Support vector regression for improved real-time, simultaneous myoelectric control

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Volumn 22, Issue 6, 2014, Pages 1198-1209

  Ameri, Ali ^a   Kamavuako, Ernest N ^b   Scheme, Erik J ^a   Englehart, Kevin B ^a   Parker, Philip A ^a  

^a UNIVERSITY OF NEW BRUNSWICK   (Canada)

^b AALBORG UNIVERSITY   (Denmark)

Author keywords

Amputee; electromyogram (EMG); myoelectric control; simultaneous control; support vector machines

Indexed keywords

DEGREES OF FREEDOM (MECHANICS); NEURAL NETWORKS; REAL TIME CONTROL; THROUGHPUT;

AMPUTEE; ELECTROMYOGRAM; MULTIPLE DEGREES OF FREEDOM; MYOELECTRIC CONTROL; PERFORMANCE MEASURE; SIMULTANEOUS CONTROL; SUPPORT VECTOR REGRESSION (SVR); WRIST FLEXION-EXTENSION;

SUPPORT VECTOR MACHINES;

ADULT; AUTOMATED PATTERN RECOGNITION; COMPUTER SYSTEM; ELECTROMYOGRAPHY; FEMALE; HUMAN; MALE; MIDDLE AGED; MOVEMENT (PHYSIOLOGY); MUSCLE CONTRACTION; PATHOPHYSIOLOGY; PROCEDURES; PSYCHOPHYSIOLOGY; REGRESSION ANALYSIS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SKELETAL MUSCLE; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE; YOUNG ADULT;

ADULT; BIOFEEDBACK, PSYCHOLOGY; COMPUTER SYSTEMS; DATA INTERPRETATION, STATISTICAL; ELECTROMYOGRAPHY; FEMALE; HUMANS; MALE; MIDDLE AGED; MOVEMENT; MUSCLE CONTRACTION; MUSCLE, SKELETAL; PATTERN RECOGNITION, AUTOMATED; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINES; YOUNG ADULT;

EID: 84912137962     PISSN: 15344320     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNSRE.2014.2323576     Document Type: Article

References (48)

