Abstract
Myoelectric hand-prostheses are used by patients with either above- or below-elbow amputations and actuated with a minimal microvolt-threshold myoelectric signal (MES). Prehensile motions or patterns are deduced from the MES by classification. Current approaches act on the assumption, that MES is adiabatic-invariant and unaffected by fatigue of contributory muscles. However, classifiers fail on the onset of muscle fatigue and cannot distinguish between voluntary-, submaximal-contraction and an intentional release of muscle tension. As a result, patients experience a gradual loss of control over their prostheses. In this contribution we show, that the probability distributions of extracted time- and frequency-domain features are fatigue dependent with regard to locality, skewness and time. Also, we examine over which time-frame, established classifiers provide unambiguous results and how classifiers can be improved by the selection of a proper sampling-window size and an appropriate threshold for select features.
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References
Oskoei, M.A., Hu, H.: Myoelectric Control Systems - A Survey. Biomedical Signal Processing and Control 2(4), 275–294 (2007)
Englehart, K., Hudgins, B., Parker, P., Stevenson, M.: Classification of the Myoelectric Signal using Time-Frequency Based Representations. Special Issue of Medical Engineering and Physics on Intelligent Data Analysis in Electromyography and Electroneurography 21, 431–438 (1999)
Muzumdar, A.: Powered Upper Limb Prostheses, Control, Implementation and Clinical Application. Springer, Heidelberg (2004)
MacIsaac, D., Parker, P., Englehart, K., Rogers, D.: Fatigue Estimation with a Multivariable Myoelectric Mapping Function. IEEE Transactions on Biomedical Engineering 53(4), 694–700 (2006)
De Luca, C.J.: The Use of Surface Electromyography in Biomechanics. Journal of Aplied Biomechanics 13, 135–163 (1997)
De Luca, C.J.: Myoelectric Manifestations of Localized Muscular Fatigue in Humans. Crit Rev Biomedeical Engineering 11(4), 251–279 (1984)
Bischoff, C., Schulte-Mattler, W.J., Conrad, B.: Das EMG-Buch; EMG und periphere Neurologie in Frage und Antwort. Georg Thieme Verlag (2005)
Merletti, R., Farina, D.: Myoelectric Manifestations of Muscle Fatigue. In: Wiley Encyclopedia of Biomedical Engineering. John Wiley & Sons, Inc., Chichester (2006)
Merlo, A., Farina, D., Merletti, R.: A Fast and Reliable Technique for Muscle Activity Detection from Surface EMG Signals. IEEE Transactions on Biomedical Engineering 50(3), 316–323 (2003)
Oskoei, M.A., Hu, H., Gan, J.Q.: Manifestation of Fatigue in Myoelectric Signals of Dynamic Contractions Produced During Playing PC Games. In: Proc. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2008, pp. 315–318 (2008)
Bonato, P., Roy, S.H., Knaflitz, M., De Luca, C.J.: Time-Frequency Parameters of the Surface Myoelectric Signal for Assessing Muscle Fatigue During Cyclic Dynamic Contractions. IEEE Transactions on Biomedical Engineering 48(7), 745–753 (2001)
Radicheva, N., Gerilovsky, L., Gydikow, A.: Changes in the Muscle Fibre Extracellular Action Potentials in Long-Lasting (Fatiguing) Activity. European Journal of Applied Physiology and Occupational Physiology 55, 545–552 (1986)
Buchenrieder, K.: Dimensionality Reduction and Classification of Myoelectric Signals for the Control of Upper-Limb Prostheses. In: Proceedings of the IASTED - Human Computer Interaction 2008, pp. 113–119 (2008)
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Herrmann, S., Buchenrieder, K.J. (2009). Dynamic Behavior of Time-Domain Features for Prosthesis Control. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2009. EUROCAST 2009. Lecture Notes in Computer Science, vol 5717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04772-5_72
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DOI: https://doi.org/10.1007/978-3-642-04772-5_72
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