Classification of finger activation for use in a robotic prosthesis arm

Author

Peleg, Dori ; Braiman, Eyal ; Yom-Tov, Elad ; Inbar, Gideon F.

Author_Institution

Fac. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel

Volume

10

Issue

4

fYear

2002

Firstpage

290

Lastpage

293

Abstract

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%.

Keywords

artificial limbs; electromyography; feature extraction; genetic algorithms; medical robotics; medical signal processing; EMG analysis; assistive devices; electrode pairs; error probability; finger activation classification; hand amputation; hand amputees; multiple signal features extraction; robotic prosthesis arm; robotic replacement hand control; signal features; Electrodes; Electromyography; Fingers; Genetic algorithms; Microswitches; Muscles; Prosthetics; Robots; Signal processing; Wrist; Adult; Algorithms; Artificial Limbs; Electromyography; Fingers; Forearm; Humans; Male; Movement; Muscle Contraction; Muscle, Skeletal; Pattern Recognition, Automated; Psychomotor Performance; Reproducibility of Results; Robotics; Sensitivity and Specificity; Statistics as Topic;

fLanguage

English

Journal_Title

Neural Systems and Rehabilitation Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1534-4320

Type

jour

DOI

10.1109/TNSRE.2002.806831

Filename

1178099