Smart Sensing and Context: 5th European Conference, EuroSSC 2010, Passau, Germany, November 14-16, 2010. Proceedings

This volume constitutes the revised papers of the 5th European Conference on Smart Sensing and Context, EuroSSC 2010, held in Passau, Germany, in November 2010. The 13 revised full papers were carefully reviewed and selected from numerous submissions. The papers address topics such as applications; sensing; systems support; and higher level modeling. In addition to the Conference is a short report about the Workshop on Ambient Assisted Living (AAL) Platform included

Sensor Networks and Their Applications: Investigating the Role of Sensor Web Enablement

The Engineering Doctorate (EngD) was conducted in conjunction with BT Research on state-of-the-art Wireless Sensor Network (WSN) projects. The first area of work is a literature review of WSN project applications, some of which the author worked on as a BT Researcher based at the world renowned Adastral Park Research Labs in Suffolk (2004-09). WSN applications are examined within the context of Machine-to-Machine (M2M); Information Networking (IN); Internet/Web of Things (IoT/WoT); smart home and smart devices; BT’s 21st Century Network (21CN); Cloud Computing; and future trends. In addition, this thesis provides an insight into the capabilities of similar external WSN project applications. Under BT’s Sensor Virtualization project, the second area of work focuses on building a Generic Architecture for WSNs with reusable infrastructure and ‘infostructure’ by identifying and trialling suitable components, in order to realise actual business benefits for BT. The third area of work focuses on the Open Geospatial Consortium (OGC) standards and their Sensor Web Enablement (SWE) initiative. The SWE framework was investigated to ascertain its potential as a component of the Generic Architecture. BT’s SAPHE project served as a use case. BT Research’s experiences of taking this traditional (vertical) stove-piped application and creating SWE compliant services are described. The author’s findings were originally presented in a series of publications and have been incorporated into this thesis along with supplementary WSN material from BT Research projects. SWE 2.0 specifications are outlined to highlight key improvements, since work began at BT with SWE 1.0. The fourth area of work focuses on Complex Event Processing (CEP) which was evaluated to ascertain its potential for aggregating and correlating the shared project sensor data (‘infostructure’) harvested and for enabling data fusion for WSNs in diverse domains. Finally, the conclusions and suggestions for further work are provided

Rhythm Modelling of Long-Term Activity Data

Long-term monitoring for activity recognition opens up new possibilities for deriving characteristics from the data, such as daily activity rhythms and certain quality measures for the activity performed or for identifying similarities or differences in daily routines. This thesis investigates the detection of activities with wearable sensors and addresses two major challenges in particular: The modelling of a person’s behaviour into rhythmic patterns and the detection of high-level activities, e.g., having lunch or sleeping. To meet these challenges, this thesis makes the following contributions: First, we study different platforms that are suitable for long-term data recording: A wrist-worn sensor and mobile phones. The latter has shown different carrying behaviours for various users. This has to be considered in ubiquitous systems for accurately recognizing the user’s context. We evaluate our findings in a study with a wrist-worn accelerometer by correlating with the inertial data of a smart phone. Second, we investigate datasets that exhibit rhythmic patterns to be used for recognizing high-level activities. Such statistical information obtained over a population is collected with time use surveys which describe how often certain activities are performed by the user. From such datasets we extract features like time and location to describe which activities are detectable by making use of prior information, showing also the benefits and limits of such data. Third, in order to improve on the recognition rates of high-level activities from wearable sensor data only, we propose the use of the aforementioned prior information from time use data. In our approach we investigate the results of a common classifier for several high-level activities, after which we compare them to the outcome of a maximum-likelihood estimation on the time use survey data. In a last step, we show how these two classification approaches are fused to raise the recognition rates. In a fourth contribution we introduce a recording platform to capture sleep and sleep behaviour in the user’s common environment, enabling the unobtrusive monitoring of patterns over several weeks. We use a wrist-worn sensor to record inertial data from which we extract sleep segments. For this purpose, we present three different sleep detection approaches: A Gaussian-, generative model- and stationary segments-based algorithm are evaluated and are found to exhibit different accuracies for detecting sleep. The latter algorithm is pitted against two clinically evaluated sleep detection approaches, indicating that we are able to reach an optimum trade-off between sleep and wake segments, while the two common algorithms tend to overestimate sleep. Further, we investigate the rhythmic patterns within sleep: We classify sleep postures and detect muscle contractions with a high confidence, enabling physicians to efficiently browse through the data

4th International Symposium on Ambient Intelligence (ISAmI 2013)

Ambient Intelligence (AmI) is a recent paradigm emerging from Artificial Intelligence (AI), where computers are used as proactive tools assisting people with their day-to-day activities, making everyone’s life more comfortable. Another main concern of AmI originates from the human computer interaction domain and focuses on offering ways to interact with systems in a more natural way by means user friendly interfaces. This field is evolving quickly as can be witnessed by the emerging natural language and gesture based types of interaction. The inclusion of computational power and communication technologies in everyday objects is growing and their embedding into our environments should be as invisible as possible. In order for AmI to be successful, human interaction with computing power and embedded systems in the surroundings should be smooth and happen without people actually noticing it. The only awareness people should have arises from AmI: more safety, comfort and wellbeing, emerging in a natural and inherent way. ISAmI is the International Symposium on Ambient Intelligence and aiming to bring together researchers from various disciplines that constitute the scientific field of Ambient Intelligence to present and discuss the latest results, new ideas, projects and lessons learned, namely in terms of software and applications, and aims to bring together researchers from various disciplines that are interested in all aspects of this area

