Wearable sensors and systems have become increasingly popular for diverse applications. An emerging technology for physical activity assessment is Smart Textile Systems (STSs), comprised of sensitive/actuating fiber, yarn, or fabric that can sense an external stimulus. All required components of an STS (sensors, electronics, energy supply, etc.) can be conveniently embedded into a garment, providing a fully textile-based system. Thus, STSs have clear potential utility for measuring health-relevant aspects of human activity, and to do so passively and continuously in diverse environments. For these reasons, STSs have received increasing interest in recent studies. Despite this, however, limited evidence exists to support the implementation of STSs during diverse applications. Our long-term goal was to assess the feasibility and accuracy of using an STS to monitor human activities. Our immediate objective was to investigate the accuracy of an STS in three representative applications with respect to occupational scenarios, healthcare, and activities of daily living. A particular STS was examined, consisting of a smart socks (SSs), using textile pressure sensors, and smart undershirt (SUS), using textile strain sensors. We also explored the relative merits of these two approaches, separately and in combination. Thus, five studies were completed to design and evaluate the usability of the smart undershirt, and investigate the accuracy of implementing an STS in the noted applications. Input from the SUS led to planar angle estimations with errors on the order of 1.3 and 9.4 degrees for the low-back and shoulder, respectively. Overall, individuals preferred wearing a smart textile system over an IMU system and indicated the former as superior in several aspects of usability. In particular, the short-sleeved T-shirt was the most preferred garments for an STS. Results also indicated that the smart shirt and smart socks, both individually and in combination, could detect occupational tasks, abnormal and normal gaits, and activities of daily living with greater than 97% accuracy. Based on our findings, we hope to facilitate future work that more effectively quantifies sedentary periods that may be deleterious to human health, as well as detect activity types that may be help or hinder health and fitness. Such information may be of use to individuals and workers, healthcare providers, and ergonomists. More specifically, further analyses from this investigation could provide strategies for: (a) modifying a sedentary lifestyle or work scenario to a more active one, and (b) helping to more accurately identify occupational injury risk factors associated with human movement. / PHD / The use of interactive or “smart” textiles that have sensing material(s) incorporated into them supports an emerging technology for physical activity assessment called Smart Textile Systems (STSs). STSs are an increasingly useful technology for researchers, athletes, patients, and others. Our aims in the current study were the development and assessment of a new smart undershirt (SUS) that was designed to monitor low-back and shoulder motions, and to evaluate the preferred placement and usability of two STSs. We also assessed the accuracy of two smart garments, smart socks (SSs) and the SUS, both individually and in combination. Accuracy was evaluated in terms of the ability of these systems to distinguish between diverse simulated occupational tasks, normal and abnormal walking patterns, and several typical daily activities. Our investigation indicated that STSs could discriminate between different human activities common in three domains: occupational scenarios, healthcare, and activities of daily life. We also found that both smart garments (i.e., SSs and SUS) provided similar accuracy for activity classification, typically exceeding 97%, and thus there was no clear superiority between these two smart garments. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating and monitoring a range of human activities. Use of this technology in the future may have positive implications for health promotion.

ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / As organizations move toward cloud [1] computing environments, data security challenges will begin to take precedence over network security issues. This will potentially impact telemetry post processing in a myriad of ways. After reviewing how data security tools like Enterprise Rights Management (ERM), Enterprise Key Management (EKM), Data Loss Prevention (DLP), Database Activity Monitoring (DAM), and tokenization are impacting cloud security, their effect on telemetry post-processing will also be examined. An architecture will be described detailing how these data security tools can be utilized to make telemetry post-processing environments in the cloud more robust.

