Falls are one of the major risks of injury for elderly living alone at home. Computer vision-based systems offer a new, low-cost and promising solution for fall detection. This paper presents a new fall-detection tool, based on a commercial RGB-D camera. The proposed system is capable of accurately detecting several types of falls, performing a real time algorithm in order to determine whether a fall has occurred. The proposed approach is based on evaluating the contraction and the expansion speed of the width, height and depth of the 3D human bounding box, as well as its position in the space. Our solution requires no pre-knowledge of the scene (i.e. the recognition of the floor in the virtual environment) with the only constraint about the knowledge of the RGB-D camera position in the room. Moreover, the proposed approach is able to avoid false positive as: sitting, lying down, retrieve something from the floor. Experimental results qualitatively and quantitatively show the quality of the proposed approach in terms of both robustness and background and speed independence.

Recognizing facial emotions is an important aspect of interpersonal communication that may be impaired in various neurological disorders: Asperger's syndrome, Autism, Schizoid Personality, Parkinsonism, Urbach-Wiethe, Amyotrophic Lateral Sclerosis, Bipolar Disorder, Depression, Alzheimer's desease. Altough it is not possible to define unique emotions, we can say that are mental states, physiological and psychophysiological changes associated with internal or external stimuli, both natural and learned. This paper highlights certain requirements that the specification approach would need to meet if the production of such tools were to be achievable. In particular, we present an innovative and still experimental tool to support diagnosis of neurological disorders by means of facial-expressionsmonitoring. At the same time, we propose a new study to measure several impairments of patients recognizing emotions ability, and to improve the reliability of using them in computer aided diagnosis strategies.

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Damage in the structure may raise a significant amount of maintenance cost and serious safety problems. Hence detection of the damage at its early stage is of prime importance. The main contribution pursued in this investigation is to propose a generic optimal methodology to improve the accuracy of positioning of the flaw in a structure. This novel approach involves a two-step process. The first step essentially aims at extracting the damagesensitive features from the received signal, and these extracted features are often termed the damage index or damage indices, serving as an indicator to know whether the damage is present or not. In particular, a multilevel SVM (support vector machine) plays a vital role in the distinction of faulty and healthy structures. Formerly, when a structure is unveiled as a damaged structure, in the subsequent step, the position of the damage is identified using Hilbert-Huang transform. The proposed algorithm has been evaluated in both simulation and experimental tests on a 6061 aluminum plate with dimensions 300 mm 9 300 mm 9 5 mm which accordingly yield considerable improvement in the accuracy of estimating the position of the flaw.