Basic Hand Gestures Classification Based on Surface Electromyography.
This paper presents an innovative classification system for hand gestures using 2-channel surface electromyography analysis. The system developed uses the Support Vector Machine classifier, for which the kernel function and parameter optimisation are conducted additionally by the Cuckoo Search swarm algorithm. The system developed is compared with standard Support Vector Machine classifiers with various kernel functions. The average classification rate of 98.12% has been achieved for the proposed method.
Department of Mechatronics and High Voltage Engineering, Gdańsk University of Technology, Ulica G. Narutowicza 11/12, 80-233 Gdańsk, Poland.
Aleksander
A
Palkowski
Department of Mechatronics and High Voltage Engineering, Gdańsk University of Technology, Ulica G. Narutowicza 11/12, 80-233 Gdańsk, Poland.
Grzegorz
G
Redlarski
D000465:Algorithms; D004576:Electromyography; D005542:Forearm / Q000000981:diagnostic imaging; D005868:Gestures; D006225:Hand; D006801:Humans; D008297:Male; D015233:Models, Statistical; D018482:Muscle, Skeletal / Q000000981:diagnostic imaging; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine