Nov 11, 2020 · The experimental results show that LSTM model is more suitable for gesture fatigue prediction. The processed sEMG signals are appropriate for ...
The processed sEMG signals are appropriate for using as the training set the fatigue degree of one-handed gesture. It is better to use wavelet ...
The fatigue energy consumption of independent gestures can be obtained by calculating the power spectrum of surface electromyography (sEMG) signals.
Jan 28, 2023 · We propose an HGR system using a binarized neural network (BNN), a lightweight convolutional neural network (CNN), with one dry-type sEMG sensor.
The fatigue energy consumption of independent gestures can be obtained by calculating the power spectrum of surface electromyography (sEMG) signals.
Optimization of inter-subject sEMG-based hand gesture ...
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This paper explored various unsupervised domain adaptation methods based on statistical learning, combined with different machine learning classifiers.
Hand gesture recognition based on surface electromyographic (sEMG) signals is a promising approach for the development of Human-Machine Interfaces (HMIs) ...
sEMG-Based Neural Network Prediction Model Selection of Gesture Fatigue and Dataset Optimization. Computational Intelligence and Neuroscience, 2020.
Mar 15, 2024 · This study explores the effect of muscle fatigue on recognition accuracy in a gesture recognition task based on sEMG.
In this paper, we systematically compare six filter and wrapper feature evaluation methods and investigate their respective impacts on the accuracy of gesture ...