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IJIET 2015 Vol.5(2): 113-118 ISSN: 2010-3689
DOI: 10.7763/IJIET.2015.V5.486

Application of Genetic Algorithm for Optimization of Data in Surface Myoelectric Prosthesis for the Transradial Amputee

Jumelyn L. Torres and Noel B. Linsangan

Abstract—Genetic algorithm (GA) is a method that can be used to discover and manage a population of useful patterns in which this study implements; specifically, in optimization. This algorithm is a powerful tool to find the best solution in problems such as prediction and data fitting due to its ability for fast adaptation in the problem environment. Continuous or discrete parameters can be optimized by GA even without requiring derivative information by simultaneously searching from a wide sampling of the cost surface even if it deals with large number of parameters. The paper makes use of this algorithm to optimize the surface electromyography (SEMG) signal from the skeletal muscle force of a transradial amputee in controlling a surface myoelectric prosthesis. The SEMG signals patterns are acquired from the two devices: the microcontroller unit and the EMG simulator. The signals from these two devices are processed and optimized using GA. The optimized signal is used to test the surface myoelectric prosthesis. Moreover, the data acquired from these signals is treated using t- test to show the significant difference of their means.

Index Terms—Genetic algorithm, optimization, surface electromyography signal (SEMG), surface myoelectric prosthesis, T-test.

The authors are with the Computer Engineering Department at the Mapua Institute of Technology, Philippines (e-mail: jumelyn.torres@yahoo.com, nblinsangan@mapua.edu.ph).


Cite: Jumelyn L. Torres and Noel B. Linsangan, "Application of Genetic Algorithm for Optimization of Data in Surface Myoelectric Prosthesis for the Transradial Amputee," International Journal of Information and Education Technology vol. 5, no. 2, pp. 113-118, 2015.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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