Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal.
Identifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from amputee residual muscle is required for controlling the myoelectric prosthetic hand. In this study, we have computed the signal fractal dimension (FD) and maximum fractal length (MFL) during different grip patterns performed by healthy and transradial amputee subjects. The FD and MFL of the sEMG, referred to as the fractal features, were classified using twin support vector machines (TSVM) to recognize the handgrips. TSVM requires fewer support vectors, is suitable for data sets with unbalanced distributions, and can simultaneously be trained for improving both sensitivity and specificity. When compared with other methods, this technique resulted in improved grip recognition accuracy, sensitivity, and specificity, and this improvement was significant (κ=0.91).
Sridhar Poosapadi
SP
Arjunan
Dinesh Kant
DK
Kumar
Jayadeva J
D000328:Adult; D000465:Algorithms; D000672:Amputation Stumps / Q000503:physiopathology*; D004576:Electromyography / Q000379:methods*; D005260:Female; D017709:Fractals; D018737:Hand Strength / Q000502:physiology*; D006801:Humans; D008297:Male; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine