High accurate lightweight deep learning method for gesture recognition based on surface electromyography.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 3, 2020

Abstract

BACKGROUND AND OBJECTIVES: Surface Electromyography (sEMG) is used mostly for neuromuscular diagnosis, assistive technology, physical rehabilitation, and human-computer interactions. Achieving a precise and lightweight method along with low latency for gesture recognition is still a real-life challenge, especially for rehabilitation and assistive robots. This work aims to introduce a highly accurate and lightweight deep learning method for gesture recognition.

Authors

  • Ali Bahador
    Faculty of Electrical and Computer Engineering, K.N. Toosi University of Technology, Tehran 1631714191, Iran.
  • Moslem Yousefi
    School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 136-713, Republic of Korea.
  • Mehdi Marashi
    Department of Mechanical Engineering, Islamic Azad University, Roudehen Branch, Roudehen 3973188981, Iran.
  • Omid Bahador
    Faculty of Mechanical Engineering, Islamic Azad University, South Tehran Branch, Tehran 1584743311, Iran.

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