UNDERSTANDING RISK FACTORS OF ELDERLY INPATIENT FALLS USING CONTEXTUAL MODEL

The field of Information Systems is about bridging the digital and information divide. Advances in the digital world enable information to be stored and structured in a manner that facilitates effective use of the information for future modelling purposes. Elderly inpatient falls are a common global phenomenon, and an inpatient fall incident can have severe consequences for the patient, caregivers and the healthcare provider. An inpatient fall can result from many causes and its risk can be increased through the combination of these causes. Many risk factors of elderly inpatient falls have been reported in various papers in the literature. However, a logical comprehensive categorisation of all these factors does not currently exist. The objective of this research in progress is to come up with a generic categorisation of the risk factors for elderly inpatient falls alongside the usage of a contextual model to illustrate the inherent interactions amongst these various factors. In addition, we found that the effect of the interaction amongst some risk factors is time dependent which also needs to be incorporated in the contextual model. Such comprehensive categorisation and contextual risk model will help health providers in the process of profiling of an elderly inpatient with respect to his/her fall risk. It is useful to experts in health informatics in formulating models to automate this process

Clustering and Analysis of User Motions to Enhance Human Learning: A First Study Case with the Flip Bottle Challenge

International audienceMore and more domains such as industry, sport, medicine, Human Computer Interaction (HCI) and education analyze user motions to observe human behavior, follow and predict its action, intention and emotion, to interact with computer systems and enhance user experience in Virtual (VR) and Augmented Reality (AR). In the context of human learning of movements, existing software applications and methods rarely use 3D captured motions for pedagogical feedback. This comes from several issues related to the highly complex and dimensional nature of these data, and by the need to correlate this information with the observation needs of the teacher. Such issues could be solved by the use of machine learning techniques, which could provide efficient and complementary feedback in addition to the expert advice, from motion data. The context of the presented work is the improvement of the human learning process of a motion, based on clustering techniques. The main goal is to give advice according to the analysis of clusters representing user profiles during a learning situation. To achieve this purpose, a first step is to work on the separation of the motions into different categories according to a set of well-chosen features. In this way, allowing a better and more accurate analysis of the motion characteristics is expected. An experimentation was conducted with the Bottle Flip Challenge. Human motions were first captured and filtered, in order to compensate for hardware related errors. Descriptors related to speed and acceleration are then computed, and used in two different automatic approaches. The first one tries to separate the motions, using the computed descriptors, and the second one, compares the obtained separation with the ground truth. The results show that, while the obtained partitioning is not relevant to the degree of success of the task, the data are separable using the descriptors

The Semantics of History. Interdisciplinary Categories and Methods for Digital Historical Research

This paper aims at introducing and discussing the data modelling and labelling methods for interdisciplinary and digital research in History developed and used by the authors. Our approach suggests the development of a conceptual framework for interdisciplinary research in history as a much-needed strategy to ensure that historians use all vestiges from the past regardless of their origin or support for the construction of historical discourse. By labelling Units of Topography and Actors in a wide range of historical sources and exploiting the obtained data, we use the Monastery of Sant Genís de Rocafort (Martorell, Spain) as a lab example of our method. This should lead researchers to the development of an integrated historical discourse maximizing the potential of interdisciplinary and fair research and minimizing the risks of bias

Proceedings of the Workshop on Organic Public Catering at the 16th IFOAM Organic World Congress, 19th June 2008 in Modena, Italy

This proceedings shows the papers and/or power point slides of presentations held during a workshop included in the OWC about how to include organic food in school meals in various countries in Europe and Asia. the workshop was arranged by the iPOPY project (more at www.ipopy.coreportal.org)