A survey on hand gesture recognition based on surface electromyography: Fundamentals, methods, applications, challenges and future trends
S Ni, MAA Al-qaness, A Hawbani, D Al-Alimi… - Applied Soft …, 2024 - Elsevier
Hand gestures are crucial for developing prosthetic and rehabilitation devices, enabling
intuitive human–computer interaction (HCI) and improving accessibility for individuals with …
intuitive human–computer interaction (HCI) and improving accessibility for individuals with …
Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data
Many machine learning-based studies have been carried out in the literature for the
detection of brain tumors using MRI data and most of what has been done in the last 6 years …
detection of brain tumors using MRI data and most of what has been done in the last 6 years …
A deep learning approach using attention mechanism and transfer learning for electromyographic hand gesture estimation
Y Wang, P Zhao, Z Zhang - Expert Systems with Applications, 2023 - Elsevier
Accurate surface electromyography decoding of hand gestures is pivotal for advancing
human–computer interaction applications. Recent developments in end-to-end deep neural …
human–computer interaction applications. Recent developments in end-to-end deep neural …
Application of min-max normalization on subject-invariant EMG pattern recognition
Surface electromyography (EMG) is one of the promising signals for the recognition of the
intended hand movement of an amputee. Nevertheless, there are several barriers to its …
intended hand movement of an amputee. Nevertheless, there are several barriers to its …
A federated transfer learning approach for surface electromyographic hand gesture recognition with emphasis on privacy preservation
Z Zhang, Y Ming, Y Wang - Engineering Applications of Artificial …, 2024 - Elsevier
Recently, surface electromyographic (sEMG) hand gesture recognition faces a serious
challenge of limited training data in various scenarios. Numerous efforts have been made to …
challenge of limited training data in various scenarios. Numerous efforts have been made to …
Online cross session electromyographic hand gesture recognition using deep learning and transfer learning
Z Zhang, S Liu, Y Wang, W Song, Y Zhang - Engineering Applications of …, 2024 - Elsevier
In recent years, hand gesture recognition in human-computer interfaces is usually based on
surface electromyography because the signals are non-intrusive and are not affected by the …
surface electromyography because the signals are non-intrusive and are not affected by the …
Transfer learning in hand movement intention detection based on surface electromyography signals
R Soroushmojdehi, S Javadzadeh… - Frontiers in …, 2022 - frontiersin.org
Over the past several years, electromyography (EMG) signals have been used as a natural
interface to interact with computers and machines. Recently, deep learning algorithms such …
interface to interact with computers and machines. Recently, deep learning algorithms such …
Multi-source domain generalization and adaptation toward cross-subject myoelectric pattern recognition
Objective. Myoelectric pattern recognition (MPR) has shown satisfactory performance under
ideal laboratory conditions. Nevertheless, the individual variances lead to dramatic …
ideal laboratory conditions. Nevertheless, the individual variances lead to dramatic …
Transfer learning on electromyography (EMG) tasks: approaches and beyond
Machine learning on electromyography (EMG) has recently achieved remarkable success
on various tasks, while such success relies heavily on the assumption that the training and …
on various tasks, while such success relies heavily on the assumption that the training and …
One-shot random forest model calibration for hand gesture decoding
Objective. Most existing machine learning models for myoelectric control require a large
amount of data to learn user-specific characteristics of the electromyographic (EMG) signals …
amount of data to learn user-specific characteristics of the electromyographic (EMG) signals …