Oleanolic and ursolic acid (OA and UA) are triterpenic acids with diverse biological activities that are of interest to the pharmaceutical industry. To investigate the scope for producing these compound using cell suspension cultures of Salvia species, calli from S. officinalis, S. virgata and S. fruticosa were induced using several plant growth regulator (PGR) combinations. Eleven lines were selected for suspension induction from a pool of calli. Six suspension cultures were established successfully and cultivated in the Respiration Activity MOnitoring System® (RAMOS®) to obtain online data on their growth kinetics and to establish appropriate sampling schedules for the determination of their OA and UA production. Based on their observed growth behaviour, OA and UA contents, and aggregation properties, one suspension culture from each studied Salvia species was selected for further optimisation. The μmax values for these suspension cultures ranged from 0.20 to 0.37°d-1, their OA and UA contents were greater than 1.3 and 1.2 mg g-1, respectively, and they afforded maximum volumetric yields of 21.0 mg l-1 for OA and 32.8 mg l-1 for UA. These results will be useful in the development of a refined Salvia suspension-based process for OA and UA production.

Le vieillissement de la population est un phénomène mondial qui s'accompagne d'une augmentation du nombre de patients atteints de maladies chroniques, ce qui oblige à repenser le système de santé en amenant le suivi de santé et les soins au domicile et dans la cité.En considérant que l'activité est un signe visible de l'état de santé, cette thèse cherche à proposer un moyen technologique pour le suivi des activités de plusieurs personnes dans un living lab composé d'un appartement et de la cité qui l'entoure.En effet, le maintien d'une activité physique soutenue, et en particulier d'une activité sociale fait partie intégrante de la bonne santé d'une personne, il doit donc être étudié au même titre que les capacités à effectuer les activités de la vie quotidienne.Cette étude a permis la mise en place d'une plateforme de conception collaborative et de test grandeur nature autour de la santé à domicile et dans la cité : le living lab de l'INL.Ce dernier a été le théâtre de premières expérimentations permettant de valider la capacité du living lab à la fois de fusionner des données d'activité venant d'un ensemble de capteurs hétérogènes, mais également d'évoluer en intégrant des nouvelles technologies et services.Les scénarios collaboratifs étudiés permettent une première approche de l'analyse de la collaboration par la détection des présences simultanées de plusieurs personnes dans la même pièce. Ces résultats préliminaires sont encourageants et seront complétés lors de captures d'activité plus fines et incluant plus de capteurs dans les mois à venir / The ageing of the population is a global phenomenon which comes with an increase of the amount of patients suffering from chronic diseases. It forces to rethink the healthcare by bringing health monitoring and care at home and in the city.Considering the activity as a visible indication of the health status, this thesis seeks to provide technological means to monitor several people's activities in a living lab composed of an apartment and the city around.Indeed, maintaining substantial physical activity, in particular social activity accounts fo an important part of a person's good health status. Hence, it must be studied as well as the ability to perform the activities of daily living.This study enabled the implementation of a platform for collaborative design and full-scale experimentation concerning healthcare at home and in the city: the INL living lab.The latest was the theatre of some first experimentations which highlighted the living lab ability to perform activity data fusion from a set of heterogeneous sensors, and also to evolve by integrating new technologies and services.The studied collaborative scenarios enable a first approach of the collaboration analysis by detecting the simultaneous presence of several people in the same room. These preliminary results are encouraging and will be completed by more precise measurements which will include more sensors in the coming months

In recent years businesses around the world have been facing the challenges of a rapidly changing business and technology environment. As a result, organisations are paying more attention to supporting business process management by adapting to the dynamic environment. With the increased complexity and uncertainty in business operations, adaptive and collaborative business process and exception management (EM) are gaining attention. In the logistics industry, the growing importance of logistics worldwide as well as the increasing complexity of logistics networks and the service requirement of customers has become a challenge. The current logistics exceptions are managed using human brain power together with the traditional workflow technology-based supply-chain management or other logistics tools. The traditional workflow technology models and manages business processes and anticipated exceptions based on predefined logical procedures of activities from a centralised perspective. This situation offers inadequate decision support for flexibility and adaptability in logistics