PHEN : parkinson helper emergency notification system using Bayesian Belief Network

Context-aware systems are used to aid users in their daily lives. In the recent years, researchers are exploring how context aware systems can benefit humanity through assist patients, specifically those who suffer incurable diseases, to cope with their illness. In this paper, we direct our work to help people who suffer from Parkinson disease. We propose PHEN, Parkinson Helper Engine Network System, a context-aware system that aims to support Parkinson disease patients on m any levels. We use ontology is for context representation and modeling. Then the ontology based context model is used to learn with Bayesian Belief network (BBN) which is beneficial in handling the uncertainty aspect of context-aware systems

A Human-Centric Approach to Group-Based Context-Awareness

The emerging need for qualitative approaches in context-aware information processing calls for proper modeling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage. Many of the current approaches to context-awareness either lack a solid theoretical basis for modeling or ignore important requirements such as modularity, high-order uncertainty management and group-based context-awareness. Therefore, their real-world application and extendability remains limited. In this paper, we present f-Context as a service-based context-awareness framework, based on language-action perspective (LAP) theory for modeling. Then we identify some of the complex, informational parts of context which contain high-order uncertainties due to differences between members of the group in defining them. An agent-based perceptual computer architecture is proposed for implementing f-Context that uses computing with words (CWW) for handling uncertainty. The feasibility of f-Context is analyzed using a realistic scenario involving a group of mobile users. We believe that the proposed approach can open the door to future research on context-awareness by offering a theoretical foundation based on human communication, and a service-based layered architecture which exploits CWW for context-aware, group-based and platform-independent access to information systems

A Dynamic Contextual Change Management Application for Real Time Decision-Making Support

Decision making is a fundamental process within organizations for many reasons. It is indeed involved at all levels (new product decisions, management and marketing decisions, etc.) and has a direct impact on companies’ efficiency and effectiveness. Many researches are conducted to enhance the decision-making process by proposing decision support systems where the most frequent challenge is the change management. Indeed, all businesses operate within an environment that is subject to constant changes (like new customers’ needs and requirements, organisational and technological changes, changes in key information used to derive decisions, etc.). These changes have a major impact on the quality and accuracy of the proposed decision if they are not detected and propagated, at the right time, during the decision-making process. The present work attempts to resolve this challenge by proposing a dynamic change management technique that allows three tasks to be automatically performed. First, continuously detect changes and note them. Second, retrieve from the detected changes those that are related to the decision rules. Finally, propagate them by computing the new value of the decision rule. The proposal has been fully implemented and tested in the supervision process of gas network exploitation.projet FUI Gontran

Increased Robustness in Context Detection and Reasoning using Uncertainty Measures - Concept and Application

In this paper we report on a novel recurrent fuzzy classification method for robust detection of context activities in an environment using either single or distributed sensors. The proposed method utilizes uncertainty measures for improvement of detection, fusion and aggregation of context knowledge. To calculate the uncertainty measure we propose the use of simple and recurrent fuzzy systems. We applied the method in a real application to recognize various applause (and non applause) situations, e.g. during a conference. Measurements were taken from mobile phone sensors (microphone, acceleration if available) and acceleration sensory attached to a board marker. We show that we are able to improve robustness of detection using our novel uncertainty measures by ~30% on average. We also show that the use of multiple phones and distributed recognition in most cases allows to achieve a recognition rate between 90% and 100%

