Neural Computing and Applications

, Volume 27, Issue 3, pp 791–804 | Cite as

Classification of electromyography signals using relevance vector machines and fractal dimension

  • Clodoaldo A. M. LimaEmail author
  • André L. V. Coelho
  • Renata C. B. Madeo
  • Sarajane M. Peres
Original Article


Surface electromyography (EMG) signals have been studied extensively in the last years aiming at the automatic classification of hand gestures and movements as well as the early identification of latent neuromuscular disorders. In this paper, we investigate the potentials of the conjoint use of relevance vector machines (RVM) and fractal dimension (FD) for automatically identifying EMG signals related to different classes of limb motion. The adoption of FD as the mechanism for feature extraction is justified by the fact that EMG signals usually show traces of self-similarity. In particular, four well-known FD estimation methods, namely box-counting, Higuchi’s, Katz’s and Sevcik’s methods, have been considered in this study. With respect to RVM, besides the standard formulation for binary classification, we also investigate the performance of two recently proposed variants, namely constructive mRVM and top-down mRVM, that deal specifically with multiclass problems. These classifiers operate solely over the features extracted by the FD estimation methods, and since the number of such features is relatively small, the efficiency of the classifier induction process is ensured. Results of experiments conducted on a publicly available dataset involving seven distinct types of limb motions are reported whereby we assess the performance of different configurations of the proposed RVM+FD approach. Overall, the results evidence that kernel machines equipped with the FD feature values can be useful for achieving good levels of classification performance. In particular, we have empirically observed that the features extracted by the Katz’s method is of better quality than the features generated by other methods.


EMG signal classification Relevance vector machines  Fractal dimension Feature extraction 



The first and second authors acknowledge the sponsorship from the Brazilian National Council for Research and Development (CNPq) via grants #475406/2010-9, #308816/2012-9, and #304603/2012-0. The third author thanks the financial support of São Paulo Research Foundation (FAPESP/ Brazil)—process number 2011/04608-8.


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Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  1. 1.Information Systems Program, School of Arts, Sciences and HumanitiesUniversity of São PauloSão PauloBrazil
  2. 2.Graduate Program in Applied Informatics, Center of Technological SciencesUniversity of FortalezaFortalezaBrazil

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