Classification of Simultaneous, Dynamic Motions with Surface EMG

2013 ◽  
pp. 49-52
Author(s):  
Jakob C. Rosenvang ◽  
Ronnie W. Horup ◽  
Kevin Englehart ◽  
Winnie Jensen ◽  
Ernest N. Kamavuako
Keyword(s):  
Surface Emg

Entropy analysis of surface EMG for classification of face movements

2015 ◽  
Author(s):  
Cagdas Topcu ◽  
Arzu Akgul ◽  
Merve Bedeloglu ◽  
Ela Naz Doger ◽  
Refik Sever ◽  
...  
Keyword(s):  
Surface Emg ◽  
Entropy Analysis

scholarly journals Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders

2010 ◽  
Vol 48 (8) ◽  
pp. 773-781 ◽  
Author(s):  
Rok Istenič ◽  
Prodromos A. Kaplanis ◽  
Constantinos S. Pattichis ◽  
Damjan Zazula
Keyword(s):  
Neuromuscular Disorders ◽  
Surface Emg ◽  
Multiscale Entropy

Classification Of Muscular Strength During Palmar Grasp Exercises Using Surface EMG Signals

2018 ◽  
Author(s):  
Juan C. Yepes ◽  
Alvaro J. Saldarriaga ◽  
Vanessa Montoya-Leal ◽  
Andres Orozco-Duque ◽  
Vera Z. Perez ◽  
...  
Keyword(s):  
Muscular Strength ◽  
Surface Emg

A novel approach for classification of hand movements using surface EMG signals

2017 ◽  
Author(s):  
Muhammad Zia ur Rehman ◽  
Syed Omer Gilani ◽  
Asim Waris ◽  
Imran Khan Niazi ◽  
Ernest Nlandu Kamavuako
Keyword(s):  
Surface Emg ◽  
Hand Movements ◽  
Novel Approach

scholarly journals Concurrent surface electromyography and force myography classification during times of prosthetic socket shift and user fatigue

2017 ◽  
Vol 4 ◽  
pp. 205566831770873 ◽  
Author(s):  
Joe Sanford ◽  
Rita Patterson ◽  
Dan O Popa
Keyword(s):  
Surface Electromyography ◽  
Muscle Activation ◽  
Surface Emg ◽  
Training Data ◽  
Control Input ◽  
Prosthetic Devices ◽  
Prosthetic Socket ◽  
User Fatigue ◽  
Force Myography

Objective Surface electromyography has been a long-standing source of signals for control of powered prosthetic devices. By contrast, force myography is a more recent alternative to surface electromyography that has the potential to enhance reliability and avoid operational challenges of surface electromyography during use. In this paper, we report on experiments conducted to assess improvements in classification of surface electromyography signals through the addition of collocated force myography consisting of piezo-resistive sensors. Methods Force sensors detect intrasocket pressure changes upon muscle activation due to changes in muscle volume during activities of daily living. A heterogeneous sensor configuration with four surface electromyography–force myography pairs was investigated as a control input for a powered upper limb prosthetic. Training of two different multilevel neural perceptron networks was employed during classification and trained on data gathered during experiments simulating socket shift and muscle fatigue. Results Results indicate that intrasocket pressure data used in conjunction with surface EMG data can improve classification of human intent and control of a powered prosthetic device compared to traditional, surface electromyography only systems. Significance Additional sensors lead to significantly better signal classification during times of user fatigue, poor socket fit, as well as radial and ulnar wrist deviation. Results from experimentally obtained training data sets are presented.

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