EM. The traditional workflow technology is also limited to monitoring the logistics activities in real-time to detect and resolve exceptions in a timely manner. To mitigate these problems, an intelligent agent incorporating business activity monitoring (BAM) decision support approach in logistics EM has been proposed and investigated in this research. This research creates and evaluates two IT designed artefacts (conceptual framework and prototype) intended to efficiently and automatically monitor and handle logistics exceptions. It follows a design science research strategy. The design, development, and evaluation adhere to the principles enunciated in the design science literature. The aim of this research is to solve the important logistics EM problem in a more effective and efficient manner. Two designed artefacts were strictly informed by, and incorporated with, three different theories. An exploratory case study and a later confirmatory case study assisted in the rigorous derivation of the design and framework. The results of the confirmatory case study were used in particular to refine the designed artefacts. Such a build-and-evaluate loop iterated several times before the final designed artefacts were generated. The designed artefacts were then evaluated empirically via a field experiment. The research included both a technical presentation and a practical framing in terms of application in the logistics exception monitoring and handling domain. In this study, there were three interrelated research phases. In the first research phase, a decision-making conceptual framework (an artefact) for design and development of real-time logistics EM system was developed. To enable more efficient decision support practices for logistics EM, the characteristics of logistics exceptions were first examined and identified. The logistics exception analysis was conducted through a comprehensive literature review and an exploratory case study conducted in a major logistics company in Australia. The logistics exceptions were then classified into known and knowable categories, based on the Cynefin sense-making framework (Snowden, 2002). On the basis of the logistics analysis, informed by Gartner’s three-layer BAM architecture (Dresner, 2003), the Cynefin sense-making framework decision models (Snowden, 2002), and Simon’s (1977) decision-making/problem-solving process, the real-time logistics EM conceptual framework was depicted. The BAM architecture provided the real-time decision support. Based on Cynefin’s decision model, adaptive business process flow was chosen for known and knowable logistics exceptions to speed up the decision-making process. In addition, Simon’s process theory was deployed to model the diagnosing process for known and knowable logistics exceptions. This conceptual model guided the analysis, design, and development for real-time logistics EM systems. In the second research phase, based on the logistics EM conceptual framework, a Web-service-multi-agent-based real-time logistics EM system (an artefact) was designed and developed. Intelligent agent technology was applied to deal with the complex, dynamic, and distributed logistics EM processes. Web-services techniques were proposed for more interoperability and scalability in network-based business environment. By integrating agent technology with Web-services to make use of the advantages from both, this approach provided a more intelligent, flexible, autonomous, and comprehensive solution to real-time logistics EM. In the third research phase, two designed artefacts were evaluated via a confirmatory case study and a field experiment. The confirmatory case study was conducted to collect feedback on the two designed artefacts (i.e., conceptual framework and prototype system) to refine them. The field experiment was then conducted to investigate the proposed logistics EM prototype system decision support effectiveness and efficiency by comparing the human decision-making performance with/without the logistics EM decision support facility. The evaluation results indicated that the proposed logistics EM prototype outperformed the one without logistics EM decision support in terms of more efficient decision process, higher decision outcome quality, and better user perception. The two designed artefacts were the major contributions of this research. They add knowledge to decision theory and practice. The artefacts are the real-time extension for Simon’s (1977) classic decision-making/problem-solving process model in logistics EM by incorporating BAM (Dresner, 2003). In addition, by adding the Cynefin sense-making framework (Snowden, 2002), the artefacts provide a more efficient decision-making routine for logistics EM. This research provides the first attempt (to the best of the researcher’s knowledge) to design a real-time logistics EM decision support mechanism based on decision science theories. To demonstrate the usability of the proposed conceptual framework, a logistics EM decision support prototype was designed, developed, and evaluated. For practice, the logistics exceptions classification, logistics EM conceptual framework, and incorporating agent technologies into logistics EM all will assist logistics companies to develop their logistics exception handling decision-making strategies and solutions.

