Paper Info
Title
Muscle force estimation with surface EMG during dynamic muscle contractions: a wavelet and ANN based approach.
Abstract
Human muscle force estimation is important in biomechanics studies, sports and assistive devices fields. Therefore, it is essential to develop an efficient algorithm to estimate force exerted by muscles. The purpose of this study is to predict force/torque exerted by muscles under dynamic muscle contractions based on continuous wavelet transform (CWT) and artificial neural networks (ANN) approaches. Mean frequency (MF) of the surface electromyography (EMG) signals power spectrum was calculated from CWT. ANN models were trained to derive the MF-force relationships from the subset of EMG signals and the measured forces. Then we use the networks to predict the individual muscle forces for different muscle groups. Fourteen healthy subjects (10 males and 4 females) were voluntarily recruited in this study. EMG signals were collected from the biceps brachii, triceps, hamstring and quadriceps femoris muscles to evaluate the proposed method. Root mean square errors (RMSE) and correlation coefficients between the predicted forces and measured actual forces were calculated.
Year
DOI
Venue
2013
10.1109/EMBC.2013.6610569
EMBC
Keywords
Field
DocType
torque,biomechanics,surface electromyography,wavelet based approach,wavelet transforms,dynamic muscle contractions,sports,continuous wavelet transform,artificial neural networks,medical signal processing,correlation coefficients,hamstring,quadriceps femoris muscles,root mean square errors,triceps,signal power spectrum,biceps brachii,electromyography,surface emg,force-torque exertion,human muscle force estimation,ann based approach,neural nets,correlation methods,assistive devices fields,force,time frequency analysis
Biceps,Torque,Computer science,Electromyography,Hamstring,Continuous wavelet transform,Electronic engineering,Biomechanics,Wavelet transform,Wavelet
Conference
Volume
ISSN
Citations 
2013
1557-170X
3
PageRank 
References 
Authors
0.51
2
2
Name
Order
Citations
PageRank
Fengjun Bai130.85
Chee-Meng Chew237540.58