Deep learning for EMG-based human-machine interaction: A review
D Xiong, D Zhang, X Zhao… - IEEE/CAA Journal of …, 2021 - ieeexplore.ieee.org
Electromyography (EMG) has already been broadly used in human-machine interaction
(HMI) applications. Determining how to decode the information inside EMG signals robustly …
(HMI) applications. Determining how to decode the information inside EMG signals robustly …
sEMG based human motion intention recognition
L Zhang, G Liu, B Han, Z Wang, T Zhang - Journal of Robotics, 2019 - Wiley Online Library
Human motion intention recognition is a key to achieve perfect human‐machine
coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as …
coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as …
All-weather, natural silent speech recognition via machine-learning-assisted tattoo-like electronics
The internal availability of silent speech serves as a translator for people with aphasia and
keeps human–machine/human interactions working under various disturbances. This paper …
keeps human–machine/human interactions working under various disturbances. This paper …
Classification of electromyographic hand gesture signals using machine learning techniques
The electromyogram (EMG) signals from an individual's muscles can reflect the
biomechanics of human movement. The accurate classification of individual and combined …
biomechanics of human movement. The accurate classification of individual and combined …
Classification of 41 hand and wrist movements via surface electromyogram using deep neural network
P Sri-Iesaranusorn, A Chaiyaroj, C Buekban… - … in bioengineering and …, 2021 - frontiersin.org
Surface electromyography (sEMG) is a non-invasive and straightforward way to allow the
user to actively control the prosthesis. However, results reported by previous studies on …
user to actively control the prosthesis. However, results reported by previous studies on …
Automatic EMG-based hand gesture recognition system using time-domain descriptors and fully-connected neural networks
Hand gesture recognition has numerous applications in medical (eg, prosthetics),
engineering (eg, robot manipulation) and, even, military research areas (eg, UAV control …
engineering (eg, robot manipulation) and, even, military research areas (eg, UAV control …
EMG-Based Automatic Gesture Recognition Using Lipschitz-Regularized Neural Networks
This article introduces a novel approach for building a robust Automatic Gesture Recognition
system based on Surface Electromyographic (sEMG) signals, acquired at the forearm level …
system based on Surface Electromyographic (sEMG) signals, acquired at the forearm level …
[HTML][HTML] Surface electromyography using dry polymeric electrodes
N Steenbergen, I Busha, A Morgan, C Mattathil… - APL …, 2023 - pubs.aip.org
Conventional wet Ag/AgCl electrodes are widely used in electrocardiography,
electromyography (EMG), and electroencephalography (EEG) and are considered the gold …
electromyography (EMG), and electroencephalography (EEG) and are considered the gold …
LSTM-MSA: A Novel Deep Learning Model With Dual-Stage Attention Mechanisms Forearm EMG-Based Hand Gesture Recognition
This paper introduces the Long Short-Term Memory with Dual-Stage Attention (LSTM-MSA)
model, an approach for analyzing electromyography (EMG) signals. EMG signals are crucial …
model, an approach for analyzing electromyography (EMG) signals. EMG signals are crucial …
Classification of sensory neural signals through deep learning methods
E Vasta, A Coviello, U Spagnolini… - IEEE EUROCON 2023 …, 2023 - ieeexplore.ieee.org
The recording and analysis of peripheral neural signals can be beneficial to provide
feedback to prosthetic limbs and recover the sensory functionality in people with nerve …
feedback to prosthetic limbs and recover the sensory functionality in people with nerve …