In recent years businesses around the world have been facing the challenges of a rapidly changing business and technology environment. As a result, organisations are paying more attention to supporting business process management by adapting to the dynamic environment. With the increased complexity and uncertainty in business operations, adaptive and collaborative business process and exception management (EM) are gaining attention. In the logistics industry, the growing importance of logistics worldwide as well as the increasing complexity of logistics networks and the service requirement of customers has become a challenge. The current logistics exceptions are managed using human brain power together with the traditional workflow technology-based supply-chain management or other logistics tools. The traditional workflow technology models and manages business processes and anticipated exceptions based on predefined logical procedures of activities from a centralised perspective. This situation offers inadequate decision support for flexibility and adaptability in logistics EM. The traditional workflow technology is also limited to monitoring the logistics activities in real-time to detect and resolve exceptions in a timely manner. To mitigate these problems, an intelligent agent incorporating business activity monitoring (BAM) decision support approach in logistics EM has been proposed and investigated in this research. This research creates and evaluates two IT designed artefacts (conceptual framework and prototype) intended to efficiently and automatically monitor and handle logistics exceptions. It follows a design science research strategy. The design, development, and evaluation adhere to the principles enunciated in the design science literature. The aim of this research is to solve the important logistics EM problem in a more effective and efficient manner. Two designed artefacts were strictly informed by, and incorporated with, three different theories. An exploratory case study and a later confirmatory case study assisted in the rigorous derivation of the design and framework. The results of the confirmatory case study were used in particular to refine the designed artefacts. Such a build-and-evaluate loop iterated several times before the final designed artefacts were generated. The designed artefacts were then evaluated empirically via a field experiment. The research included both a technical presentation and a practical framing in terms of application in the logistics exception monitoring and handling domain. In this study, there were three interrelated research phases. In the first research phase, a decision-making conceptual framework (an artefact) for design and development of real-time logistics EM system was developed. To enable more efficient decision support practices for logistics EM, the characteristics of logistics exceptions were first examined and identified. The logistics exception analysis was conducted through a comprehensive literature review and an exploratory case study conducted in a major logistics company in Australia. The logistics exceptions were then classified into known and knowable categories, based on the Cynefin sense-making framework (Snowden, 2002). On the basis of the logistics analysis, informed by Gartner’s three-layer BAM architecture (Dresner, 2003), the Cynefin sense-making framework decision models (Snowden, 2002), and Simon’s (1977) decision-making/problem-solving process, the real-time logistics EM conceptual framework was depicted. The BAM architecture provided the real-time decision support. Based on Cynefin’s decision model, adaptive business process flow was chosen for known and knowable logistics exceptions to speed up the decision-making process. In addition, Simon’s process theory was deployed to model the diagnosing process for known and knowable logistics exceptions. This conceptual model guided the analysis, design, and development for real-time logistics EM systems. In the second research phase, based on the logistics EM conceptual framework, a Web-service-multi-agent-based real-time logistics EM system (an artefact) was designed and developed. Intelligent agent technology was applied to deal with the complex, dynamic, and distributed logistics EM processes. Web-services techniques were proposed for more interoperability and scalability in network-based business environment. By integrating agent technology with Web-services to make use of the advantages from both, this approach provided a more intelligent, flexible, autonomous, and comprehensive solution to real-time logistics EM. In the third research phase, two designed artefacts were evaluated via a confirmatory case study and a field experiment. The confirmatory case study was conducted to collect feedback on the two designed artefacts (i.e., conceptual framework and prototype system) to refine them. The field experiment was then conducted to investigate the proposed logistics EM prototype system decision support effectiveness and efficiency by comparing the human decision-making performance with/without the logistics EM decision support facility. The evaluation results indicated that the proposed logistics EM prototype outperformed the one without logistics EM decision support in terms of more efficient decision process, higher decision outcome quality, and better user perception. The two designed artefacts were the major contributions of this research. They add knowledge to decision theory and practice. The artefacts are the real-time extension for Simon’s (1977) classic decision-making/problem-solving process model in logistics EM by incorporating BAM (Dresner, 2003). In addition, by adding the Cynefin sense-making framework (Snowden, 2002), the artefacts provide a more efficient decision-making routine for logistics EM. This research provides the first attempt (to the best of the researcher’s knowledge) to design a real-time logistics EM decision support mechanism based on decision science theories. To demonstrate the usability of the proposed conceptual framework, a logistics EM decision support prototype was designed, developed, and evaluated. For practice, the logistics exceptions classification, logistics EM conceptual framework, and incorporating agent technologies into logistics EM all will assist logistics companies to develop their logistics exception handling decision-making strategies and solutions.

