Surface myoelectric signal classification for prostheses control.
This paper represents an ongoing investigation for surface myoelectric signal segmentation and classification. The classical moving average technique augmented with principal components analysis and time-frency analysis were used for segmentation. Multiresolution wavelet analysis was adopted as an effective feature extraction technique while artificial neural networks were used for classification. Results of classifying four elbow and wrist movement signals recorded from biceps and triceps gave 5.1% classification error when two channels were used.
Al-Assaf
Y
Y
School of Engineering, University of Sharjah, Sharjah, UAE. yassaf@ausharjah.edu
Al-Nashash
H
H
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004551:Elbow Joint / Q000502:physiology*; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D007595:Joint Prosthesis; D008297:Male; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D014955:Wrist Joint / Q000502:physiology*