Multiday EMG-based classification of hand motions with deep learning techniques
Pattern recognition of electromyography (EMG) signals can potentially improve the
performance of myoelectric control for upper limb prostheses with respect to current clinical …
performance of myoelectric control for upper limb prostheses with respect to current clinical …
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 …
Feature extraction and selection for myoelectric control based on wearable EMG sensors
A Phinyomark, R N. Khushaba, E Scheme - Sensors, 2018 - mdpi.com
Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent
advancements in wearable sensors, wireless communication and embedded technologies …
advancements in wearable sensors, wireless communication and embedded technologies …
Performance evaluation of convolutional neural network for hand gesture recognition using EMG
Electromyography (EMG) is a measure of electrical activity generated by the contraction of
muscles. Non-invasive surface EMG (sEMG)-based pattern recognition methods have …
muscles. Non-invasive surface EMG (sEMG)-based pattern recognition methods have …
Stacked sparse autoencoders for EMG-based classification of hand motions: A comparative multi day analyses between surface and intramuscular EMG
Advances in myoelectric interfaces have increased the use of wearable prosthetics including
robotic arms. Although promising results have been achieved with pattern recognition-based …
robotic arms. Although promising results have been achieved with pattern recognition-based …
Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning
Myoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and
prehensile abilities to ease rehabilitation and daily life activities. However, studies with …
prehensile abilities to ease rehabilitation and daily life activities. However, studies with …
EMG-based hand gesture classification with long short-term memory deep recurrent neural networks
M Jabbari, RN Khushaba… - 2020 42nd Annual …, 2020 - ieeexplore.ieee.org
Electromyogram (EMG) pattern recognition has been utilized with the traditional machine
and deep learning architectures as a control strategy for upper-limb prostheses. However …
and deep learning architectures as a control strategy for upper-limb prostheses. However …
Resolving the adverse impact of mobility on myoelectric pattern recognition in upper-limb multifunctional prostheses
Electromyogram pattern recognition (EMG-PR) based control for upper-limb prostheses
conventionally focuses on the classification of signals acquired in a controlled laboratory …
conventionally focuses on the classification of signals acquired in a controlled laboratory …
[HTML][HTML] Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on convolutional neural network
Hand movement classification based on surface electromyography (sEMG) pattern
recognition is a promising approach for upper limb neuroprosthetic control. However …
recognition is a promising approach for upper limb neuroprosthetic control. However …
Current trends and confounding factors in myoelectric control: Limb position and contraction intensity
This manuscript presents a hybrid study of a comprehensive review and a systematic
(research) analysis. Myoelectric control is the cornerstone of many assistive technologies …
(research) analysis. Myoelectric control is the cornerstone of many assistive technologies …