Estimation of wrist angle from sonomyography using support vector machine and artificial neural network models

Medical Engineering and Physics

Volumn 31, Issue 3, 2009, Pages 384-391

  Xie, Hong Bo ^a,b   Zheng, Yong Ping ^a   Guo, Jing Yi ^a   Chen, Xin ^a   Shi, Jun ^a,c  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b JIANGSU UNIVERSITY   (China)

^c SHANGHAI UNIVERSITY   (China)

Author keywords

Artificial neural network (ANN); Electromyography (EMG); Least squares support vector machine (LS SVM); Muscle; Sonomyography (SMG); Ultrasound; Wrist angle prediction

Indexed keywords

ARTIFICIAL NEURAL NETWORK (ANN); LEAST SQUARES SUPPORT VECTOR MACHINE (LS-SVM); SONOMYOGRAPHY (SMG); ULTRASOUND; WRIST ANGLE PREDICTION;

ACOUSTIC WAVES; BACKPROPAGATION; CURVE FITTING; DIFFRACTION GRATINGS; ELECTROMYOGRAPHY; IMAGE PROCESSING; IMAGE RETRIEVAL; LEAST SQUARES APPROXIMATIONS; MUSCLE; RADIAL BASIS FUNCTION NETWORKS; RESEARCH AIRCRAFT; SECURITY OF DATA; SONOCHEMISTRY; SUPPORT VECTOR MACHINES; ULTRASONIC IMAGING; ULTRASONICS; VECTORS;

NEURAL NETWORKS;

ACCURACY; ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; FEMALE; HUMAN; HUMAN EXPERIMENT; MALE; MUSCLE REFLEX; MYOGRAPHY; NORMAL HUMAN; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; WRIST;

ALGORITHMS; BIOMECHANICS; BIOMEDICAL ENGINEERING; ELECTROMYOGRAPHY; HUMANS; LEAST-SQUARES ANALYSIS; MODELS, STATISTICAL; MUSCLE, SKELETAL; NEURAL NETWORKS (COMPUTER); POSTURE; SIGNAL PROCESSING, COMPUTER-ASSISTED; TIME FACTORS; ULTRASONOGRAPHY; WRIST; WRIST JOINT;

EID: 60749088342     PISSN: 13504533     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.medengphy.2008.05.005     Document Type: Article

References (40)