1. C. Cipriani, C. Antfolk, M. Controzzi, G. Lundborg, B. Rosen, M. C. Carrozza, and F. Sebelius, "Online myoelectric control of a dexterous hand prosthesis by transradial amputees," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 19, no. 3, pp. 260-270, Jun. 2011.
2. P. K. Artemiadis and K. J. Kyriakopoulos, "An EMG-based robot control scheme robust to time-varying EMG signal features," IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 3, pp. 582-588, May 2010.
3. J.M. Lambrecht, C. L. Pulliam, and R. F. Kirsch, "Virtual reality environment for simulating tasks with a myoelectric prosthesis: An assessment and training tool," J. Prosthet. Orthot., vol. 23, no. 2, pp. 89-94, Apr. 2011.
4. P. A. Parker, K. B. Englehart, and B. Hudgins, "Myoelectric signal processing for control of powered limb prostheses," J. Electromyogr. Kinesiol., vol. 16, no. 6, pp. 541-548, 2006.
5. D. S. Dorcas and R. N. Scott, "A three-state myoelectric controller," Med. Biol. Eng., vol. 4, pp. 367-372, 1966.
6. D. Childress, "A myoelectric three state controller using rate sensitivity," presented at the Int. Conf.Math. Biology and Ecology, Chicago, IL, 1969.
7. T.W.Williams, "Practical methods for controlling powered upper-extremity prostheses," Assist. Technol., vol. 2, no. 3, pp. 3-18, 1990.
8. B. Hudgins, P. A. Parker, and R. N. Scott, "A new strategy for multifunction myoelectric control," IEEE Trans. Biomed. Eng., vol. 40, no. 1, pp. 82-94, Jan. 1993.
9. K. B. Englehart, B. Hudgins, P. A. Parker, and M. Stevenson, "Classification of the myoelectric signal using time-frequency based representations," Med. Eng. Phys., vol. 21, no. 6-7, pp. 431-438, Jul.-Sep. 1999.
10. Y. Huang, K. B. Englehart, B. Hudgins, and A. D. C. Chan, "Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses," IEEE Trans. Biomed. Eng., vol. 52, no. 11, pp. 1801-1811, Nov. 2005.
11. M. F. Lucas, A. Gaufriau, S. Pascual, C. Doncarli, and D. Farina, "Multichannel surface EMG classification using support vector machines and signal-based wavelet optimization," Biomed. Signal Process. Control, vol. 3, no. 2, pp. 169-174, Apr. 2008.
12. L. Hargrove, E. Scheme, K. Englehart, and B. Hudgins, "Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 18, no. 1, pp. 49-57, Feb. 2010.
13. E. Scheme and K. Englehart, "Training strategies for mitigating the effect of proportional control on classification in pattern recognitionbased myoelectric control," J. Prosthet. Orthot., vol. 25, no. 2, pp. 76-83, Apr. 2013.
14. A. J. Young, L. H. Smith, E. J. Rouse, and L. J. Hargrove, "A new hierarchical approach for simultaneous control of multi-joint powered prostheses," in IEEE RAS EMBS Int. Conf. BioRob., 2012, pp. 514-520.
15. E. Kamavuako, J. Rosenvang, R. Horup, W. Jensen, D. Farina, and K. Englehart, "Surface versus untargeted intramuscular EMG based classification of simultaneous and dynamically changing movements," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 21, no. 6, pp. 992-998, Nov. 2013.
16. J. L. Nielsen, S. Holmgaard, N. Jiang, K. B. Englehart, D. Farina, and P. A. Parker, "Simultaneous and proportional force estimation for multifunction myoelectric prostheses using mirrored bilateral training," IEEE Trans. Biomed. Eng., vol. 58, no. 3, Mar. 2011.
17. E. N. Kamavuako, D. Farina, K. Yoshida, and W. Jensen, "Estimation of grasping force from features of intramuscular EMG signals with mirrored bilateral training," Ann. Biomed. Eng., vol. 40, no. 3, pp. 648-656, Mar. 2012.
18. F. Sebelius, L. Eriksson, C. Balkenius, and T. Laurell, "Myoelectric control of a computer animated hand: A new concept based on the combined use of a tree-structured artificial neural network and a data glove," J. Med. Eng. Tech., vol. 30, no. 1, pp. 2-10, Jan.-Feb 2006.
19. S. Muceli and D. Farina, "Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees of freedom," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 20, no. 3, pp. 371-378, May 2012.
20. N. Jiang, J. L. Vest-Nielsen, S. Muceli, and D. Farina, "EMG-based simultaneous and proportional estimation of wrist/hand unconstraineds in uni-lateral trans-radial amputees," J. Neuro. Eng. Rehabil., vol. 9, p. Epub, June 2012.
21. A. Ameri, E. Scheme, E. Kamavuako, K. Englehart, and P. Parker, "Real time, simultaneous myoelectric control using force and position based training paradigms," IEEE Trans. Biomed. Eng., vol. 61, no. 2, pp. 279-287, Feb. 2014.
22. E. N. Kamavuako, J. C. Rosenvang, M. F. Bøg, A. Smidstrup, E. Erkocevic, M. J. Niemeier,W. Jensen, and D. Farina, "Influence of the feature space on the estimation of hand grasping force from intramuscular EMG," Biomed. Signal Process. Control, vol. 8, no. 1, pp. 1-5, Jan. 2013.
