Most of the neuroscientific results on synergies and their technical implementations in robotic systems, which are widely discussed throughout this book (see e.g. Chaps. 2, 3, 4, 8, 10, 12 and 13), moved from the analysis of hand kinematics in free motion or during the interaction with the external environment. This observation motivates both the need for the development of suitable and manageable models for kinematic recordings, as described in Chap. 14, and the calling for accurate and economic systems or “gloves” able to provide reliable hand pose reconstructions. However, this latter aspect, which represents a challenging point also for many human-machine applications, is hardly achievable in economically and ergonomically viable sensing gloves, which are often imprecise and limited. To overcome these limitations, in this chapter we propose to exploit the bi-directional relationship between neuroscience and robotic/artificial systems, showing how the findings achieved in one field can inspire and be used to advance the state of art in the other one, and vice versa. More specifically, our leading approach is to use the concept of kinematic synergies to optimally estimate the posture of a human hand using non-ideal sensing gloves. Our strategy is to collect and organize synergistic information and to fuse it with insufficient and inaccurate glove measurements in a consistent manner and with no extra costs. Furthermore, we will push forward such an analysis to 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, especially with a limited number of sensors. We will study the optimal design of gloves of different nature. Conclusions that can be drawn take inspiration from and might inspire further investigations on the biology of human hand receptors. Experimental evaluations of these techniques are reported and discussed.