Controlling a dexterous myoelectric prosthetic hand with many degrees of freedom (DoFs) could be a very demanding task, which requires the amputee for high concentration and ability in modulating many different muscular contraction signals. In this work a new approach to multi-DoF control is proposed, which makes use of Principal Component Analysis (PCA) to reduce the DoFs space dimensionality and allow to drive a 15 DoFs hand by means of a 2 DoFs signal. This approach has been tested and properly adapted to work onto the underactuated robotic hand named CyberHand, using mouse cursor coordinates as input signals and a principal components (PCs) matrix taken from the literature. First trials show the feasibility of performing grasps using this method. Further tests with real EMG signals are foreseen.
Bio-inspired controller for a dexterous prosthetic hand based on Principal Components Analysis.
MATRONE, GIULIA;SECCO, EMANUELE LINDO;MAGENES, GIOVANNI
2009-01-01
Abstract
Controlling a dexterous myoelectric prosthetic hand with many degrees of freedom (DoFs) could be a very demanding task, which requires the amputee for high concentration and ability in modulating many different muscular contraction signals. In this work a new approach to multi-DoF control is proposed, which makes use of Principal Component Analysis (PCA) to reduce the DoFs space dimensionality and allow to drive a 15 DoFs hand by means of a 2 DoFs signal. This approach has been tested and properly adapted to work onto the underactuated robotic hand named CyberHand, using mouse cursor coordinates as input signals and a principal components (PCs) matrix taken from the literature. First trials show the feasibility of performing grasps using this method. Further tests with real EMG signals are foreseen.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
