Genetic reasoning for finger sign identification based on forearm electromyogram

Author

Tsujimura, Takeshi ; Hashimoto, Toshikazu ; Izumi, Kiyotaka

Author_Institution

Dept. of Mech. Eng., Saga Univ., Saga, Japan

fYear

2014

fDate

9-10 Sept. 2014

Firstpage

297

Lastpage

302

Abstract

This paper proposes a meta-heuristic data-clustering application to identify finger signs only by measuring surface electromyogram (EMG) of a forearm. It classifies EMG signal patterns peculiar to finger signs. Genetic programming learns intensity characteristics of EMG signals, and creates classification algorithm. Three typical finger signs are evaluated in terms of generated EMG. Experiments are conducted to reveal the successful identification of finger signs in real time.

Keywords

electromyography; fingerprint identification; genetic algorithms; medical signal processing; pattern clustering; signal classification; EMG signal pattern classification; classification algorithm; finger sign identification; forearm electromyogram; genetic programming; intensity characteristics; metaheuristic data-clustering application; surface electromyogram; Electrodes; Electromyography; Genetic programming; Muscles; Real-time systems; Thumb; electromyogram (EMG); finger; forearm; genetic programming (GP); identification; muscle; sign;

fLanguage

English

Publisher

ieee

Conference_Titel

Applied Electronics (AE), 2014 International Conference on

Conference_Location

Pilsen

ISSN

1803-7232

Print_ISBN

978-8-0261-0276-2

Type

conf

DOI

10.1109/AE.2014.7011724

Filename

7011724