Kontextmodellierung für das ambient assisted living

Die Zielstellung der Dissertation ist die Erarbeitung einer Methodik für die Kontextmodellierung im Rahmen des AAL. Zunächst wurden die besonderen Anforderungen aus der Anwendungsdomäne AAL an die Kontextmodellierung erarbeitet, besonders die Trennung zwischen Kontextinfrastruktur und den Diensten, die Bereitstellung dienstetyp-spezifischer Kontextmodelle und ihre Erweiterbarkeit, die Bereitstellung einer nutzerspezifischen Kontextmodellierung sowie die Berücksichtigung der Phasen der Entwicklung und Nutzung von AAL-Diensten. Es wurde eine Reihe aktueller Ansätze zur Kontextmodellierung anhand der Anforderungen bewertet und der Bedarf für einen weitergehenden Ansatz erkannt. Der in dieser Arbeit entwickelte Ansatz basiert auf einer zweidimensionalen Kontextmodellierung von "Einsatzzweck" sowie "Phasen der Entwicklung und Nutzung". In der ersten Dimension werden drei Ebenen der Abstraktion festgelegt: "Infrastruktur", "Dienste" und "Nutzerinteraktion" und in der zweiten vier Modelltypen identifiziert und den verschiedenen Phasen "Planung", "Definition", "Design", "Implementation", "Test", "Bereitstellung", "Übernahme" sowie "Betrieb" zugeordnet. Jeder dieser Modelltypen besitzt einen eigenen Fokus und eine eigene Darstellung. Beispielsweise definiert das konzeptuelle Kontextmodell eine graphische Repräsentation, der ein bereits bekannter Ansatz zur konzeptuellen Kontextmodellierung zugrunde liegt, welcher hier aber um fehlende Konzepte ergänzt wird. In die durch zwei Dimensionen aufgespannte Matrix werden die jeweils durch sie eingegrenzten Anforderungen eingeordnet sowie entsprechende Konzepte und Ausprägungen des Kontextmodells definiert. Die gemeinsamen Elemente des Kontextmodells sowie die Überführungen zwischen den einzelnen Elementen der Matrix werden durch das sie tragende Metamodell definiert. Die zweidimensionale Kontextmodellierung ist auch Bestandteil der Methodik, bei der sich fünf Schritte unterscheiden lassen: die Definition der Kontextinfrastruktur, die Instanziierung der Kontextinfrastruktur, die Definition der AAL-Dienstetypen, die Definition eines AAL-Dienstes, sowie die Instanziierung der AAL-Dienstemenge. Jede der Schritte beschreibt einen Teilschritt in der Realisierung einer intelligenten häuslichen Umgebung mit integrierten AAL-Diensten und definiert den notwendigen Ausschnitt aus dem zweidimensionalen Kontextmodell sowie die benötigten Werkzeuge, von denen die wesentlichen im Rahmen der Dissertation umgesetzt worden sind und so der Methodik zur Verfügung stehen. Die Definition der Schritte ermöglicht auch eine flexible Nutzung der Methodik für unterschiedlich komplexe Szenarien von kontextadaptiven AAL-Diensten

Investigating Tafheet as a Unique Driving Style Behaviour

Road safety has become a major concern due to the increased rate of deaths caused by road accidents. For this purpose, intelligent transportation systems are being developed to reduce the number of fatalities on the road. A plethora of work has been undertaken on the detection of different styles of behaviour such as fatigue and drunken behaviour of the drivers; however, owing to complexity of human behaviour, a lot has yet to be explored in this field to assess different styles of the abnormal behaviour to make roads safer for travelling. This research focuses on detection of a very complex driver’s behaviours: ‘tafheet’, reckless and aggressive by proposing and building a driver’s behaviour detection model in the context-aware system in the VANET environment. Tafheet behaviour is very complex behaviour shown by young drivers in the Middle East, Japan and the USA. It is characterised by driving at dangerously high speeds (beyond those commonly known in aggressive behaviour) coupled with the drifting and angular movements of the wheels of the vehicle, which is similarly aggressive and reckless driving behaviour. Thus, the dynamic Bayesian Network (DBN) framework was applied to perform reasoning relating to the uncertainty associated with driver’s behaviour and to deduce the possible combinations of the driver’s behaviour based on the information gathered by the system about the foregoing factors. Based on the concept of context-awareness, a novel Tafheet driver’s behaviour detection architecture had been built in this thesis, which had been separated into three phases: sensing phase, processing and thinking phase and the acting phase. The proposed system elaborated the interactions of various components of the architecture with each other in order to detect the required outcomes from it. The implementation of this proposed system was executed using GeNIe 2.0 software, resulting in the construction of DBN model. The DBN model was evaluated by using experimental set of data in order to substantiate its functionality and accuracy in terms of detection of tafheet, reckless and aggressive behaviours in the real time manner. It was shown that the proposed system was able to detect the selected abnormal behaviours of the driver based on the contextual data collected. The novelty of this system was that it could detect the reckless, aggressive and tafheet behaviour in sequential manner, based on the intensity of the driver’s behaviour itself. In contrast to previous detection model, this research work suggested the On Board Unit architecture for the arrangement of sensors and data processing and decision making of the proposed system, which can be used to pre-infer the complex behaviour like tafheet. Thus it has the potential to prevent the road accidents from happening due to tafheet behaviour

