Every day, billions of mobile network log data (commonly defined as Call Detailed Records, or CDRs) are generated by cell phones operators. These data provide inspiring insights about human actions and behaviors, which are essentials in the development of context aware appli- cations and services. This potential demand has fostered major research activities in a variety of domains such as social and economic development, urban planning, and health prevention. The major challenge of this thesis is to interpret CDR for human activity recognition, in the light of background knowledge of the CDR data context. Indeed each entry of the CDR is as- sociated with a context, which describes the temporal and spatial location of the user when a particular network data has been generated by his/her mobile devices. Knowing, by leveraging available Web 2.0 data sources, (e.g., Openstreetmap) this research thesis proposes to develop a novel model from combination of logical and statistical reasoning standpoints for enabling human activity inference in qualitative terms. The results aimed at compiling human behavior predictions into sets of classification tasks in the CDRs. Our research results show that Point of Interest (POI)s are a good proxy for predicting the content of human activities in an area. So the model is proven to be effective for predicting the context of human activity, when its total level could be efficiently observed from cell phone data records.

Mobile assistive robotics can play a key role to improve the autonomy and lifestyle of patients. In this context, RoboEye project aims to support people affected by mobility problems that range from very impairing pathologies (like ALS, amyotrophic lateral sclerosis) to old age. Any severe motor disability is a condition that limits the capability of interacting with the environment, even the domestic one, caused by the loss of the control on our own mobility. Although these pathologies are relatively rare, the number of people affected by this disease are increasing during the years. The focus of this project is the restore of persons’ mobility using novel technologies based on the gaze on a power wheelchair designed to enable the user to move easily and autonomously inside his home. A novel and intuitive control system was designed to achieve such a goal, in which a non-invasive eye tracker, a monitor, and a 3D camera represent some of the core elements. The developed prototype integrates, on a standard power wheelchair, functionalities from the mobile robotics field, with the main benefit of providing to the user two driving options and comfortable navigation. The most intuitive, and direct, modality foresees the continuous control of the frontal and angular velocities of the wheelchair by gazing at different areas of the monitor. The second, semi-autonomous, enables the navigation toward a selected point in the environment by just pointing and activating the wished destination while the system autonomously plans and follows the trajectory that brings the wheelchair there. The main goal is the development of shared control, combining direct control by the user with the comfort of autonomous navigation based on augmented reality markers. A first evaluation has been performed on a real test bed where specific motion metrics are evaluated. The designs of the control structure and driving interfaces were tuned thanks to the testing of some volunteers, habitual users of standard power wheelchairs. The driving modalities, especially the semi-autonomous one, were modelled and qualified to verify their efficiency, reliability, and safety for domestic usage.