Principal components analysis based control of a multi-dof underactuated prosthetic hand
Functionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG). Driving a multi degrees of freedom (DoF) hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user.
A Principal Components Analysis (PCA) based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand) with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs). Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control.
Trials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture) may be achieved.
This work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis.
- Principal components analysis based control of a multi-dof underactuated prosthetic hand
- Open Access
- Available under Open Access This content is freely available online to anyone, anywhere at any time.
Journal of NeuroEngineering and Rehabilitation
- Online Date
- April 2010
- Online ISSN
- BioMed Central
- Additional Links
- Author Affiliations
- 1. Department of Computer Engineering and Systems Science, University of Pavia, Via Ferrata 1, 27100, Pavia, Italy
- 2. ARTS Lab, Scuola Superiore Sant'Anna, V.le Piaggio 34, 56025, Pontedera (PI), Italy
- 3. EUCENTRE Foundation, Via Ferrata 1, 27100, Pavia, Italy