[HTML][HTML] EMG pattern recognition in the era of big data and deep learning
A Phinyomark, E Scheme - Big Data and Cognitive Computing, 2018 - mdpi.com
The increasing amount of data in electromyographic (EMG) signal research has greatly
increased the importance of developing advanced data analysis and machine learning …
increased the importance of developing advanced data analysis and machine learning …
Surface electromyography as a natural human–machine interface: a review
Surface electromyography (sEMG) is a non-invasive method of measuring neuromuscular
potentials generated when the brain instructs the body to perform both fine and coarse …
potentials generated when the brain instructs the body to perform both fine and coarse …
Communication-efficient federated learning
Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …
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 …
[HTML][HTML] Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation
High-density surface electromyography (HD-sEMG) is to record muscles' electrical activity
from a restricted area of the skin by using two dimensional arrays of closely spaced …
from a restricted area of the skin by using two dimensional arrays of closely spaced …
[HTML][HTML] 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 …
FS-HGR: Few-shot learning for hand gesture recognition via electromyography
This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their
widespread applications in human-machine interfaces. DNNs have been recently used for …
widespread applications in human-machine interfaces. DNNs have been recently used for …
A myoelectric prosthetic hand with muscle synergy–based motion determination and impedance model–based biomimetic control
A Furui, S Eto, K Nakagaki, K Shimada, G Nakamura… - Science Robotics, 2019 - science.org
Prosthetic hands are prescribed to patients who have suffered an amputation of the upper
limb due to an accident or a disease. This is done to allow patients to regain functionality of …
limb due to an accident or a disease. This is done to allow patients to regain functionality of …
[HTML][HTML] putEMG—a surface electromyography hand gesture recognition dataset
In this paper, we present a putEMG dataset intended for the evaluation of hand gesture
recognition methods based on sEMG signal. The dataset was acquired for 44 able-bodied …
recognition methods based on sEMG signal. The dataset was acquired for 44 able-bodied …
Dynamic gesture recognition based on LSTM-CNN
Y Wu, B Zheng, Y Zhao - 2018 Chinese Automation Congress …, 2018 - ieeexplore.ieee.org
The current research on using surface electromyography (sEMG) for gesture recognition
mainly focuses on designing EMG signal features, decent feature designs can significantly …
mainly focuses on designing EMG signal features, decent feature designs can significantly …