Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/2362
Title: Estimation of wrist angle from sonomyography using support vector machine and artificial neural network models
Authors: Xie, HB
Zheng, YP 
Guo, JY
Chen, X
Shi, J
Keywords: Sonomyography (SMG)
Ultrasound
Muscle
Wrist angle prediction
Electromyography (EMG)
Least squares support vector machine (LS-SVM)
Artificial neural network (ANN)
Issue Date: Apr-2009
Publisher: Elsevier
Source: Medical engineering & physics, Apr. 2009, v. 31, no. 3, p. 384-391 How to cite?
Journal: Medical engineering & physics 
Abstract: Sonomyography (SMG) is the signal we previously termed to describe muscle contraction using real-time muscle thickness changes extracted from ultrasound images. In this paper, we used least squares support vector machine (LS-SVM) and artificial neural networks (ANN) to predict dynamic wrist angles from SMG signals. Synchronized wrist angle and SMG signals from the extensor carpi radialis muscles of five normal subjects were recorded during the process of wrist extension and flexion at rates of 15, 22.5, and 30 cycles/min, respectively. An LS-SVM model together with back-propagation (BP) and radial basis function (RBF) ANN was developed and trained using the data sets collected at the rate of 22.5 cycles/min for each subject. The established LS-SVM and ANN models were then used to predict the wrist angles for the remained data sets obtained at different extension rates. It was found that the wrist angle signals collected at different rates could be accurately predicted by all the three methods, based on the values of root mean square difference (RMSD < 0.2) and the correlation coefficient (CC > 0.98), with the performance of the LS-SVM model being significantly better (RMSD < 0.15, CC > 0.99) than those of its counterparts. The results also demonstrated that the models established for the rate of 22.5 cycles/min could be used for the prediction from SMG data sets obtained under other extension rates. It was concluded that the wrist angle could be precisely estimated from the thickness changes of the extensor carpi radialis using LS-SVM or ANN models.
URI: http://hdl.handle.net/10397/2362
ISSN: 1350-4533
DOI: 10.1016/j.medengphy.2008.05.005
Rights: Medical Engineering & Physics © 2009 Elsevier. The journal web site is located at http://www.sciencedirect.com.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Xie et al MEP Prediction of wrist angle from sonomyography.pdfPre-published version447.38 kBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

24
Last Week
0
Last month
0
Citations as of May 26, 2016

WEB OF SCIENCETM
Citations

17
Last Week
0
Last month
0
Citations as of May 26, 2016

Page view(s)

403
Last Week
3
Last month
Checked on May 22, 2016

Download(s)

240
Checked on May 22, 2016

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.