382
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Techniques of EMG signal analysis: detection, processing, classification and applications

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

          Related collections

          Most cited references70

          • Record: found
          • Abstract: found
          • Article: not found

          Electrophysiological estimation of the number of motor units within a human muscle.

          An electrophysiological method is described for estimating the numbers of motor units in the extensor digitorum brevis muscle in man. The results obtained are compared with counts of axons in the nerve to the muscle. The significance of the sizes of the evoked motor unit potentials is discussed.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Theory of communication

            D GABOR (1947)
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A fast and reliable technique for muscle activity detection from surface EMG signals.

              The estimation of on-off timing of human skeletal muscles during movement is an important issue in surface electromyography (EMG) signal processing with relevant clinical applications. In this paper, a novel approach to address this issue is proposed. The method is based on the identification of single motor unit action potentials from the surface EMG signal with the use of the continuous wavelet transform. A manifestation variable is computed as the maximum of the outputs of a bank of matched filters at different scales. A threshold is applied to the manifestation variable to detect EMG activity. A model, based on the physical structure of the muscle, is used to test the proposed technique on synthetic signals with known features. The resultant bias of the onset estimate is lower than 40 ms and the standard deviation lower than 30 ms in case of additive colored Gaussian noise with signal-to-noise ratio as low as 2 dB. Comparison with previously developed methods was performed, and representative applications to experimental signals are presented. The method is designed for a complete real-time implementation and, thus, may be applied in clinical routine activity.
                Bookmark

                Author and article information

                Journal
                Biol Proced Online
                Biological Procedures Online
                Biological Procedures Online
                1480-9222
                2006
                23 March 2006
                : 8
                : 11-35
                Affiliations
                [1 ]Faculty of Engineering, Multimedia University. 63100 Cyberjaya, Selangor. Malaysia.
                Author notes
                M.B.I. Raez, Faculty of Engineering, Multimedia University. 63100 Cyberjaya, Selangor. Malaysia. mamun.raez@ 123456mmu.edu.my
                Article
                m115
                10.1251/bpo115
                1455479
                16799694
                7d218617-19f0-445f-951b-b7fbbd58437f
                Copyright © March 03, 2006, M Raez et al. This paper is Open Access and is published in Biological Procedures Online under license from the authors. Copying, printing, redistribution and storage permitted.
                History
                : 04 October 2005
                : 09 January 2006
                : 18 January 2006
                Categories
                Research Article

                Life sciences
                electromyography,fourier analysis,muscles,nervous system
                Life sciences
                electromyography, fourier analysis, muscles, nervous system

                Comments

                Comment on this article