23. J. M. Hahne, F. Biebmann, N. Jiang, H. Rehbaum, D. Farina, F. C. Meinecke, K. R. Muller, and L. C. Parra, "Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 2, pp. 269-279, Mar. 2014.
24. C. Castellini, E. Gruppioni, A. Davalli, and G. Sandini, "Fine detection of grasp force and posture by amputees via surface electromyography," J. Physiol.-Paris, vol. 103, pp. 255-262, Sep.-Dec. 2009.
25. N. Jiang, H. Rehbaum, I. Vujaklija, B. Graimann, and D. Farina, "Intuitive, online, simultaneous and proportional myoelectric control over two degrees of freedom in upper limb amputees," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 3, pp. 501-510, May 2014.
26. S. Muceli, N. Jiang, and D. Farina, "Extracting signals robust to electrode number and shift for online simultaneous and proportional myoelectric control by factorization algorithms," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 3, pp. 501-510, May 2014.
27. C. Choi and J. Kim, "Synergy matrices to estimate fluid wrist movements by surface electromyography," J. Med. Eng. Phys., vol. 33, no. 8, pp. 916-923, 2011.
28. C. Castellini and P. Van der Smagt, "Surface EMG in advanced hand prosthetics," Biol. Cybern., vol. 100, pp. 35-47, 2009.
29. D. Yang, D. J. Zhao, Y. Gu, L. Jiang, and H. Liu, "EMG pattern recognition and grasping force estimation: Improvement to the myocontrol of multi-DOF prosthetic hands," in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2009, pp. 516-521.
30. T. Tommasi, F.Orabona, C. Castellini, and B. Caputo, "Improving control of dexterous hand prostheses using adaptive learning," IEEE Trans. Robotics, vol. 29, no. 1, pp. 207-219, Feb. 2013.
31. A. Ziai andM. Carlo, "Comparison of regressionmodels for estimation of isometric wrist joint torques using surface electromyography," J. Neuroeng. Rehabil., vol. 8, no. 1
32. P. Fitts, "The information capacity of the human motor system in controlling the amplitude of movement," J. Exp. Psychol., vol. 47, no. 6, pp. 381-391, Jun. 1954.
33. C. Shannon, "Mathematical theory of communication," Bell Syst. Tech. J., vol. 27, pp. 623-656, 1948.
34. J. Park, W. Bei, H. Kim, and S. Park, "EMG-Force correlation considering Fitts' Law," in IEEE Int. Conf. Multisensor Fusion Integrat. Intell. Syst., Seoul, Korea, Aug. 2008, pp. 644-649.
35. M. Williams and R. Kirsch, "Evaluation of head orientation and neck muscle EMG signals as command inputs to a human-computer interface for individuals with high tetraplegia," IEEE Trans. Neural. Syst. Rehabil. Eng., vol. 16, no. 5, pp. 485-496, Oct. 2008.
36. C. Choi, S.Micera, J. Carpaneto, and J. Kim, "Development and quantitative performance evaluation of a noninvasive EMG computer interface," IEEE Trans. Biomed. Eng., vol. 56, no. 1, pp. 188-191, Jan. 2009.
37. E. Scheme and K. Englehart, "Validation of a selective ensemble-based classification scheme for myoelectric control using a three dimensional Fitts' Law test," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 21, no. 4, pp. 616-623, Jul. 2013.
38. E. Kamavuako, E. Scheme, and K. Englehart, "Combined surface and intramuscular EMG for improved real-time myoelectric control performance," Biomed. Signal Process. Control, vol. 10, pp. 102-107, 2014.
39. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
40. B. Boser, I. Guyon, and V. Vapnik, "A training algorithm for optimal margin classifiers," in Proc. 5th Annu. Workshop Computat. Learn. Theory, Pittsburgh, PA, 1992, pp. 144-152.
41. B. Scholkopf, P. Bartlett, A. Smola, and R.Williamson, "Shrinking the tube: A new support vector regression algorithm," in Adv. Neural Inf. Process. Syst., 1999, pp. 330-336.
42. W. Karush, "Minima of functions of several variables wiith inequalities as side constraints,"M.S. thesis, Dept.Math., Univ. Chicago, Chicago, IL, 1939.
43. H. Kuhn and A. Tucker, "Nonlinear programming," in Proc. 2nd Berkeley Symp. Math. Stat. Probabilist., Berkeley, CA, 1951, pp. 481-492.
44. C. Chang and C. Lin, "Training v-support vector regression: Theory and algorithms," Neural Computat., vol. 14, no. 8, pp. 1959-1977, 2002.
45. P. Chen, C. Lin, and B. Schölkopf, "A tutorial on v-support vector machines," Appl. Stochastic Models Business Indust., vol. 21, no. 2, pp. 111-136, 2005.
46. A. Simon, L. Hargrove, B. Lock, and T. Kuiken, "Target achievement control test: Evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses," J. Rehabil. Res. Develop., vol. 48, no. 6, pp. 619-628, 2011.
47. E. Scheme, B. Lock, L. Hargrove, W. Hill, U. Kuraganti, and K. Englehart, "Motion normalized proportional control for improved pattern recognition based myoelectric control," IEEE Trans. Neural Syst. Rehabil, vol. 22, no. 1, pp. 149-157, Jan. 2014.
48. L. Smith and L. Hargrove, "Comparison of surface and intramuscular EMG pattern recognition for simultaneous wrist/hand motion classification," in Proc. IEEE Conf. EMBS, 2013, pp. 4223-4226.