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: firstname.lastname@example.org, email@example.com).
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.