"Predicting Muscle Fatigue via Electromyography: A Comparative Study"
Proceedings of the 25-th Inter'l Conference on Computers and Industrial Engineering,
New Orleans, LA, March, pp. 277-280, 1999.
by Torvik, V.I., E. Triantaphyllou, T.W. Liao, and S.M. Waly
Abstract:
This paper presents a comparison of some statistical and AI predicative techniques.
The data used electromyography (EMG) signals related to fatigue and rest conditions of
certain arm muscles. Besides the building of some effective models for predicting muscle
fatigue from EMG signals, this study indicates that a new data mining techniques (the OCAT approach),
which has been developed by some of the authors, has a lot of promise.
Key Words:
Electromyography, comparison of prediction methods, neural networks, fuzzy membership,
C-Means, K-nearest neighbor, logistic regression, linear discriminate, data mining,
Fourier transform.
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