Abstract
BackgroundThe nonstationary property of electromyography (EMG) signals usually makes the pattern recognition (PR) based methods ineffective after some time in practical application for multinational prosthesis. The conventional EMG PR, which is accomplished in two separate steps: training and testing, ignores the mismatch between training and testing conditions and often discards the useful information in testing dataset.MethodThis paper presents a novel self-enhancing approach to improve the classification performance of the electromyography (EMG) pattern recognition (PR). The proposed self-enhancing method incorporates the knowledge beyond the training condition to the classifiers from the testing data. The widely-used linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are extended to self-enhancing LDA (SELDA) and self-enhancing QDA (SEQDA) by continuously updating their model parameters such as the class mean vectors, the class covariances and the pooled covariance. Autoregressive (AR) and Fourier-derived cepstral (FC) features are adopted. Experimental data in two different protocols are used to evaluate performance of the proposed methods in short-term and long-term application respectively.ResultsIn protocol of short-term EMG, based on AR and FC, the recognition accuracy of SEQDA and SELDA is 2.2% and 1.6% higher than conventional that of QDA and LDA respectively. The mean results of SEQDA(C) and SEQDA (M) are improved by 2.2% and 0.75% for AR, and 1.99% and 1.1% for FC respectively when compared to QDA. The mean results of SELDA(C) and SELDA (M) are improved by 0.48% and 1.55% for AR, and 0.67% and 1.22% for FC when compared to LDA. In protocol of long-term EMG, the mean result of SEQDA is 3.15% better than that of QDA.ConclusionThe experimental results show that the self-enhancing classifiers significantly outperform the original versions using both AR and FC coefficient feature sets. The performance of SEQDA is superior to SELDA. In addition, preliminary study on long-term EMG data is conducted to verify the performance of SEQDA.
Highlights
Surface electromyogram (EMG) signal is a noninvasive measurement and contains rich information associated with the muscle electrical activities
The mean results of self-enhancing QDA (SEQDA)(C) and SEQDA (M) are improved by 2.2% and 0.75% for AR, and 1.99% and 1.1% for Fourier-derived cepstral (FC) respectively when compared to quadratic discriminant analysis (QDA)
The performance of SEQDA is superior to self-enhancing LDA (SELDA)
Summary
Surface electromyogram (EMG) signal is a noninvasive measurement and contains rich information associated with the muscle electrical activities. Conventional myoelectric control systems enable the amputees to operate a single device such as a hand or a wrist [2], based on amplitude decoding of the EMG signal recorded from the separable forearm muscles. The early myoelectric controllers can only operate in an on-off mode to control electrically powered hands with openclose functions [3]. To increase the number of motion classes, much attention has been drawn to a pattern-recognition (PR)-based approach to the myoelectric control of multifunctional prostheses in last two decades. Unlike the conventional EMG decoding method that assigns each function to a specific control muscle, the PR-based approach extracts useful information from several EMG channels to form a feature vector and maps it to a motion class, maximizing the separability between each motion. The conventional EMG PR, which is accomplished in two separate steps: training and testing, ignores the mismatch between training and testing conditions and often discards the useful information in testing dataset
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