In the last decade, several localization systems have been proposed, some of them achieving an accuracy in the order of decimeters. Nonetheless, a broad range of these methods may reveal unattractive for low-cost and resource-bounded mobile application scenarios. Indeed, significant constraints stem from costs and availability of the required technological infrastructure for the target environment. In addition, resources necessary to train and run the localization algorithm may pose stringent requirements in terms of time, power and computational. In the aforementioned context, this paper presents an effective radio-based positioning algorithm able to localize the user by exploiting a graph-based representation of the environment. The graph is created by analyzing the received signal level and the movement directions of a mobile device with respect to a set of real and virtual landmarks. The comparison with a commercial localization system proves the effectiveness and accuracy of the proposed approach.

In humans, the system that controls several biomechanical tasks has a modular organization (muscle synergies). While the muscle synergies in human walking were already studied deeply, this phenomenon in robotic system is not so explored and could bring simpler controllers requiring a minor number of parameters. So, the purpose of this work is to acquire and interpret synergies from selected joint signals in order to answer some specific research questions regarding the verification of synergistic control of a biped robot in a walking activity, e.g, the number of synergies, their activation profiles, their mechanical role, their quality to reproduce/control walking motions, etc. The selected robotic system was the Darwin-OP robot when walking both with a model based and a bio-inspired controller. Tests were performed with the simulated and real robot. The results appear to suggest that the extraction of synergies are quite useful for the robotic locomotion, since with only 2 synergies, is possible to reconstruct the signals of the studied joints. The biomechanical role for each determined synergy is described based on the functional transformation between sensorimotor signals and biomechanical output. Besides, the robustness of the determined synergies is verified in the locomotor activation pattern across strides and across speeds when the mechanical requirements do not change to a large extent.