Intelligent EMG pattern recognition control method for upper-limb multifunctional prostheses: advances, current challenges, and future prospects

OW Samuel, MG Asogbon, Y Geng… - Ieee …, 2019 - ieeexplore.ieee.org
Upper-limb amputation imposes significant burden on amputees thereby restricting them
from fully exploring their environments during activities of daily living. The use of intelligent …

[HTML][HTML] ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost

S Thongsuwan, S Jaiyen, A Padcharoen… - Nuclear Engineering and …, 2021 - Elsevier
We describe a new deep learning model-Convolutional eXtreme Gradient Boosting
(ConvXGB) for classification problems based on convolutional neural nets and Chen et al.'s …

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 …

The new generation brain-inspired sparse learning: A comprehensive survey

L Jiao, Y Yang, F Liu, S Yang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In recent years, the enormous demand for computing resources resulting from massive data
and complex network models has become the limitation of deep learning. In the large-scale …

Surface electromyography (EMG) signal processing, classification, and practical considerations

A Phinyomark, E Campbell, E Scheme - Biomedical Signal Processing …, 2020 - Springer
Electromyography (EMG) is the process of measuring the electrical activity produced by
muscles throughout the body using electrodes on the surface of the skin or inserted in the …

E2cnn: An efficient concatenated cnn for classification of surface emg extracted from upper limb

MF Qureshi, Z Mushtaq, MZU Rehman… - IEEE Sensors …, 2023 - ieeexplore.ieee.org
Surface electromyography is a bioelectrical indicator that emerges during muscle
contraction and has been widely used in a variety of clinical applications. Several prosthetic …

Toward ML-based energy-efficient mechanism for 6G enabled industrial network in box systems

AH Sodhro, N Zahid, L Wang… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Machine learning (ML) techniques in association to emerging sixth generation (6G)
technologies, ie, massive Internet of Things (IoT), big data analytics have caught too much …

Towards resolving the co-existing impacts of multiple dynamic factors on the performance of EMG-pattern recognition based prostheses

MG Asogbon, OW Samuel, Y Geng… - Computer methods and …, 2020 - Elsevier
Abstract Background and Objective Mobility of subject (MoS) and muscle contraction force
variation (MCFV) have been shown to individually degrade the performance of multiple …

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 …

Appropriate feature set and window parameters selection for efficient motion intent characterization towards intelligently smart EMG-PR system

MG Asogbon, OW Samuel, Y Jiang, L Wang, Y Geng… - Symmetry, 2020 - mdpi.com
The constantly rising number of limb stroke survivors and amputees has motivated the
development of intelligent prosthetic/rehabilitation devices for their arm function restoration …