The main aim of the project is to develop an algorithm which will classify the activity performed by a human who is carrying a smart phone. The day to day life made humans very busy at work and during daily activities, mostly elderly people who are at home have an important need to monitor their activity by others when they are alone, if they are inactive for a long time without movement, or in some situations like if they have fallen down, became unconscious for sometime or seized with a cardiac arrest etc… will help the observer to know the state of activity of person being monitored. In this project we develop an algorithm to know the activity of a person using accelerometer available in Smartphone. We have extracted the Smartphone accelerometer data using an application called accelerometer data logger version 1.0 available in Smartphone market and have processed the data in Matlab for classifying the different activities of human being into static and dynamic activity, if the activity is dynamic then further classification into walking or running is performed with the algorithm. We implemented smoothening filters for data analysis and statistical techniques like standard deviation, mean and signal magnitude analysis for activity classification. This classification algorithm will let us know the type of activity either static or dynamic and then classify the position of the user, such as walking, running or ideal, which can provide useful information for the observer who is monitoring the activities of wearer, and which will help the wearer for his daily living. To bring out the extensive use of algorithm and to provide valuable feedback for wearer regarding his activities, energy spent by user during the activities was calculated at a given time using regression methods and was implemented in the algorithm. The developed model was able to estimate the energy spent by the user, the observations recorded were almost similar to the treadmill data which is taken as a standard for our model and the mean error is not more than ±2 for 30 observations. The final results when compared with the standard model was proved to be 93 % accurate on average of 30 subjects data which is used for verifying the algorithm developed. With these set of results we have come to a conclusion that algorithm can be easily implemented in a real time Smartphone application with low false predictions and can be implemented with low computational cost and fast real-time response. In future our classification algorithm can also be used in military applications where one can know what the soldier is doing without actually seeing him and additionally it can be proved as a support system in athlete’s health monitoring and training. / I denna modell har vi utvecklat en algoritm för aktivitetsklassificeringoch energiförbrukning uppskattning , vilket hjälper oss i övervakningen daglig mänsklig aktivitet med större noggrannhet . Resultaten valideras med standard energiförbrukning teknik och aktivitetsklassificeringsvideoobservationer. Vi vill att denna modell ska integreras i smarta mobiltelefoner för att ge slutanvändaren en vänlig atmosfär utan att lägga några komplicerade funktioner för hantering av utrustningen . Denna modell är mycket användbart i klinisk uppföljning av patienterna , kommer det att hjälpa oss att övervaka gamla , sjuka och utvecklingsstörda personens aktivitetsidentifiering och hjälper oss i nära övervakning av patienterna men fysiskt att vara borta från dem . Våra bärbara MEMS baserade treaxlig accelerometer system baserat smartphone kompatibel algoritm tillsammans med andra fysiologiska övervakningsparametrarkommer att ge korrekt övervakning rörelse och energiförbrukning uppskattning för klinisk analys . Denna modell är användbar för analys och övervakning av grupp -och enskilda individer , vilket kommer att leda till att spåra deras rörelser och en framgångsrik räddningsaktion för att rädda dem från dödliga sjukdomar och förebygga risker när de är skadade . Framtida arbete kommer att vara kontinuerlig övervakning av ämnen enskild aktivitet tillsammans med gruppaktivitet . Identifiera hållning övergång av olika aktiviteter i en kort tid som att springa till sittande , sittande till stående , står att krypa etc. / 0091-7660885577

