This paper proposes the use of artificial neural
networks (ANNs) in the framework of a biomechanical
hand model for grasping. ANNs enhance the model
capabilities as they substitute estimated data for the
experimental inputs required by the grasping algorithm
used. These inputs are the tentative grasping posture and
the most open posture during grasping. As a
consequence, more realistic grasping postures are
predicted by the grasping algorithm, along with the
contact information required by the dynamic
biomechanical model (contact points and normals).
Several neural network architectures are tested and
compared in terms of prediction errors, leading to
encouraging results. The performance of the overall
proposal is also shown through simulation, where a
grasping experiment is replicated and compared to the
real grasping data collected by a data glove device.