Recognition of grasp types through principal components of DWT based EMG features.
IEEE Int Conf Rehabil Robot
; 2011: 5975398, 2011.
Article
en En
| MEDLINE
| ID: mdl-22275601
With the advancement in machine learning and signal processing techniques, electromyogram (EMG) signals have increasingly gained importance in man-machine interaction. Multifingered hand prostheses using surface EMG for control has appeared in the market. However, EMG based control is still rudimentary, being limited to a few hand postures based on higher number of EMG channels. Moreover, control is non-intuitive, in the sense that the user is required to learn to associate muscle remnants actions to unrelated posture of the prosthesis. Herein lies the promise of a low channel EMG based grasp classification architecture for development of an embedded intelligent prosthetic controller. This paper reports classification of six grasp types used during 70% of daily living activities based on two channel forearm EMG. A feature vector through principal component analysis of discrete wavelet transform coefficients based features of the EMG signal is derived. Classification is through radial basis function kernel based support vector machine following preprocessing and maximum voluntary contraction normalization of EMG signals. 10-fold cross validation is done. We have achieved an average recognition rate of 97.5%.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Fuerza de la Mano
/
Electromiografía
Límite:
Humans
Idioma:
En
Revista:
IEEE Int Conf Rehabil Robot
Año:
2011
Tipo del documento:
Article
País de afiliación:
India
Pais de publicación:
Estados Unidos