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Recognition of palm finger movements on the basis of EMG signals with the application of wavelets

Krysztoforski K., Wolczowski A., Będziński R., Helt K.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2004

Vol. 8, No 2

269-280

The paper describes an EMG signal analysis based on the wavelet transform, applied for the hand prosthesis control. Signal features are represented by wavelet coefficients. A cross-validation method is applied for the feature selection process. The classification algorithm uses multistage recognition. The information about finger posture provided by a data glove is recorded concurrently with forearm EMG signals. The acquired data are used to train the classification algorithm.

Case of fuzzy loss function in multistage recognition algorithm

Burduk R.

Journal of Medical Informatics & Technologies

2003

Vol. 5

MI107--112

The work deals with a recognition problem using a probabilistic-fuzzy model and multistage decision logic. A case where a loss function is described using fuzzy numbers has been considered. The globally optimal Bayes strategy has been calculated for this case with stage-dependent and dependent on the node of the decision tree fuzzy loss function. The obtained result is illustrated by a calculation example in which some methods for ranking fuzzy numbers were used.