Context Aware Pre-Crash System for Vehicular ad hoc Networks Using Dynamic Bayesian Model

Tragically, traffic accidents involving drivers, motorcyclists and pedestrians result in thousands of fatalities worldwide each year. For this reason, making improvements to road safety and saving people’s lives is an international priority. In recent years, this aim has been supported by Intelligent Transport Systems, offering safety systems and providing an intelligent driving environment. The development of wireless communications and mobile ad hoc networks has led to improvements in intelligent transportation systems heightening these systems’ safety. Vehicular ad hoc Networks comprise an important technology; included within intelligent transportation systems, they use dedicated short-range communications to assist vehicles to communicate with one another, or with those roadside units in range. This form of communication can reduce road accidents and provide a safer driving environment. A major challenge has been to design an ideal system to filter relevant contextual information from the surrounding environment, taking into consideration the contributory factors necessary to predict the likelihood of a crash with different levels of severity. Designing an accurate and effective pre-crash system to avoid front and back crashes or mitigate their severity is the most important goal of intelligent transportation systems, as it can save people’s lives. Furthermore, in order to improve crash prediction, context-aware systems can be used to collect and analyse contextual information regarding contributory factors. The crash likelihood in this study is considered to operate within an uncertain context, and is defined according to the dynamic interaction between the driver, the vehicle and the environment, meaning it is affected by contributory factors and develops over time. As a crash likelihood is considered to be an uncertain context and develops over time, any usable technology must overcome this uncertainty in order to accurately predict crashes. This thesis presents a context-aware pre-crash collision prediction system, which captures information from the surrounding environment, the driver and other vehicles on the road. It utilises a Dynamic Bayesian Network as a reasoning model to predict crash likelihood and severity level, whether any crash will be fatal, serious, or slight. This is achieved by combining the above mentioned information and performing probabilistic reasoning over time. The thesis introduces novel context aware on-board unit architecture for crash prediction. The architecture is divided into three phases: the physical, the thinking and the application phase; these which represent the three main subsystems of a context-aware system: sensing, reasoning and acting. In the thinking phase, a novel Dynamic Bayesian Network framework is introduced to predict crash likelihood. The framework is able to perform probabilistic reasoning to predict uncertainty, in order to accurately predict a crash. It divides crash severity levels according to the UK department for transport, into fatal, serious and slight. GeNIe version 2.0 software was used to implement and verify the Dynamic Bayesian Network model. This model has been verified using both syntactical and real data provided by the UK department for transport in order to demonstrate the prediction accuracy of the proposed model and to demonstrate the importance of including a large amount of contextual information in the prediction process. The evaluation of the proposed system delivered high-fidelity results, when predicting crashes and their severity. This was judged by inputting different sensor readings and performing several experiments. The findings of this study has helped to predict the probability of a crash at different severity levels, accounting for factors that may be involved in causing a crash, thereby representing a valuable step towards creating a safer traffic network

