This paper intends to provide a critical review of the literature on the technological issues on control and sensorization of hand prostheses interfacing with the Peripheral Nervous System (i.e., PNS), and their experimental validation on amputees. The study opens with an in-depth analysis of control solutions and sensorization features of research and commercially available prosthetic hands. Pros and cons of adopted technologies, signal processing techniques and motion control solutions are investigated. Special emphasis is then dedicated to the recent studies on the restoration of tactile perception in amputees through neural interfaces. The paper finally proposes a number of suggestions for designing the prosthetic system able to re-establish a bidirectional communication with the PNS and foster the prosthesis natural control.

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The success of grasping and manipulation tasks of commercial prosthetic hands is mainly related to amputee visual feedback since they are not provided either with tactile sensors or with sophisticated control. As a consequence, slippage and object falls often occur. This article wants to address the specific issue of enhancing grasping and manipulation capabilities of existing prosthetic hands, by changing the control strategy. For this purpose, it proposes a multilevel control based on two distinct levels consisting of (1) a policy search learning algorithm combined with central pattern generators in the higher level and (2) a parallel force/position control managing slippage events in the lower level. The control has been tested on an anthropomorphic robotic hand with prosthetic features (the IH2 hand) equipped with force sensors. Bi-digital and tri-digital grasping tasks with and without slip information have been carried out. The KUKA-LWR has been employed to perturb the grasp stability inducing controlled slip events. The acquired data demonstrate that the proposed control has the potential to adapt to changes in the environment and guarantees grasp stability, by avoiding object fall thanks to prompt slippage event detection.

The human hand is considered as the highest example of dexterous system capable of interacting with different objects and adapting its manipulation abilities to them. The control of poliarticulated prosthetic hands represents one important research challenge, typically aiming at replicating the manipulation capabilities of the natural hand. For this reason, this paper wants to propose a bio-inspired learning architecture based on parallel force/position control for prosthetic hands, capable of learning cyclic manipulation capabilities. To this purpose, it is focused on the control of a commercial biomechatronic hand (the IH2 hand) including the main features of recent poliarticulated prosthetic hands. The training phase of the hand was carried out in simulation, the parallel force/position control was tested in simulation whereas preliminary tests were performed on the real IH2 hand. The results obtained in simulation and on the real hand provide an important evidence of the applicability of the bio-inspired neural control to real biomechatronic hand with the typical features of a hand prosthesis.