The automatic assessment of the level of independence of a person, based on the recognition of a set of Activities of Daily Living, is among the most challenging research fields in Ambient Intelligence. The article proposes a framework for the recognition of motion primitives, relying on Gaussian Mixture Modeling and Gaussian Mixture Regression for the creation of activity models. A recognition procedure based on Dynamic Time Warping and Mahalanobis distance is found to: (i) ensure good classification results; (ii) exploit the properties of GMM and GMR modeling to allow for an easy run-time recognition; (iii) enhance the consistency of the recognition via the use of a classifier allowing unknown as an answer.

A new feedback control model is provided that allows a wheeled vehicle to follow a prescribed path. Differently from all other methods in the literature, the method that is proposed neither requires the computation of a projection of the robot position on the path, nor does it need to consider a moving virtual target to be tracked. Nevertheless, it guarantees asymptotic convergence to a generic 2-D curve which can be represented through its implicit equation in the form f ( x , y )=0, and it puts no bounds on the initial position of the vehicle, provided that ∇ f ≠ 0 .

Because photovoltaic (PV) plants require periodic maintenance, using unmanned aerial vehicles (UAV) for inspections can help reduce costs. Usually, the thermal and visual inspection of PV installations works as follows. A UAV equipped with a global positioning system (GPS) receiver is assigned a flight zone, which the UAV will cover back and forth to collect images to be subsequently composed in an orthomosaic. When doing this, the UAV typically flies at a height above the ground that is appropriate to ensure that images overlap even in the presence of GPS positioning errors. However, this approach has two limitations. First, it requires covering the whole flight zone, including “empty” areas between PV module rows. Second, flying high above the ground limits the resolution of the images to be subsequently inspected. The article proposes a novel approach using an autonomous UAV with an RGB and a thermal camera for PV module tracking through segmentation and visual servoing, which does not require a GPS except for measuring the “small” relative displacement between a PV module row and the next one. With this solution, the UAV moves along PV module rows at a lower height than usual and inspects them back and forth in a boustrophedon way by ignoring “empty” areas with no PV modules. Experimental tests performed in simulation and at an actual PV plant are reported, showing a tracking error lower than 0.2 m in most situations when moving at 1.2 m/s.