1. Liu M.M., Herzog W., and Savelberg H.H. Dynamic muscle force predictions from EMG: an artificial neural network approach. J Electromyogr Kinesiol 9 (1999) 391-400
2. Sepulveda F., Wells D.M., and Vaughan C.L. A neural network representation of electromyography and joint dynamics in human gait. J Biomech 26 (1993) 101-109
3. Wang L., and Buchanan T.S. Prediction of joint moments using a neural network model of muscle activations from EMG signals. IEEE Trans Neural Syst Rehabil Eng 10 (2002) 30-37
4. Hahn M.E. Feasibility of estimating isokinetic knee torque using a neural network model. J Biomech 40 (2007) 1107-1114
5. Koike Y., and Kawato M. Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model. Biol Cybern 73 (1995) 1015-1024
6. Guo J.Y., Zheng Y.P., Huang Q.H., and Chen X. Dynamic monitoring of forearm muscles using 1D sonomyography (SMG) systems. J Rehabil Res Dev 45 (2008) 187-196
7. Shi J., Zheng Y.P., Yan Z.Z., and Huang Q.H. Prediction of wrist angle from sonomyography signals with artificial neural networks technique. Proceedings of the 28th annual international conference of the IEEE engineering in medicine and biology society (2006) 3549-3552
8. Shi J., Zheng Y.P., Chen X., and Huang Q.H. Assessment of muscle fatigue using sonomyography: muscle thickness change detected from ultrasound images. Med Eng Phys 29 (2007) 472-479
9. Zheng Y.P., Chan M.M.F., Shi J., Chen X., and Huang Q.H. Sonomyography: monitoring morphological changes of forearm muscles in actions with the feasibility for the control of powered prosthesis. Med Eng Phys 28 (2006) 405-415
10. Shi J., Zheng Y.P., Chen X., and Huang Q.H. Relationships among continuous sonomyography, electromyography and torque generated by normal upper arm muscles during isometric contraction. IEEE Trans Biomed Eng 55 (2008) 1191-1198
11. Luh J.J., Chang G.C., Cheng C.K., Lai J.S., and Kuo T.S. Isokinetic elbow joint torques estimation from surface EMG and joint kinematic data: using an artificial neural network model. J Electromyogr Kinesiol 9 (1999) 173-183
12. Au A.T.C., and Kirsch R.F. EMG-based prediction of shoulder and elbow kinematics in able-bodied and spinal cord injured individuals. IEEE Trans Rehabil Eng 8 (2000) 471-480
13. Dipietro L., Sabatini A.M., and Dario P. Artificial neural network model of the mapping between electromyographic activation and trajectory patterns in free-arm movements. Med Biol Eng Comput 41 (2003) 124-132
14. Vapnik V. Estimation of dependencies based on empirical data (1982), Springer, Berlin
15. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2 (1998) 121-167
16. Smola A.J., and Scholkopf B. A tutorial on support vector regression. Stat Comput 14 (2004) 199-222
17. Güler N.F., and Kocer S. Use of support vector machines and neural network in diagnosis of neuromuscular disorders. J Med Syst 29 (2005) 271-284
18. Xie H.B., Wang Z.H., Huang H., and Qin C. Support vector machine in computer aided clinical electromyography. Proceedings of the second international conference on machine learning and cybernetics. Xi'an (2003) 1106-1108
19. Huang Y.L., and Chen D.R. Support vector machines in sonography application to decision making in the diagnosis of breast cancer. J Clin Imaging 29 (2005) 179-184
20. Mohandes M.A., Halawani T.O., Rehman S., and Hussain A.A. Support vector machines for wind speed prediction. Renew Energ 29 (2004) 939-947
21. Shin K.S., Leel T.S., and Kim H.J. An application of support vector machines in bankruptcy prediction model. Expert Syst Appl 28 (2005) 127-135
22. Tay F.E.H., and Cao L. Application of support vector machines in financial time series forecasting. Omega 29 (2001) 309-317
23. Vonga C.M., Wong P.K., and Li Y.P. Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference. Eng Appl Artif Intell 19 (2006) 277-287
24. Yu X., and Liong S.Y. Forecasting of hydrologic time series with ridge regression in feature space. J Hydrol 332 (2007) 290-302
25. Cristianini N., and Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods (2000), Cambridge University Press, Cambridge, UK
26. Suykens J.A.K., Gestel T.V., Brabanter J.D., Moor B.D., and Vandewalle J. Least squares support vector machines (2002), World Scientific Publishing, Singapore
27. Hornik K., Stinchcommbe M., and White H. Multilayer feedforward networks are universal approximators. Neural Netw 2 (1989) 359-366
28. Haykin S. Neural networks, a comprehensive foundation. 2nd ed. (1999), Prentice-Hall, Upper Saddle River, NJ
29. Kasabov K. Foundations of neural network fuzzy systems and knowledge engineering (1996), MIT Press
30. Powell M.J.D. The theory of radial basis functions approximation, in advance of numerical analysis (1992), Oxd Charendon Press p. 105-210
31. Castillo-Valdivieso P.A., Merelo J.J., Prieto A., Rojas I., and Romero G. Statistical analysis of the parameters of a neuro-genetic algorithm. IEEE Trans Neural Netw 13 (2002) 1374-1394
32. Wang G., Yan Z.G., Hu X., Xie H.B., and Wang Z.Z. Classification of surface EMG signals using harmonic wavelet packet transform. Physiol Meas 27 (2006) 1255-1267
33. Suryanarayanan S., Reddy N.P., and Gupta V. Artificial neural networks for estimation of joint angle from EMG signals. IEEE 17th annual conference of engineering in medicine and biology society, vol. 1 (1995) 823-824
34. Van Gestel T., Suykens J.A.K., Baestaens D.E., Lambrechts D., Lanckriet A., Lanckriet G., et al. Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Trans Neural Netw Spec Issue Financ Eng 12 (2001) 809-821
35. Erfanian A., Chizeck H.J., and Hashemi R.M. The relationship between joint angle and evoked EMG in electrically stimulated muscle. Proceedings of the 16th annual international conference of the IEEE engineering in medicine and biology, vol. 1 (1994) 345-346
36. Leedham J.S., and Dowling J.J. Force-length, torque-angle and EMG-joint angle relationships of the human in vivo biceps brachii. Eur J Appl Physiol 70 (1995) 421-426
37. Vredenbregt J., and Rau G. Surface electromyography in relation to force, muscle length and endurance. In: Desmedt J.E. (Ed). New developments in electromyography and clinical neurophysiology vol. 1 (1973), S. Karger, Basel 607-622
38. Hodges P.W., Pengel L.H.M., and Herbert R.D. Measurement of muscle contraction with ultrasound imaging. Muscle Nerve 27 (2003) 682-692
39. Crago P.E., Memberg W.D., Usey M.K., Keith M.W., Kirsch R.F., Chapman G.J., et al. An elbow extension neuroprosthesis for individuals with tetraplegia. IEEE Trans Rehabil Eng 6 (1998) 1-6
40. Hart R.L., Kilgore K.L., and Peckham P.H. A comparison between control methods for implanted FES hand-grasp systems. IEEE Trans Rehabil Eng 6 (1998) 208-218