  In this paper we study the problem of improving human hand pose sensing
device performance by exploiting the knowledge on how humans most frequently
use their hands in grasping tasks. In a companion paper we studied the problem
of maximizing the reconstruction accuracy of the hand pose from partial and
noisy data provided by any given pose sensing device (a sensorized "glove")
taking into account statistical a priori information. In this paper we consider
the dual problem of how to design pose sensing devices, i.e. how and where to
place sensors on a glove, to get maximum information about the actual hand
posture. We study the continuous case, whereas individual sensing elements in
the glove measure a linear combination of joint angles, the discrete case,
whereas each measure corresponds to a single joint angle, and the most general
hybrid case, whereas both continuous and discrete sensing elements are
available. The objective is to provide, for given a priori information and
fixed number of measurements, the optimal design minimizing in average the
reconstruction error. Solutions relying on the geometrical synergy definition
as well as gradient flow-based techniques are provided. Simulations of
reconstruction performance show the effectiveness of the proposed optimal
design.
