Med Biol Eng Comput - SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine.

Tópicos

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Resumo

This paper proposes a scheme consisting of two novel components to recognize multiple hand motions from surface electromyography (SEMG). First, we use the cumulative residual entropy (CREn), a measure of uncertainty in a random variable, as the feature. Second, we employ the extreme learning machine (ELM), a fast and effective classifier using single-hidden layer feedforward neural network with additive neurons, to distinguish different motions. To evaluate performance of the proposed system, we compare CREn with fuzzy entropy, sample entropy, and approximate entropy, and a state-of-the-art time-domain feature; and ELM with linear discriminant analysis and support vector machine. They are tested on four channel SEMG signals acquired from ten normal subjects. Experimental results indicate that the classification accuracies of CREn are not only better than those of other entropies with all the classifiers, but also comparable to the time-domain feature for all the segment lengths of 200, 250 and 1,000 ms with all classifiers that are evaluated. Furthermore, the computational complexity of CREn is lower than those of other features, and ELM performs significantly faster than other classifiers without sacrificing any performance. It suggests that the proposed CREn-ELM scheme has the potential to be applied to real-time control of SEMG-based multifunctional prosthesis.

Resumo Limpo

paper propos scheme consist two novel compon recogn multipl hand motion surfac electromyographi semg first use cumul residu entropi cren measur uncertainti random variabl featur second employ extrem learn machin elm fast effect classifi use singlehidden layer feedforward neural network addit neuron distinguish differ motion evalu perform propos system compar cren fuzzi entropi sampl entropi approxim entropi stateoftheart timedomain featur elm linear discrimin analysi support vector machin test four channel semg signal acquir ten normal subject experiment result indic classif accuraci cren better entropi classifi also compar timedomain featur segment length ms classifi evalu furthermor comput complex cren lower featur elm perform signific faster classifi without sacrif perform suggest propos crenelm scheme potenti appli realtim control semgbas multifunct prosthesi

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