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 …
Surface electromyography and artificial intelligence for human activity recognition-A systematic review on methods, emerging trends applications, challenges, and …
Human activity recognition (HAR) has become increasingly popular in recent years due to its
potential to meet the growing needs of various industries. Electromyography (EMG) is …
potential to meet the growing needs of various industries. Electromyography (EMG) is …
MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning
Objective. This paper proposes machine learning models for mapping surface
electromyography (sEMG) signals to regression of joint angle, joint velocity, joint …
electromyography (sEMG) signals to regression of joint angle, joint velocity, joint …
Explainable and robust deep forests for EMG-force modeling
Machine and deep learning techniques have received increasing attentions in estimating
finger forces from high-density surface electromyography (HDsEMG), especially for neural …
finger forces from high-density surface electromyography (HDsEMG), especially for neural …
Electromyography based decoding of dexterous, in-hand manipulation motions with temporal multichannel vision transformers
Electromyography (EMG) signals have been used in designing muscle-machine interfaces
(MuMIs) for various applications, ranging from entertainment (EMG controlled games) to …
(MuMIs) for various applications, ranging from entertainment (EMG controlled games) to …
On emg based dexterous robotic telemanipulation: Assessing machine learning techniques, feature extraction methods, and shared control schemes
Electromyography (EMG) signals are commonly used for the development of Muscle
Machine Interfaces. EMG-based solutions provide intuitive and often hand-free control in a …
Machine Interfaces. EMG-based solutions provide intuitive and often hand-free control in a …
Simultaneous estimation of grip force and wrist angles by surface electromyography and acceleration signals
In myoelectric control, simultaneous and proportional (SP) control of multiple degrees of
freedom (DOFs) can realize a high level of dexterity. This study proposed a new control …
freedom (DOFs) can realize a high level of dexterity. This study proposed a new control …
On lightmyography based muscle-machine interfaces for the efficient decoding of human gestures and forces
Conventional muscle-machine interfaces like Electromyography (EMG), have significant
drawbacks, such as crosstalk, a non-linear relationship between the signal and the …
drawbacks, such as crosstalk, a non-linear relationship between the signal and the …
Real-Time sEMG Pattern Recognition of Multiple-Mode Movements for Artificial Limbs Based on CNN-RNN Algorithm
S Li, Y Zhang, Y Tang, W Li, W Sun, H Yu - Electronics, 2023 - mdpi.com
Currently, sEMG-based pattern recognition is a crucial and promising control method for
prosthetic limbs. A 1D convolutional recurrent neural network classification model for …
prosthetic limbs. A 1D convolutional recurrent neural network classification model for …
Continuous estimation of finger and wrist joint angles using a muscle synergy based musculoskeletal model
Recently, many muscle synergy-based human motion prediction models and algorithms
have been proposed. In this study, the muscle synergies extracted from electromyography …
have been proposed. In this study, the muscle synergies extracted from electromyography …