Show simple item record

dc.contributor.authorAnam, Khairul
dc.contributor.authorAl-Jumaily, Adel
dc.date.accessioned2017-10-11T06:41:07Z
dc.date.available2017-10-11T06:41:07Z
dc.date.issued2017-10-11
dc.identifier.issn0893-6080
dc.identifier.urihttp://repository.unej.ac.id/handle/123456789/82055
dc.descriptionJournal Neural Networks 85 (2017) 51–68en_US
dc.description.abstractThe success of myoelectric pattern recognition (M-PR) mostly relies on the features extracted and classifier employed. This paper proposes and evaluates a fast classifier, extreme learning machine (ELM), to classify individual and combined finger movements on amputees and non-amputees. ELM is a single hidden layer feed-forward network (SLFN) that avoids iterative learning by determining input weights randomly and output weights analytically. Therefore, it can accelerate the training time of SLFNs. In addition to the classifier evaluation, this paper evaluates various feature combinations to improve the performance of M-PR and investigate some feature projections to improve the class separability of the features. Different from other studies on the implementation of ELM in the myoelectric controller, this paper presents a complete and thorough investigation of various types of ELMs including the node-based and kernel-based ELM. Furthermore, this paper provides comparisons of ELMs and other well-known classifiers such as linear discriminant analysis (LDA), k-nearest neighbour (kNN), support vector machine (SVM) and least-square SVM (LS-SVM). The experimental results show the most accurate ELM classifier is radial basis function ELM (RBF-ELM). The comparison of RBF-ELM and other well-known classifiers shows that RBF-ELM is as accurate as SVM and LS-SVM but faster than the SVM family; it is superior to LDA and kNN. The experimental results also indicate that the accuracy gap of the M-PR on the amputees and nonamputees is not too much with the accuracy of 98.55% on amputees and 99.5% on the non-amputees using six electromyography (EMG) channels.en_US
dc.language.isoenen_US
dc.subjectClassificationen_US
dc.subjectMyoelectric pattern recognitionen_US
dc.subjectElectromyography (EMG)en_US
dc.subjectExtreme learning machine (ELM)en_US
dc.subjectAmputeeen_US
dc.titleEvaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputeesen_US
dc.typeArticleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record