L’indexation par le contenu de lifelogs issus de capteurs portées a émergé comme un enjeu à forte valeur ajoutée permettant l’exploitation de ces nouveaux types de donnés. Rendu plus accessible par la récente disponibilité de dispositifs miniaturisés d’enregistrement, les besoins pour l’extraction automatique d’informations pertinents générées par autres applications, la localisation en environnement intérieur est un problème difficile à l’analyse de telles données.Beaucoup des solutions existantes pour la localisation fonctionnent insuffisamment bien ou nécessitent une intervention important à l’intérieur de bâtiment. Dans cette thèse, nous abordons le problème de la localisation topologique à partir de séquences vidéo issues d’une camera portée en utilisant une approche purement visuelle. Ce travail complète d’extraction des descripteurs visuels de bas niveaux jusqu’à l’estimation finale de la localisation à l’aide d’algorithmes automatiques.Dans ce cadre, les contributions principales de ce travail ont été faites pour l’exploitation efficace des informations apportées par descripteurs visuels multiples, par les images non étiquetées et par la continuité temporelle de la vidéo. Ainsi, la fusion précoce et la fusion tardive des données visuelles ont été examinées et l’avantage apporté par la complémentarité des descripteurs visuels a été mis en évidence sur le problème de la localisation. En raison de difficulté à obtenir des données étiquetées en quantités suffisantes, l’ensemble des données a été exploité ; d’une part les approches de réduction de dimensionnalité non-linéaire ont été appliquées, afin d’améliorer la taille des données à traiter et la complexité associée ; d’autre part des approches semi-supervisés ont été étudiées pour utiliser l’information supplémentaire apportée par les images non étiquetées lors de la classification. Ces éléments ont été analysé séparément et on été mis en œuvre ensemble sous la forme d’une nouvelle méthode par co-apprentissage temporelle. Finalement nous avons également exploré la question de l’invariance des descripteurs, en proposant l’utilisation d’un apprentissage invariant à la transformation spatiale, comme un autre réponse possible un manque de données annotées et à la variabilité visuelle.Ces méthodes ont été évaluées sur des séquences vidéo en environnement contrôlé accessibles publiquement pour évaluer le gain spécifique de chaque contribution. Ce travail a également été appliqué dans le cadre du projet IMMED, qui concerne l’observation et l’indexation d’activités de la vie quotidienne dans un objectif d’aide au diagnostic médical, à l’aide d’une caméra vidéo portée. Nous avons ainsi pu mettre en œuvre le dispositif d’acquisition vidéo portée, et montrer le potentiel de notre approche pour l’estimation de la localisation topologique sur un corpus présentant des conditions difficiles représentatives des données réelles. / Visual lifelog indexing by content has emerged as a high reward application. Enabled by the recent availability of miniaturized recording devices, the demand for automatic extraction of relevant information from wearable sensors generated content has grown. Among many other applications, indoor localization is one challenging problem to be addressed.Many standard solutions perform unreliably in indoors conditions or require significant intervention. In this thesis we address from the perspective of wearable video camera sensors using an image-based approach. The key contribution of this work is the development and the study of a location estimation system composed of diverse modules, which perform tasks ranging from low-level visual information extraction to final topological location estimation with the aid of automatic indexing algorithms. Within this framework, important contributions have been made by efficiently leveraging information brought by multiple visual features, unlabeled image data and the temporal continuity of the video.Early and late data fusion were considered, and shown to take advantage of the complementarities of multiple visual features describing the images. Due to the difficulty in obtaining annotated data in our context, semi-supervised approaches were investigated, to use unlabeled data as additional source of information, both for non-linear data-adaptive dimensionality reduction, and for improving classification. Herein we have developed a time-aware co-training approach that combines late data-fusion with the semi-supervised exploitation of both unlabeled data and time information. Finally, we have proposed to apply transformation invariant learning to adapt non-invariant descriptors to our localization framework.The methods have been tested on controlled publically available datasets to evaluate the gain of each contribution. This work has also been applied to the IMMED project, dealing with activity recognition and monitoring of the daily living using a wearable camera. In this context, the developed framework has been used to estimate localization on the real world IMMED project video corpus, which showed the potential of the approaches in such challenging conditions.

