International Journal of
Information and Education Technology

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.