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Biol Cybern. 2011 Jul;105(1):1-19. doi: 10.1007/s00422-011-0444-8. Epub 2011 Jul 19.

A robotic model to investigate human motor control.

Author information

1
The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy. lenzi@ieee.org

Abstract

The role of the mechanical properties of the neuromuscular system in motor control has been investigated for a long time in both human and animal subjects, mainly through the application of mechanical perturbations to the limb during natural movements and the observation of its corrective responses. These methods have provided a wealth of insight into how the central nervous system controls the limb. They suffer, however, from the fact that it is almost impossible to separate the active and passive components of the measured arm stiffness and that the measurement may themselves alter the stiffness characteristic of the arm. As a complement to these analyses, the implementation of a given neuroscientific hypothesis on a real mechanical system could overcome these measurement artifact and provide a tool that is, under full control of the experimenter, able to replicate the relevant functional features of the human arm. In this article, we introduce the NEURARM platform, a robotic arm intended to test hypotheses on the human motor control system. As such, NEURARM satisfies two key requirements. First, its kinematic parameters and inertia are similar to that of the human arm. Second, NEURARM mimics the main physical features of the human actuation system, specifically, the use of tendons to transfer force, the presence of antagonistic muscle pairs, the passive elasticity of muscles in the absence of any neural feedback and the non-linear elastic behaviour. This article presents the design and characterization of the NEURARM actuation system. The resulting mechanical behaviour, which has been tested in joint and Cartesian space under static and dynamic conditions, proves that the NEURARM platform can be exploited as a robotic model of the human arm, and could thus represent a powerful tool for neuroscience investigations.

PMID:
21769741
DOI:
10.1007/s00422-011-0444-8
[Indexed for MEDLINE]

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