Deep learning for EMG-based human-machine interaction: A review
D **ong, D Zhang, X Zhao… - IEEE/CAA Journal of …, 2021 - ieeexplore.ieee.org
Electromyography (EMG) has already been broadly used in human-machine interaction
(HMI) applications. Determining how to decode the information inside EMG signals robustly …
(HMI) applications. Determining how to decode the information inside EMG signals robustly …
Emerging wearable interfaces and algorithms for hand gesture recognition: A survey
Hands are vital in a wide range of fundamental daily activities, and neurological diseases
that impede hand function can significantly affect quality of life. Wearable hand gesture …
that impede hand function can significantly affect quality of life. Wearable hand gesture …
Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future
W Li, P Shi, H Yu - Frontiers in neuroscience, 2021 - frontiersin.org
Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life,
and limits their performance in activities of daily life. The realization of natural control for …
and limits their performance in activities of daily life. The realization of natural control for …
A review of the key technologies for sEMG-based human-robot interaction systems
K Li, J Zhang, L Wang, M Zhang, J Li, S Bao - … Signal Processing and …, 2020 - Elsevier
As physiological signals that are closely related to human motion, surface electromyography
(sEMG) signals have been widely used in human-robot interaction systems (HRISs). Some …
(sEMG) signals have been widely used in human-robot interaction systems (HRISs). Some …
EMGHandNet: A hybrid CNN and Bi-LSTM architecture for hand activity classification using surface EMG signals
Abstract Recently, Convolutional Neural Networks (CNNs) have been used for the
classification of hand activities from surface Electromyography (sEMG) signals. However …
classification of hand activities from surface Electromyography (sEMG) signals. However …
Hand gesture recognition based on surface electromyography using convolutional neural network with transfer learning method
This paper presents an effective transfer learning (TL) strategy for the realization of surface
electromyography (sEMG)-based gesture recognition with high generalization and low …
electromyography (sEMG)-based gesture recognition with high generalization and low …
Dynamic gesture recognition using surface EMG signals based on multi-stream residual network
Z Yang, D Jiang, Y Sun, B Tao, X Tong… - … in Bioengineering and …, 2021 - frontiersin.org
Gesture recognition technology is widely used in the flexible and precise control of
manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform …
manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform …
Surface-electromyography-based gesture recognition by multi-view deep learning
Gesture recognition using sparse multichannel surface electromyography (sEMG) is a
challenging problem, and the solutions are far from optimal from the point of view of muscle …
challenging problem, and the solutions are far from optimal from the point of view of muscle …
Multiday EMG-based classification of hand motions with deep learning techniques
Pattern recognition of electromyography (EMG) signals can potentially improve the
performance of myoelectric control for upper limb prostheses with respect to current clinical …
performance of myoelectric control for upper limb prostheses with respect to current clinical …
EMG-based online classification of gestures with recurrent neural networks
Online gesture classification can rely on unsupervised segmentation in order to divide the
data stream into static and dynamic segments for individual classification. However, this …
data stream into static and dynamic segments for individual classification. However, this …