Classification of finger activation for use in a robotic prosthesis arm.
Hand amputees would highly benefit from a robotic prosthesis, which would allow the movement of a number of fingers. In this paper we propose using the electromyographic signals recorded by two pairs of electrodes placed over the arm for operating such prosthesis. Multiple features from these signals are extracted whence the most relevant features are selected by a genetic algorithm as inputs for a simple classifier. This method results in a probability of error of less than 2%.
Peleg
Dori
D
Faculty of Electrical Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel.
Braiman
Eyal
E
Yom-Tov
Elad
E
Inbar
Gideon F
GF
D000328:Adult; D000465:Algorithms; D001186:Artificial Limbs; D004576:Electromyography / Q000379:methods*; D005385:Fingers / Q000502:physiology*; D005542:Forearm / Q000502:physiology*; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology; D009119:Muscle Contraction / Q000502:physiology; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated; D011597:Psychomotor Performance / Q000502:physiology; D015203:Reproducibility of Results; D012371:Robotics / Q000379:methods*; D012680:Sensitivity and Specificity; D013223:Statistics as Topic