Simultaneous and proportional force estimation for multifunction myoelectric prostheses using mirrored bilateral training

IEEE Transactions on Biomedical Engineering

Volumn 58, Issue 3 PART 1, 2011, Pages 681-688

  Nielsen, Johnny L G ^a   Holmgaard, Steffen ^b   Jiang, Ning ^a,c   Englehart, Kevin B ^d   Farina, Dario ^c   Parker, Phil A ^d  

^a Otto Bock HealthCare GmbH   (Germany)

^b AARHUS UNIVERSITY HOSPITAL   (Denmark)

^c UNIVERSITY MEDICAL CENTER GÖTTINGEN   (Germany)

^d UNIVERSITY OF NEW BRUNSWICK   (Canada)

Author keywords

Electromyography (EMG); mirrored bilateral training; multilayer perceptron; myoelectric control; upper limb prosthesis

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLINICAL SETTINGS; CONGENITAL MALFORMATIONS; ESTIMATION ALGORITHM; ESTIMATION PERFORMANCE; FORCE ESTIMATION; ISOMETRIC FORCE; MIRRORED BILATERAL TRAINING; MULTILAYER PERCEPTRON; MULTIPLE DEGREES OF FREEDOM; MYOELECTRIC CONTROL; MYOELECTRIC PROSTHESIS; NOVEL METHODS; PROCESSING PARAMETERS; SHORT SIGNAL; SIMULTANEOUS CONTROL; SURFACE ELECTROMYOGRAM; SURFACE EMG; UPPER LIMB PROSTHESIS; UPPER LIMBS;

ELECTROMYOGRAPHY; ESTIMATION; MULTILAYERS; MUSCLE; NEURAL NETWORKS;

MYOELECTRICALLY CONTROLLED PROSTHETICS;

ADULT; ALGORITHM; ARM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLINICAL ARTICLE; CONGENITAL MALFORMATION; CONTROLLED STUDY; DISABLED PERSON; ELECTROMYOGRAM; FEMALE; FORCE; FOREARM; HUMAN; HUMAN EXPERIMENT; LIMB PROSTHESIS; MALE; MYOELECTRIC CONTROL; MYOELECTRIC PROSTHESES; PERCEPTRON; PROSTHESES AND ORTHOSES; WRIST;

ADULT; ALGORITHMS; ARTIFICIAL LIMBS; BIOMECHANICS; ELECTROMYOGRAPHY; FEMALE; HUMANS; MALE; NEURAL NETWORKS (COMPUTER); SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 79952182926     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2010.2068298     Document Type: Article

References (18)

1. R. Merletti and P. A. Parker, Electromyography - Physiology, Engineering and Noninvasive Applications. Wiley-Interscience, 2004, ch. 18, pp. 453-475.
2. 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.
3. N. Jiang, K. B. Englehart, and P. A. Parker, "Extracting simultaneous and proportional neural control information for multiple degree of freedom prostheses from the surface electromyographic signal," IEEE Trans. Biomed. Eng., vol. 56, no. 4, pp. 1070-1080, Apr. 2009.
4. 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.
5. 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, 1999.
6. 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.
7. 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, 2008.
8. S. Oda, "Motor control for bilateral muscular contractions in humans," Jpn. J. Physiol., vol. 47, no. 6, pp. 487-498, 1997.
9. M. Post, H. van Duinen, A. Steens, R. Renken, B. Kuipers, N. Maurits, and I. Zijdewind, "Reduced cortial activity during maximal bilateral contractions of the index finger," NeuroImage, vol. 35, no. 1, pp. 16-27, 2007.
10. 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. Technol., vol. 30, no. 1, pp. 2-10, 2006.
11. M. DiCicco, L. Lucas, andY. Matsuoka, "Comparison of control strategies for an EMG controlled orthotic exoskeleton for the hand," in Proc. IEEE ICRA 2004, New Orleans, LA, pp.1622-1627.
12. A. B. Ajiboye and R. F. Weir, "A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control," IEEE Trans. Neural. Syst. Rehabil. Eng., vol. 13, no. 3, pp. 280-291, Sep. 2005.
13. A. d'Avella, A. Portone, L. Fernandez, and F. Lacquaniti, "Control of fast-reaching movements by muscle synergy combinations," J Neurosci, vol. 26, no. 30, pp. 7791-7810, 2006.
14. M. M. Liu, W. Herzog, and H. H. Savelberg, "Dynamic muscle force predictions from EMG: An artificial neural network approach.," J Electromyogr. Kinesiol., vol. 9, no. 6, pp. 391-400, 1999.
15. L. Hargrove, Y. Losier, B. Lock, K. Englehart, and B. Hudgins, "A real-time pattern recognition based myoelectric control usability study implemented in a virtual environment," in Proc. IEEE-EMBS, EMBC 2007, Lyon, pp. 4842-4845.
16. M. E. Hahn, "Feasibility of estimating isokinetic knee torque using a neural network model," J. Biomech., vol. 40, no. 5, pp. 1107-1114, 2007.
17. M. T. Legere, J. R. Sexsmith, A. M. Mackie, R. N. Scott, and P. A. Parker, "Isometric muscle fatigue effects on pattern recognition ability of a new myoelectric prosthesis control system," presented at the Myoelectric Controls Symp., Fredericton, New Brunswick, Canada, 1993.
18. T. R. Farrell and R. F. Weir, "The optimal controller delay for myoelectric prostheses," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 15, no. 1, pp. 111-118, Mar. 2007.