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Research Article | Open Access |

Cost-Effective System for the Classification of Muscular Intent using Surface Electromyography and Artificial Neural Networks

Author 1: Anmol Khanna Author 2: Senthil Arumugam Muthukumaraswamy
International Journal of Advanced Computer Science and Applications (IJACSA) · Published 2018

DOI: 10.14569/SpecialIssue.2018.090123

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Abstract

This paper presents the implementation of a cost-effective system to classify muscular intent. A neural network is used for this purpose. After skin preparation, feature extraction, network training and real-time testing, an average overall classification accuracy of 93.3% over three possible gestures was obtained. Ultimately, the results obtained speak to the suitability of an Arduino-based system for the acquisition and decoding of muscular intent. This result is indicative of the potential of the Arduino microcontroller in this application, to provide effective performance at a far lower price-point than its competition.

Keywords

Electromyography orthosis prosthesis neural network muscular intent Arduino cost-effective

How to Cite this Article

Khanna, A., & Muthukumaraswamy, S. A. (2018). Cost-Effective System for the Classification of Muscular Intent using Surface Electromyography and Artificial Neural Networks. International Journal of Advanced Computer Science and Applications, 9(1). https://doi.org/10.14569/SpecialIssue.2018.090123

Khanna, Anmol, and Senthil Arumugam Muthukumaraswamy. "Cost-Effective System for the Classification of Muscular Intent using Surface Electromyography and Artificial Neural Networks." International Journal of Advanced Computer Science and Applications, vol. 9, no. 1, 2018, https://doi.org/10.14569/SpecialIssue.2018.090123.

@article{Khanna2018,
  title     = {Cost-Effective System for the Classification of Muscular Intent using Surface Electromyography and Artificial Neural Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {1},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Anmol Khanna and Senthil Arumugam Muthukumaraswamy},
  doi       = {10.14569/SpecialIssue.2018.090123},
  url       = {https://doi.org/10.14569/SpecialIssue.2018.090123}
}

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