Results 191 to 200 of about 39,692 (260)

Continuous online prediction of lower limb joints angles based on sEMG signals by deep learning approach

Comput. Biol. Medicine, 2023
Continuous online prediction of human joints angles is a key point to improve the performance of man-machine cooperative control. In this study, a framework of online prediction method of joints angles by long short-term memory (LSTM) neural network only
Qiuzhi Song, Xunju Ma, Yali Liu
semanticscholar   +1 more source

Tutorial. Surface electromyogram (sEMG) amplitude estimation: Best practices.

Journal of Electromyography & Kinesiology, 2023
This tutorial intends to provide insight, instructions and "best practices" for those who are novices-including clinicians, engineers and non-engineers-in extracting electromyogram (EMG) amplitude from the bipolar surface EMG (sEMG) signal of voluntary ...
E. Clancy, E. Morin, G. Hajian, R. Merletti   +3 more
semanticscholar   +1 more source

Advances and Disturbances in sEMG-Based Intentions and Movements Recognition: A Review

IEEE Sensors Journal, 2021
Surface EMG-based gestures recognition systems are helping the disable to enjoy a better life. Academic institutes and commercial companies have been developing a lot of sEMG-based prosthesis, exoskeletons and rehabilitation systems.
Hao Xu
exaly   +2 more sources

Subject-Independent Continuous Estimation of sEMG-Based Joint Angles Using Both Multisource Domain Adaptation and BP Neural Network

IEEE Transactions on Instrumentation and Measurement, 2023
Continuous angle estimation from surface electromyography (sEMG) is crucial for robot-assisted upper limb rehabilitation. The sEMG-based control provides an optimal way to achieve harmonic interactions between subjects and upper limb rehabilitation ...
Hengrui Li, Shuxiang Guo, Hanze Wang, Dongdong Bu   +3 more
semanticscholar   +1 more source

Automatic selection of IMFs to denoise the sEMG signals using EMD.

Journal of Electromyography & Kinesiology, 2023
Surface Electromyography (sEMG) signals are muscle activation signals, which has applications in muscle diagnosis, rehabilitation, prosthetics, and speech etc. However, they are known to be affected by noises such as Power Line Interference (PLI), motion
Pratap Kumar Koppolu, K. Chemmangat
semanticscholar   +1 more source

A review of the key technologies for sEMG-based human-robot interaction systems

Biomedical Signal Processing and Control, 2020
As physiological signals that are closely related to human motion, surface electromyography (sEMG) signals have been widely used in human-robot interaction systems (HRISs).
Kexiang Li
exaly   +2 more sources

sEMG and IMU Data-Based Hand Gesture Recognition Method Using Multistream CNN With a Fine-Tuning Transfer Framework

IEEE Sensors Journal, 2023
Interfaces based on surface electromyography (sEMG) and inertial measurement units (IMUs) enable users to interact with computers in a natural and intuitive way through hand gestures.
Guiyin Li, Bo Wan, Kejia Su, Jiwang Huo, Changhua Jiang, Fei Wang   +5 more
semanticscholar   +1 more source

Toward Generalization of sEMG-Based Pattern Recognition: A Novel Feature Extraction for Gesture Recognition

IEEE Transactions on Instrumentation and Measurement, 2022
Gesture recognition via surface electromyography (sEMG) has drawn significant attention in the field of human–computer interaction. An important factor limiting the performance of sEMG-based pattern recognition (PR) is the generalization ability which ...
Cheng Shen, Z. Pei, Weihai Chen, Jianhua Wang, Jianbin Zhang, Zuobing Chen   +5 more
semanticscholar   +1 more source

SEMG Evaluations: An Overview

Applied Psychophysiology and Biofeedback, 2003
This article reviews the current techniques of surface electromyography (SEMG) assessment. Discussed are static, dynamic, and combination assessment techniques and the rational for their use.
Stuart, Donaldson, Mary, Donaldson, Leslie, Snelling   +2 more
openaire   +2 more sources

sEMG-Based Gesture Recognition Using Temporal History

IEEE Transactions on Biomedical Engineering, 2023
Surface electromyography (sEMG) patterns have been decoded using learning-based methods that determine complicated nonlinear decision boundaries. However, overlapping classes in sEMG pattern recognition still degrade the classification accuracy because they cannot be separated by the decision boundaries. We hypothesized that certain overlapping classes
Chaerin Hong, Seongsik Park, Keehoon Kim
openaire   +2 more sources