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 …
Intelligent EMG pattern recognition control method for upper-limb multifunctional prostheses: advances, current challenges, and future prospects
Upper-limb amputation imposes significant burden on amputees thereby restricting them
from fully exploring their environments during activities of daily living. The use of intelligent …
from fully exploring their environments during activities of daily living. The use of intelligent …
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 …
Electromyogram-based classification of hand and finger gestures using artificial neural networks
Electromyogram (EMG) signals have been increasingly used for hand and finger gesture
recognition. However, most studies have focused on the wrist and whole-hand gestures and …
recognition. However, most studies have focused on the wrist and whole-hand gestures and …
Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements
Background Myoelectric pattern recognition systems can decode movement intention to
drive upper-limb prostheses. Despite recent advances in academic research, the …
drive upper-limb prostheses. Despite recent advances in academic research, the …
Proceedings of the first workshop on peripheral machine interfaces: going beyond traditional surface electromyography
One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic
devices. Despite decades of research, the state of the art is dramatically behind the …
devices. Despite decades of research, the state of the art is dramatically behind the …
Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control
Pattern recognition methods for classifying user motion intent based on surface
electromyography developed by research groups in well-controlled laboratory conditions …
electromyography developed by research groups in well-controlled laboratory conditions …
Toward robust, adaptiveand reliable upper-limb motion estimation using machine learning and deep learning–A survey in myoelectric control
To develop multi-functionalhuman-machine interfaces that can help disabled people
reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) …
reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) …
Big data in myoelectric control: large multi-user models enable robust zero-shot EMG-based discrete gesture recognition
Myoelectric control, the use of electromyogram (EMG) signals generated during muscle
contractions to control a system or device, is a promising input, enabling always-available …
contractions to control a system or device, is a promising input, enabling always-available …
Multi-grip classification-based prosthesis control with two EMG-IMU sensors
In the field of upper-limb myoelectric prosthesis control, the use of statistical and machine
learning methods has been long proposed as a means of enabling intuitive grip selection …
learning methods has been long proposed as a means of enabling intuitive grip selection …