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Biomimetic grasp planning for cortical control of a robotic hand
In this paper we outline a grasp planning system designed to augment the cortical control of a prosthetic arm and hand. A key aspect of this system it the ability to combine online user input and autonomous planning to enable the execution of stable grasping tasks. While user input can ultimately be of any modality, the system is being designed to adapt to partial or noisy information obtained from grasp-related activity in the primate motor cortex. First, principal component analysis is applied to the observed kinematics of physiologic grasping to reduce the dimensionality of hand posture space and simplify the planning task for on-line use. The planner then accepts control input in this reduced-dimensionality space, and uses it as a seed for a hand posture optimization algorithm based on simulated annealing. We present two applications of this algorithm, using data collected from both primate and human subjects during grasping, to demonstrate its ability to synthesize stable grasps using partial control input in real or near-real time
Principal components analysis based control of a multi-dof underactuated prosthetic hand
<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p