Title :
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
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;
Conference_Titel :
Applied Electronics (AE), 2014 International Conference on
Conference_Location :
Pilsen
Print_ISBN :
978-8-0261-0276-2
DOI :
10.1109/AE.2014.7011724
