Cross-user gesture recognition from sEMG signals using an optimal transport assisted student-teacher framework
The cross-user gesture recognition is a puzzle in the myoelectric control system, owing to
great variability in muscle activities across different users. To address this problem, a novel …
great variability in muscle activities across different users. To address this problem, a novel …
SEMG-based upper limb movement classifier: Current scenario and upcoming challenges
Despite achieving accuracies higher than 90% on recognizing upper-limb movements
through sEMG (surface Electromyography) signal with the state of art classifiers in the …
through sEMG (surface Electromyography) signal with the state of art classifiers in the …
Hand gesture recognition based on sEMG signal and convolutional neural network
Z Su, H Liu, J Qian, Z Zhang, L Zhang - International Journal of …, 2021 - World Scientific
Recently, deep learning has become a promising technique for constructing gesture
recognition classifiers from surface electromyography (sEMG) signals in human–computer …
recognition classifiers from surface electromyography (sEMG) signals in human–computer …
A novel and lightweight real-time continuous motion gesture recognition algorithm for smartphones
Advancement in smartphones has facilitated the investigation of new modalities of human-
machine interaction, including communication through touch, voice, and gestures. In-depth …
machine interaction, including communication through touch, voice, and gestures. In-depth …
Incremental adaptive gesture classifier for upper limb prostheses
Myoelectric pattern recognition is widely used to control upper limb prostheses. However,
the non-stationary characteristics of electromyography (EMG) signals, caused by …
the non-stationary characteristics of electromyography (EMG) signals, caused by …
Online myoelectric pattern recognition based on hybrid spatial features
Although Electromyography (EMG) signals are sources of neural information that are
essential in controlling the prosthetic hand, many confounding factors caused the variation …
essential in controlling the prosthetic hand, many confounding factors caused the variation …
Enhancing sEMG-Based Finger Motion Prediction with CNN-LSTM Regressors for Controlling a Hand Exoskeleton
In recent years, the number of people with disabilities has increased hugely, especially in
low-and middle-income countries. At the same time, robotics has made significant advances …
low-and middle-income countries. At the same time, robotics has made significant advances …
Using the robust high density-surface electromyography features for real-time hand gestures classification
Abstract Using High-Density surface Electromyography (HD-sEMG) signals for gesture
classification has augmented the spatial information of muscle activity by increasing the …
classification has augmented the spatial information of muscle activity by increasing the …
Adaptive myoelectric pattern recognition based on hybrid spatial features of HD-sEMG signals
Myoelectric pattern recognition is a useful tool for identifying the user's intended motion.
However, the inherent nonstationary properties of Electromyography (EMG) signals usually …
However, the inherent nonstationary properties of Electromyography (EMG) signals usually …
Hand gesture recognition based on deep neural network and sEMG signal
H Liu, Z Zhang, J Qian, W Wang… - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
The physiological electrical signal of human body is the direct response from human
behavior intention. By analyzing and interpreting the physiological electrical signal of human …
behavior intention. By analyzing and interpreting the physiological electrical signal of human …