decoding biological signals is on the rise in the research community. This interest spans various applications, with a particular focus on prosthetic control and rehabilitation, where achieving prec...

Classification of EMG signals with CNN features and voting ensemble classifier.

Computer methods in biomechanics and biomedical engineering
Electromyography (EMG) signals are primarily used to control prosthetic hands. Classifying hand gestures efficiently with EMG signals presents numerous challenges. In addition to overcoming these challenges, a successful combination of feature extrac...

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In the field of EMG-based force modeling, the ability to generalize models across individuals could play a significant role in its adoption across a range of applications, including assistive devices, robotic and rehabilitation devices. However, curr...

Deep Learning-Based Identification Algorithm for Transitions Between Walking Environments Using Electromyography Signals Only.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Although studies on terrain identification algorithms to control walking assistive devices have been conducted using sensor fusion, studies on transition classification using only electromyography (EMG) signals have yet to be conducted. Therefore, th...