Dynamic muscle fatigue detection using self-organizing maps

Applied Soft Computing Journal

Volumn 5, Issue 4, 2005, Pages 391-398

  Moshou, Dimitrios ^a   Hostens, Ivo ^a   Papaioannou, George ^b   Ramon, Herman ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b The Catholic University of America ^*  (United States)

Author keywords

Biomedical signal processing; Comfort; EMG; Muscle fatigue; Neural networks; Self organizing maps; Transient signal; Visualization; Wavelets

Indexed keywords

APPROXIMATION THEORY; BIOMEDICAL ENGINEERING; ELECTROMYOGRAPHY; SELF ORGANIZING MAPS; VISUALIZATION; WAVELET TRANSFORMS;

BIOMEDICAL SIGNAL PROCESSING; COMFORT; EMG; MUSCLE FATIGUE; TRANSIENT SIGNALS;

MUSCLE;

EID: 18844390483     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2004.09.001     Document Type: Article

References (22)

1. B.S. Hudgins, A new approach to multifunction myoelectric control, Ph.D. Dissertation, University of New Brunswick, 1991.
2. K. Engelhart, Signal representation for classification of the transient myoelectric signal, Ph.D. Dissertation, University of New Brunswick, 1998.
3. J.V. Basmajian, and C.V. De Luca Muscles Alive 1985 Williams & Wilkins Baltimore, MD
4. E.J. Kupa, S.H. Roy, S.C. Kandarian, and C.V. De Luca Effects of muscle fiber type and size on EMG median frequency and conduction velocity J. Appl. Physiol. 79 1 1995 23 32
5. M. Solomonow, C. Baten, J. Smit, R. Baratta, H. Hermens, D'Ambrosia, and H. Shoji Electromyogram power spectra frequencies associated with motor unit recruitment strategies J. Appl. Physiol. 68 3 1990 1177 1185
6. G. Balestra, R. Knaflitz, and R. Merletti Stationarity of voluntary and electrically elicited surface myoelectric signals Electrophysiol. Kinesiol. 1988 275 278
7. R. Merletti, M. Knaflitz, and C.J. De Luca Electrically evoked myoelectric signals Crit. Rev. Biomed. Eng. 19 4 1992 293 340
8. J.R. Potvin, and L.R. Bent A validation of techniques using surface EMG signals from dynamic contractions to quantify muscle fatigue during repetitive tasks J. Electromyogr. Kinesiol. 7 2 1997 131 139
9. S. Karlsson, and B. Gerdle Mean frequency and signal amplitude of the surface EMG of the quadriceps muscles increase with increasing torque - a study using the continuous wavelet transform J. Electromyogr. Kinesiol. 11 2001 131 140
10. M. Knaflitz, and P. Bonato Time-frequency methods applied to muscle fatigue assessment during dynamic contractions J. Electromyogr. Kinesiol. 9 1999 337 350
11. M.H. Pope, A. Aleksiev, N.D. Panagiotacopulos, J.S. Lee, D.G. Wilder, K. Friesen, W. Stielau, and V.K. Goel Evaluation of low back muscle surface EMG signals using wavelets Clin. Biomech. 15 2000 567 573
12. H.J. Hermens, F. Freriks, C. Disselhorst-Klug, and G. Rau Development of recommendations for EMG sensors and sensor placement procedures J. Electromyogr. Kinesiol. 10 2000 361 374
13. A. Luttmann, M. Jäger, K. Witscher, M. Vorgerd, and M. Tegenthoff Fatigue-induced EMG changes during low-level isometric contractions H.J. Hermens G. Rau C. Disselhorst-Klug B. Freriks Proceedings of the Third General SENIAM (Surface EMG for Non Invasive Assessment of Muscles) Workshop on Surface Electromyography: Application Areas and Parameters Aachen, Germany, May 1998 1998 Roessingh Research and Development Enschede 141 150
14. Daubechies Orthonormal bases of compactly supported wavelets Commun. Pure Appl. Math. 41 1988 909 996
15. Y. Meyer Orthonormal wavelets J.M. Combes A. Grossman P. Tchamitchian Wavelets Time-Frequency Methods and Phase-Space 1989 Springer-Verlag Berlin 21 37
16. M.R. Rao, and A.S. Bopardikar Wavelet Transforms - Introduction to Theory and Applications 1998 Addison-Wesley
17. T. Kohonen Self-organized formation of topographically correct feature maps Biol. Cybernet. 43 1982 59 69
18. N.K. Vøllestadt Measurements of human muscle fatigue J. Neuro. Sci. Meth. 74 1997 219 227
19. J.R. Doud, and J.M. Walsh Muscle fatigue and muscle length interaction, effect on the EMG frequency components Electromyogr. Clin. Neurophysiol. 35 1995 331 339
20. G.D. Magoulas, V.P. Plagianakos, and M.N. Vrahatis Neural network-based colonoscopic diagnosis using on-line learning and differential evolution Appl. Soft Comput. 4 2004 369 379
21. S. Yamada Recognizing environments from action sequences using self-organizing maps Appl. Soft Comput. 4 2004 35 47
22. X.Z. Gao, and S.J. Ovaska Soft computing methods in motor fault diagnosis Appl. Soft Comput. 1 2001 73 81