Nonnegative matrix factorization for the identification of EMG finger movements: Evaluation using matrix analysis

Ganesh R. Naik, Hung T. Nguyen

Research output: Contribution to journalArticlepeer-review

117 Citations (Scopus)


Surface electromyography (sEMG) is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. The sEMG is a noninvasive, easy to record signal of superficial muscles from the skin surface. Considering the nonstationary characteristics of sEMG, recent feature selection of hand gesture recognition using sEMG signals necessitate designers to use nonnegative matrix factorization (NMF)-based methods. This method exploits both the additive and sparse nature of signals by extracting accurate and reliable measurements of sEMG features using a minimum number of sensors. The testing has been conducted for simple and complex finger flexions using several experiments with artificial neural network classification scheme. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to classify ten finger flexions (five simple and five complex finger flexions) recorded from two sEMG sensors up to 92% (95% for simple and 87% for complex flexions) accuracy. The recognition performances of simple and complex finger flexions are also validated with NMF permutation matrix analysis.

Original languageEnglish
Pages (from-to)478-485
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Issue number2
Publication statusPublished - Mar 2015
Externally publishedYes


  • Artificial neural network (ANN)
  • auto regression (AR)
  • flexions
  • gestures
  • hand gesture recognition
  • nonnegativematrix factorization (NMF)
  • principal component analysis (PCA)
  • root mean square (RMS)
  • surface electromyography (sEMG)


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