Saint-Valery-sur-Somme – Le Mollenelle Nord

L’extension du lotissement du « Soleil Levant » sur la commune de Saint-Valery-sur-Somme menée par la commune a donné lieu à un diagnostic archéologique mis en œuvre sur la période du 14 décembre au 17 décembre 2010. L’emprise à sonder couvre 27 017 m2. Pour mener à bien cette mission, une équipe de deux personnes a été nécessaire sur cette durée. Les parcelles concernées se situent à l’est de Saint-Valery-sur-Somme et sont cernées par une zone pavillonnaire au nord et à l’est et au sud par l..

Longueau – Zac Jules-Verne, secteur nord-ouest Rocade

L’extension de la Zac « Jules-Verne » sur la commune de Longueau (Somme) menée par la Chambre de commerce et d’industrie d’Amiens a donné lieu à un diagnostic archéologique mis en œuvre sur la période du 5 janvier au 2 février 2010. L’emprise à sonder couvre 173 990 m2. Pour mener à bien cette mission, une équipe de deux personnes a été nécessaire sur cette durée. Les parcelles concernées se situent au nord-ouest de la rocade d’Amiens et sont cernées par une zone pavillonnaire au sud-ouest et..

Validation of an ambient system for the measurement of gait parameters

Fall risk in elderly people is usually assessed using clinical tests. These tests consist in a subjective evaluation of gait performed by healthcare professionals, most of the time shortly after the first fall occurrence. We propose to complement this one-time, subjective evaluation, by a more quantitative analysis of the gait pattern using a Microsoft Kinect. To evaluate the potential of the Kinect sensor for such a quantitative gait analysis, we benchmarked its performance against that of a gold-standard motion capture system, namely the OptiTrack. The “Kinect” analysis relied on a home-made algorithm specifically developed for this sensor, whereas the OptiTrack analysis relied on the “built-in” OptiTrack algorithm. We measured different gait parameters as step length, step duration, cadence, and gait speed in twenty-five subjects, and compared the results respectively provided by the Kinect and OptiTrack systems. These comparisons were performed using Bland-Altman plot (95% bias and limits of agreement), percentage error, Spearman’s correlation coefficient, concordance correlation coefficient and intra-class correlation. The agreement between the measurements made with the two motion capture systems was very high, demonstrating that associated with the right algorithm, the Kinect is a very reliable and valuable tool to analyze gait. Importantly, the measured spatio-temporal parameters varied significantly between age groups, step length and gait speed proving the most effective discriminating parameters. Kinect-monitoring and quantitative gait pattern analysis could therefore be routinely used to complete subjective clinical evaluation in order to improve fall risk assessment during rehabilitation

Les technologies de l'information et de la communication pour le maintien à domicile des personnes âgées.

La dégradation de la marche chez les personnes âgées est un facteur important de perte d'autonomie ayant pour conséquence une aggravation du risque de chutes. Pour évaluer le risque de chute de la personne âgée, l'équipe MAIA développe depuis plusieurs années un système de capture du mouvement fondé sur un système multi-caméras. L'objectif du stage était d'évaluer la justesse et la précision de ce système, en particulier pour la mesure de la longueur de pas. Pour cela une expérimentation avec des sujets sains a été dimensionnée et réalisée. Les mesures du système vidéo ont été ensuite comparées aux valeurs réelles. Ces valeurs réelles ont été obtenues en relevant les traces de pas laissées par les sujets. Un tampon imbibé d'encre placé à l'avant et l'arrière de la chaussure permettait en effet de marquer une bande de papier positionnée au sol. La comparaison a nécessité l'utilisation des méthodes statistiques. Ainsi nous avons pu déterminer si les longueurs de pas réelles et estimées étaient significativement non différentes. Dans le cas où les distributions des longueurs de pas étaient gaussiennes nous utilisions le test de Student, dans le cas contraire nous utilisions le test de Wilcoxon

Automating the timed up and go test using a depth camera

Fall prevention is a human, economic and social issue. The Timed Up and Go (TUG) test is widely used to identify individuals with a high fall risk. However, this test has been criticized because its “diagnostic” is too dependent on the conditions in which it is performed and on the healthcare professionals running it. We used the Microsoft Kinect ambient sensor to automate this test in order to reduce the subjectivity of outcome measures and to provide additional information about patient performance. Each phase of the TUG test was automatically identified from the depth images of the Kinect. Our algorithms accurately measured and assessed the elements usually measured by healthcare professionals. Specifically, average TUG test durations provided by our system differed by only 0.001 s from those measured by clinicians. In addition, our system automatically extracted several additional parameters that allowed us to accurately discriminate low and high fall risk individuals. These additional parameters notably related to the gait and turn pattern, the sitting position and the duration of each phase. Coupling our algorithms to the Kinect ambient sensor can therefore reliably be used to automate the TUG test and perform a more objective, robust and detailed assessment of fall risk

Human Activities Recognition with RGB-Depth Camera using HMM

International audienceFall detection remains today an open issue for improving elderly people security. It is all the more pertinent today when more and more elderly people stay longer and longer at home. In this paper, we propose a method to detect fall using a system made up of RGB-Depth cameras. The major benefit of our approach is its low cost and the fact that the system is easy to distribute and install. In few words, the method is based on the detection in real time of the center of mass of any mobile object or person accurately determining its position in the 3D space and its velocity. We demonstrate in this paper that this information is adequate and robust enough for labeling the activity of a person among 8 possible situations. An evaluation has been conducted within a real smart environment with 26 subjects which were performing any of the eight activities (sitting, walking, going up, squatting, lying on a couch, falling, bending and lying down). Seven out of these eight activities were correctly detected among which falling which was detected without false positives

Measuring frailty and detecting falls for elderly home care using depth camera

International audienceThis work concerns the development of low-cost am- bient systems for helping elderly to stay at home. Depth cameras allow a real-time analysis of the dis- placement of the person. We show that it is possible to recognize the activity of the person and to measure gait parameters from the analysis of simple features extracted from depth images. Activity recognition is based on Hidden Markov Models and performs fall detection. When a person is walking, the analysis of the trajectory of her centre of mass allows to mea- sure gait parameters that can then be used for frailty evaluation. We show that the proposed models are robust enough for activity classification, and that gait parameters measurement is accurate. We believe that such a system could be installed in the home of the elderly, while respecting privacy, since it relies on a local processing of depth images. Our system would be able to provide daily information on the person’s activity, the evolution of her gait parameters, and her habits, information that is useful for securing her and evaluating her frailty

A Gait Analysis Method Based on a Depth Camera for Fall Prevention

International audienceThis paper proposes a markerless system whose purpose is to help preventing falls of elderly people at home. To track human movements, the Microsoft Kinect camera is used which allows to acquire at the same time a RGB image and a depth image. Several articles show that the analysis of some gait parameters could allow fall risk assessment. We developed a system which extracts three gait parameters (the length and the duration of steps and the speed of the gait) by tracking the center of mass of the person. To check the validity of our system, the accuracy of the gait parameters obtained with the camera is evaluated. In an experiment, eleven subjects walked on an actimetric carpet, perpendicularly to the camera which filmed the scene. The three gait parameters obtained by the carpet are compared with those of the camera. In this study, four situations were tested to evaluate the robustness of our model. The subjects walked normally, making small steps, wearing a skirt and in front of the camera. The results showed that the system is accurate when there is one camera fixed perpendicularly. Thus we believe that the presented method is accurate enough to be used in real fall prevention applications