The majority of the senior population lives alone at home. Falls can cause serious injuries, such as fractures or head injuries. These injuries can be an obstacle for a person to move around and normally practice his daily activities. Some of these injuries can lead to a risk of death if not handled urgently. In this paper, we propose a fall detection system for elderly people based on their postures. The postures are recognized from the human silhouette which is an advantage to preserve the privacy of the elderly. The effectiveness of our approach is demonstrated on two well-known datasets for human posture classification and three public datasets for fall detection, using a Support-Vector Machine (SVM) classifier. The experimental results show that our method can not only achieves a high fall detection rate but also a low false detection.

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This paper presents a vision-based system for recognizing when elderly adults fall. A fall is characterized by shape deformation and high motion. We represent shape variation using three features, the aspect ratio of the bounding box, the orientation of an ellipse representing the body, and the aspect ratio of the projection histogram. For motion variation, we extract several features from three blocks corresponding to the head, center of the body, and feet using optical flow. For each block, we compute the speed and the direction of motion. Each activity is represented by a feature vector constructed from variations in shape and motion features for a set of frames. A support vector machine is used to classify fall and non-fall activities. Experiments on three different datasets show the effectiveness of our proposed method.

This article presents a systemof a morphological analyzer of the Arabic language, by integrating several approaches and the viterbi algorithm. First approach is based on database for all thesurface patterns in the Arabic language, second approach is Buckwalter Arabic morphological analyzer and the last approach is based on finite state automaton. With the integration of correspondence tables between affixes in these approaches. The combination between these approaches in our analyzer is very important. Our analyzer is tested on a morphological corpus of 200,000 words, which generalize the words of the Arabic language. The effectiveness of the proposed approaches is demonstrated experimentally and the results obtained are comparable to the state of the art. Moreover, it shows the interest and the advantages of integrating these approaches are to improve our morphological analyzer.