Machine Learning
Enabling always-available input with muscle-computer interfaces
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Enhancing input on and above the interactive surface with muscle sensing
Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces
An adaptive feature extractor for gesture SEMG recognition
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
Feature reduction and selection for EMG signal classification
Expert Systems with Applications: An International Journal
Computing emotion awareness through facial electromyography
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
Fractal analysis features for weak and single-channel upper-limb EMG signals
Expert Systems with Applications: An International Journal
A comparative study of wavelet families for classification of wrist motions
Computers and Electrical Engineering
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In pattern recognition-based myoelectric control, high accuracy for multiple discriminated motions is presented in most of related literature. However, there is a gap between the classification accuracy and the usability of practical applications of myoelectric control, especially the effect of long-term usage. This paper proposes and investigates the behavior of fifty time-domain and frequency-domain features to classify ten upper limb motions using electromyographic data recorded during 21days. The most stable single feature and multiple feature sets are presented with the optimum configuration of myoelectric control, i.e. data segmentation and classifier. The result shows that sample entropy (SampEn) outperforms other features when compared using linear discriminant analysis (LDA), a robust classifier. The averaged test classification accuracy is 93.37%, when trained in only initial first day. It brings only 2.45% decrease compared with retraining schemes. Increasing number of features to four, which consists of SampEn, the fourth order cepstrum coefficients, root mean square and waveform length, increase the classification accuracy to 98.87%. The proposed techniques achieve to maintain the high accuracy without the retraining scheme. Additionally, this continuous classification allows the real-time operation.