Deep learning for healthcare applications based on physiological signals: A review
Background and objective: We have cast the net into the ocean of knowledge to retrieve the
latest scientific research on deep learning methods for physiological signals. We found 53 …
latest scientific research on deep learning methods for physiological signals. We found 53 …
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 …
A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition
Wearable devices that monitor muscle activity based on surface electromyography could be
of use in the development of hand gesture recognition applications. Such devices typically …
of use in the development of hand gesture recognition applications. Such devices typically …
Deep learning for electromyographic hand gesture signal classification using transfer learning
U Côté-Allard, CL Fall, A Drouin… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
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 …
A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition
The surface electromyography (sEMG)-based gesture recognition with deep learning
approach plays an increasingly important role in human-computer interaction. Existing deep …
approach plays an increasingly important role in human-computer interaction. Existing deep …
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 …
Gesture recognition by instantaneous surface EMG images
Gesture recognition in non-intrusive muscle-computer interfaces is usually based on
windowed descriptive and discriminatory surface electromyography (sEMG) features …
windowed descriptive and discriminatory surface electromyography (sEMG) features …
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 …
EMGHandNet: A hybrid CNN and Bi-LSTM architecture for hand activity classification using surface EMG signals
NK Karnam, SR Dubey, AC Turlapaty… - Biocybernetics and …, 2022 - Elsevier
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 …
Comparison of six electromyography acquisition setups on hand movement classification tasks
Hand prostheses controlled by surface electromyography are promising due to the non-
invasive approach and the control capabilities offered by machine learning. Nevertheless …
invasive approach and the control capabilities offered by machine learning. Nevertheless …