Resolving semantic conflicts through ontological layering

We examine the problem of semantic interoperability in modern software systems, which exhibit pervasiveness, a range of heterogeneities and in particular, semantic heterogeneity of data models which are built upon ubiquitous data repositories. We investigate whether we can build ontologies upon heterogeneous data repositories in order to resolve semantic conflicts in them, and achieve their semantic interoperability. We propose a layered software architecture, which accommodates in its core, ontological layering, resulting in a Generic ontology for Context aware, Interoperable and Data sharing (Go-CID) software applications. The software architecture supports retrievals from various data repositories and resolves semantic conflicts which arise from heterogeneities inherent in them. It allows extendibility of heterogeneous data repositories through ontological layering, whilst preserving the autonomy of their individual elements. Our specific ontological layering for interoperable data repositories is based on clearly defined reasoning mechanisms in order to perform ontology mappings. The reasoning mechanisms depend on the user‟s involvments in retrievals of and types of semantic conflicts, which we have to resolve after identifying semantically related data. Ontologies are described in terms of ontological concepts and their semantic roles that make the types of semantic conflicts explicit. We contextualise semantically related data through our own categorisation of semantic conflicts and their degrees of similarities. Our software architecture has been tested through a case study of retrievals of semantically related data across repositories in pervasive healthcare and deployed with Semantic Web technology. The extensions to the research results include the applicability of our ontological layering and reasoning mechanisms in various problem domains and in environments where we need to (i) establish if and when we have overlapping “semantics”, and (ii) infer/assert a correct set of “semantics” which can support any decision making in such domains

Kontextbereitstellung in Automobilen Ad-hoc Netzen

Je detaillierter ein Fahrer über den Streckenabschnitt informiert ist, den er in naher Zukunft befahren wird, desto größer ist die Wahrscheinlichkeit, dass er rechtzeitig und angemessen auf komplexe Verkehrssituationen reagiert. Die umfassende Verfügbarkeit von qualitativ hochwertigen Kontextinformationen im Fahrzeug leistet vor diesem Hintergrund einen wichtigen Beitrag zur Erhöhung der Verkehrssicherheit und -effizienz. Ziel dieser Arbeit ist eine zuverlässige Vorhersage der zukünftigen Fahrsituation auf Basis des gemeinschaftlich bekannten Wissens der Verkehrsteilnehmer. Dabei steht die Verwaltung ortsbezogener Kontextinformationen, die Fusion von verschiedenartigen Informationsquellen, sowie die Problematik der Verteilung der von den Fahrzeugen erzeugten Kontextinformationen über automobile Ad-hoc Netzen im Fokus der Arbeit. Aufbauend auf einer formalen Lösungsspezifikation beschreibt die Arbeit einen zweistufigen Bewertungsprozess, der es erlaubt, auf Basis verteilter Sensorbeobachtungen unterschiedlicher Fahrzeuge ein Wahrscheinlichkeitsmaß für das Eintreten eines konkreten Zustands eines relevanten Fahrkontexts abzuleiten. Die räumlichen und zeitlichen Eigenschaften des Kontextaspekts werden dabei gewichtet interpoliert. Anschließend werden auf Basis eines Bayesschen Netzes die kausalen Zusammenhänge unterschiedlicher Kontextaspekte quervalidiert. Zudem wird aufgezeigt, wie Kontextinformationen zwischen Fahrzeugen in einem automobilen Ad-hoc Netzwerk ausgetauscht werden können. Das aus drahtgebundenen Netzen bekannte Konzept der Nutzenmaximierung des Netzwerks wird hierzu auf die speziellen Charakteristika automobiler Netze erweitert. Es wird zudem eine schichtenübergreifende Lösungsarchitektur vorgestellt, die situationsadaptiv sowohl kurze Latenzzeiten für kritische Nachrichten, als auch eine nachhaltige Skalierbarkeit des Netzes in Szenarien mit geringen und hohen Fahrzeugdichten sicherstellt. Der Kanalzugriff und die Verbreitung der Kontextinformationen im Netzwerk basieren dabei auf einer situationsabhängigen Bewertung des Anwendungsnutzens der zu übertragenden Nachrichten. Mit Hilfe von Simulationen wird das Verhalten des Systems bewertet. Durch eine ontologiebasierte Verwaltung wird auch nichtfahrzeugbezogenen Systemen eine domänenübergreifende Nutzung der Sensorinformationen und kausalen Zusammenhänge ermöglicht