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 …
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 develo** advanced data analysis and machine learning …
increased the importance of develo** advanced data analysis and machine learning …
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 …
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 …
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 …
[HTML][HTML] Machine learning-based feature extraction and classification of emg signals for intuitive prosthetic control
Signals play a fundamental role in science, technology, and communication by conveying
information through varying patterns, amplitudes, and frequencies. This paper introduces …
information through varying patterns, amplitudes, and frequencies. This paper introduces …
Improved multi-stream convolutional block attention module for sEMG-based gesture recognition
S Wang, L Huang, D Jiang, Y Sun, G Jiang… - … in Bioengineering and …, 2022 - frontiersin.org
As a key technology for the non-invasive human-machine interface that has received much
attention in the industry and academia, surface EMG (sEMG) signals display great potential …
attention in the industry and academia, surface EMG (sEMG) signals display great potential …
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 …