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 …
Control of upper limb prostheses: Terminology and proportional myoelectric control—A review
The recent introduction of novel multifunction hands as well as new control paradigms
increase the demand for advanced prosthetic control systems. In this context, an …
increase the demand for advanced prosthetic control systems. In this context, an …
Deep learning for electromyographic hand gesture signal classification using transfer learning
In recent years, deep learning algorithms have become increasingly more prominent for
their unparalleled ability to automatically learn discriminant features from large amounts of …
their unparalleled ability to automatically learn discriminant features from large amounts of …
Electromyography data for non-invasive naturally-controlled robotic hand prostheses
Recent advances in rehabilitation robotics suggest that it may be possible for hand-
amputated subjects to recover at least a significant part of the lost hand functionality. The …
amputated subjects to recover at least a significant part of the lost hand functionality. The …
Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands
Natural control methods based on surface electromyography (sEMG) and pattern
recognition are promising for hand prosthetics. However, the control robustness offered by …
recognition are promising for hand prosthetics. However, the control robustness offered by …
Hand gesture recognition using compact CNN via surface electromyography signals
L Chen, J Fu, Y Wu, H Li, B Zheng - Sensors, 2020 - mdpi.com
By training the deep neural network model, the hidden features in Surface
Electromyography (sEMG) signals can be extracted. The motion intention of the human can …
Electromyography (sEMG) signals can be extracted. The motion intention of the human can …
Characterization of a benchmark database for myoelectric movement classification
In this paper, we characterize the Ninapro database and its use as a benchmark for hand
prosthesis evaluation. The database is a publicly available resource that aims to support …
prosthesis evaluation. The database is a publicly available resource that aims to support …
A survey of sensor fusion methods in wearable robotics
Modern wearable robots are not yet intelligent enough to fully satisfy the demands of end-
users, as they lack the sensor fusion algorithms needed to provide optimal assistance and …
users, as they lack the sensor fusion algorithms needed to provide optimal assistance and …
A subject-transfer framework based on single-trial EMG analysis using convolutional neural networks
In recent years, electromyography (EMG)-based practical myoelectric interfaces have been
developed to improve the quality of daily life for people with physical disabilities. With these …
developed to improve the quality of daily life for people with physical disabilities. With these …
Building the Ninapro database: A resource for the biorobotics community
This paper is about (self-powered) advanced hand prosthetics and their control via surface
electromyography (sEMG). We hereby introduce to the biorobotics community the first …
electromyography (sEMG). We hereby introduce to the biorobotics community the first …