A transfer learning model for gesture recognition based on the deep features extracted by CNN
The surface electromyogram (sEMG) based hand gesture recognition is prevalent in human–
computer interface systems. However, the generalization of the recognition model does not …
computer interface systems. However, the generalization of the recognition model does not …
Gender recognition using optimal gait feature based on recursive feature elimination in normal walking
This study aims to propose a novel approach for gender recognition using best feature
subset based on recursive feature elimination (RFE) in normal walking. This study has …
subset based on recursive feature elimination (RFE) in normal walking. This study has …
Decoding hd-emg signals for myoelectric control-how small can the analysis window size be?
RN Khushaba, K Nazarpour - IEEE Robotics and Automation …, 2021 - ieeexplore.ieee.org
Recently the use of high-density electromyogram (HD-EMG) signal acquisition setups has
been promoted for myoelectric control and several databases have been made open access …
been promoted for myoelectric control and several databases have been made open access …
Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
Multivariate Time-Series (MTS) data is crucial in various application fields. With its
sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits …
sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits …
Multivariate Time-Series Representation Learning via Hierarchical Correlation Pooling Boosted Graph Neural Network
Representation learning is vital for the performance of multivariate time series (MTS)-related
tasks. Given high-dimensional MTS data, researchers generally rely on deep learning …
tasks. Given high-dimensional MTS data, researchers generally rely on deep learning …
Deep cross-user models reduce the training burden in myoelectric control
The effort, focus, and time to collect data and train EMG pattern recognition systems is one of
the largest barriers to their widespread adoption in commercial applications. In addition to …
the largest barriers to their widespread adoption in commercial applications. In addition to …
A novel multi-feature fusion network with spatial partitioning strategy and cross-attention for armband-based gesture recognition
Effectively integrating the time-space-frequency information of multi-modal signals from
armband sensor, including surface electromyogram (sEMG) and accelerometer data, is …
armband sensor, including surface electromyogram (sEMG) and accelerometer data, is …
Hand gesture recognition with acoustic myography and wavelet scattering transform
AH Al-Timemy, Y Serrestou, RN Khushaba… - IEEE …, 2022 - ieeexplore.ieee.org
In the past decade, improving upper limb prostheses control methods with pattern
recognition (PR) has been the focus of an extended amount of research. However, several …
recognition (PR) has been the focus of an extended amount of research. However, several …
Myoelectric control with fixed convolution-based time-domain feature extraction: Exploring the spatio–temporal interaction
RN Khushaba, AH Al-Timemy… - … on Human-Machine …, 2022 - ieeexplore.ieee.org
The role of feature extraction in electromyogram (EMG) based pattern recognition has
recently been emphasized with several publications promoting deep learning (DL) solutions …
recently been emphasized with several publications promoting deep learning (DL) solutions …
Electromyography-based, robust hand motion classification employing temporal multi-channel vision transformers
With an increasing use of robotic and bionic devices for the execution of everyday life,
complex tasks, Electromyography (EMG) based interfaces are being explored as candidate …
complex tasks, Electromyography (EMG) based interfaces are being explored as candidate …