Main focus of the thesis lies in the corporate management decision support deploying and using IT / ICT. Specific technology described is Business Activity Monitoring. The contribution of the work lies primarily in two planes. The first plane is to create as far as the most comprehensive view of the BAM. The findings are collected from different directions and areas. The first direction of research is focused on the development of Business Intelligence and description of BAM as a trend of BI, including the stages of development and projections into the future. The second direction focuses primarily on a detailed circumscribe of BAM. Its definition, deployment assumptions, basic models, the way how business can benefit from BAM usage. The third guideline shows the classification of BAM surrounded by the other / similar technologies and business solutions -- BI and BSM, and the search key differences. The second level of the work is to support the AML implementation in a specific environment of banks using BAM. This is a practical demonstration of the possibility of using BAM in practice. Basic design solution lies in the analysis of risks arising from the law and the current state of the solution. By mapping of banking processes and searching for points of risk it is then possible to deploy these risks BAM tools for their management. Motion of support lies mainly in conceptual terms.

La Tension dans un Service d’Urgences (SU) est un déséquilibre entre le flux de charge des soins et la capacité de prise en charge sur une durée suffisante pouvant entrainer des conséquences néfastes au bon fonctionnement. Elle se reflète par la surcharge des locaux, l’allongement des délais de traitement et d’attente. Ce qui provoque à la fois l’insatisfaction des patients et l’anxiété du personnel. Cette thèse s’inscrit dans le cadre du projet HOST financé par le programme ANR-TECSAN-2011 afin d’élaborer un Système d'Aide à la Gestion de la Tension (SAGeT) assurant trois objectifs:1. L’évaluation multicritère grâce à une panoplie d’indicateurs agrégés par la logique floue afin de résoudre la subjectivité du ressentie humain de la tension. Chaque scénario d’évaluation déclenche des règles de décision spécifiques ciblant ainsi des points de défaillance à surveiller.2. L’anticipation de la demande sur différents horizons temporels : l’application des méthodes SARIMA et SARIMAX est justifiée par la saisonnalité des chroniques de visites et l’influence de certains paramètres externes (épidémies, vacances, météo). De plus, la qualité de l’information venant de l’historique a été améliorée par une recomposition d’historique basée sur la vraisemblance journalière.3. L’amélioration de la gestion des flux et le pilotage de l’activité puisque l’utilisation de SAGeT comme un tableau de bord offre une vue macro sur l’ensemble de l’activité (lits occupés, patients en attente, durées de passages prévisionnelles et allongements excessifs). Les simulations traitent des vrais scénarios de tension observés entre 2011 et 2013 dans le SU Pédiatriques Jeanne de Flandre du CHRU-Lille. / He strain in an Emergency Department (ED) is an imbalance between the total demand load of healthcare treatment and resources ability to support it during a convenient horizon, which may results negative consequences on the smooth running of the activity. It is reflected by overcrowding, longer treatment and waiting times which causes both patients dissatisfaction and anxiety of personnel. This thesis is part of the HOST project funded by the ANR-TECSAN-2011 program to develop a Management Support System of Strain (MSSS) ensuring three objectives:1. Multi-criteria evaluation through a variety of indicators aggregated by fuzzy logic to solve the subjectivity of the human feeling of strain. Each evaluation scenario involves specific decision rules targeting to supervise failure points.2. Demand forecasting through several time horizons: applying SARIMA and SARIMAX methods is justified by the time series seasonality of visits and the influence of some external parameters (epidemics, holidays, weather). In addition, the quality of the historical information has been improved by a history rebuilding based on the daily likelihood.3. Improving flow management and activity monitoring since the use of MSSS as a dashboard provides a macro view of the whole activity (beds occupied, waiting, estimated length of stay, excessive elongation).The simulations address real strain scenarios observed between 2011 and 2013 in the Pediatric ED Jeanne de Flandre of the Regional University Hospital of Lille (France).