The recommendation that there must be a balance between insulin, food, and exercise to keep diabetes under control provides an opportunity for developing mobile applications for self-management of the disease. Real predictions can improve the quality of patients’ lives by avoiding unwanted events, namely, hypoglycaemia. We proposed a hypoglycaemia prediction approach combining information fusion and classifiers consensus to predict the risk of hypoglycaemia in a 24-h window. First, we train a multi-classifiers system from different sources of different patients. After using data from a unique patient, we performed the prediction of the risk of hypoglycaemia and evaluate the consensus decision of the single models resulting from the learning process. The predictions were performed for 54 patients from the University of California Irvine diabetes dataset. The results from classifiers consensus decision provide very promising results, which are acceptable considering that we used sparse data and data from self-monitoring blood glucose. Our approach shows that with a 24-h window is possible to catch appropriate patterns associated with the risk of hypoglycaemia and proposed a solution that can improve the hypoglycaemia prediction with a higher specificity, i.e. less false alarms, when compared with similar literature.

We describe a method for user tracking and localization based on textile capacitive sensor arrays placed under the floor. The sensor array is a commercial product (SensFloor®) that can be installed under any standard floor type (from carpet to stone) and is able to detect objects (including the user's foot) being placed on it. The challenges addressed in this paper are (1) how to map sequences of such signals onto user trajectories and (2) how to correlate the steps detected by the SensFloor system with the step detection based on a wearable accelerometer as means of user identification. Footstep detection is performed online on the devices, which are seamlessly integrated with the floor's wireless sensor network. Initial experiments performed over a week in a real life office environment show the ability to track multiple humans and to identify up to three users walking in a narrow corridor at the same time.

In this paper, we show how a team of autonomous mobile robots, which drive in formation, can be endowed with basic cognitive capabilities. The formation control relies on the leader-follower strategy, with three main pair-wise configurations: column, line and oblique. Furthermore, non-linear attractor dynamics are used to generate basic robotic behaviors (i.e. follow-the-leader and avoid obstacles). The control architecture of each follower integrates a representation of the leader (target) direction, which supports leader detection, selection between multiple leaders (decision) and temporary estimation of leader direction (short-term memory during occlusion and prediction). Formalized as a dynamic neural field, this additional layer is smoothly integrated with the motor movement control system. Experiments conducted in our 3D simulation software, as well as results from the implementation in middle size robotic platforms, show the ability for the team to navigate, whilst keeping formation, through unknown and unstructured environments and is robust against ambiguous and temporarily absent sensory information.

Electrical distribution utilities have been dealing with the problem of estimating distribution network load diagrams, either for operation studies or in forecasting models for planning purposes. Load curve assessment is essential for the efficient management of electric distribution systems. Demand-side management (DSM) constitutes what is often a very effective integrated resource, because the load reduction is at the customer site and the information about its impact on load figures is crucial, mainly if we think about its effects in network expansion planning. In this paper, we present a fuzzy inference tool to model the demand-side actions through inference mechanisms aiming at explicitly including the influence of those factors in the estimation of load at a spatial load distribution level. This tool allows the DSM effects on the network and distribution/generation politics to be anticipated, integrated in a philosophy of sustainable development. The approach is also capable of explicitly including these influences in a spatial forecasting environment and easily reflecting the predicted results in the marginal costs of a real electricity network represented on a geographical basis.