Forschungsbericht Universität Mannheim 2006 / 2007

Sie erhalten darin zum einen zusammenfassende Darstellungen zu den Forschungsschwerpunkten und Forschungsprofilen der Universität und deren Entwicklung in der Forschung. Zum anderen gibt der Forschungsbericht einen Überblick über die Publikationen und Forschungsprojekte der Lehrstühle, Professuren und zentralen Forschungseinrichtungen. Diese werden ergänzt um Angaben zur Organisation von Forschungsveranstaltungen, der Mitwirkung in Forschungsausschüssen, einer Übersicht zu den für Forschungszwecke eingeworbenen Drittmitteln, zu den Promotionen und Habilitationen, zu Preisen und Ehrungen und zu Förderern der Universität Mannheim. Darin zeigt sich die Bandbreite und Vielseitigkeit der Forschungsaktivitäten und deren Erfolg auf nationaler und internationaler Ebene

Técnicas de computación evolutiva aplicadas a la clasificación a partir de monitores de actividad física

Actualmente, varios factores están haciendo que el campo de reconocimiento de actividades humanas cobre una mayor importancia, como por ejemplo, la proliferación de dispositivos “wearables” que permiten monitorizar la actividad física o la tendencia de la población mundial hacia un estilo de vida cada vez más sedentario. Este estilo de vida sedentario propio de la sociedad actual se traduce en insuficiente actividad física y se considera uno de los mayores factores de riesgo para la salud, estando entre los primeros puestos de factores de riesgo de mortalidad a nivel mundial, según la OMS [11]. De esta manera, dentro del ámbito de la salud y el bienestar, gracias al avance en la miniaturización de sensores, que incluso permite su uso incorporados a la ropa de las personas, el reconocimiento automático de actividades se presenta como una solución a problemas de diversa índole, como por ejemplo, prevención de enfermedades, envejecimiento activo, monitorización remota de enfermos, además de un amplio espectro de aplicaciones en el ámbito deportivo. Es por esto que se convierten en dispositivos de monitorización sumamente útiles en otras áreas de investigación, introduciendo el reconocimiento de actividades humanas en la computación ubicua, el entretenimiento, el registro de actividades diarias personales o el seguimiento del rendimiento deportivo o profesional. Con la principal motivación de explorar nuevos frentes de investigación del reconocimiento de actividades, con un enfoque distinto a los planteados hasta ahora, en este trabajo se propone un sistema de reconocimiento automático de actividades que integra un algoritmo evolutivo, para la tarea de clasificación de actividades, y un enjambre de partículas, para la realización de un clustering que mejore el aprendizaje automático. El sistema ha sido evaluado mediante validación cruzada del tipo leave-one-subject-out, para comprobar su rendimiento en situaciones de reconocimiento independiente del sujeto, obteniendo un 52,37% de acierto. También, se ha evaluado el sistema con validación cruzada estándar de 10-folds en cada sujeto, para analizar la capacidad del sistema en casos de clasificación dependiente del sujeto, alcanzando un 98,07% de acierto. Un resultado significativamente más positivo que el primero, que muestra que el sistema puede tender a la personalización del reconocimiento de actividades. Además, se ha llevado a cabo la evaluación del sistema con validación cruzada estándar de 10-folds en el conjunto de todos los sujetos, con un 70,2267% de acierto, abundándose en la conclusión expuesta más arriba, de que el sistema presenta un mejor funcionamiento en situaciones de personalización del reconocimiento de actividades.In the current time, various factors are making the field of activity recognition become more important, such as the proliferation of wearable devices that allow to monitor physical activity or global population’s tendency towards a more sendentary lifestyle. This sedentary lifestyle is turning into insufficient physical activity and is considered one of the factors with a highest risk for health, being among the leading risk factors of mortality, regarding the WHO [11]. This way, within health and wellness field, thanks to the advance in sensor miniaturization, which even allows sensor usage incorporated to people clothes, activity automatic recognition is presented as a solution to very diverse problems, such as diseases prevention, active aging, patient remote monitoring, as well as a wide range of applications in sports. For that reason, wearable sensors happen to be extremely useful monitorizing devices in other research areas, introducing human activity recognition to ubiquitous computing, entertainment industry, daily life activities logging and sportive and professional perfomance monitoring, among others. With the main motivation of exploring new research horizons, through a different approach to the previous works, in this project, an activity automatic recognition system that integrates an evolutionary algorithm, for the activity classification task, and a particle swarm, for a clustering that improves the automatic learning, is proposed. The system has been evaluated with leave-one-subject-out (LOSO) cross validation, in order to assess its performance in situations where the recognition is subject independent, obtaining an accuracy rate of 52,37%. Also, the system has been evaluated with 10-fold standard cross validation within each subject, to analyze the system’s capacity in subject dependent classification cases, reaching an accuracy rate of 98,07%. A significantly more positive result than the first one, that shows the system might tend to personalization of activity recognition. In addition, the system evaluation has been carried with 10-fold standard cross validation within the whole set of all the subjects, getting an accuracy rate of 70,2267%, which supports the conclusion presented above that the system works better in situations of personalization of the activity recognition.Grado en Ingeniería Informátic

An infrastructure for context-dependent RDF data replication on mobile devices

Der im Rahmen dieser Arbeit vorgestellte Ansatz beschreibt die Erstellung einer technischen Infrastruktur, die selektiv RDF-Daten in Abhängigkeit der Informationsbedürfnisse und den unterschiedlichen Kontexten mobiler Nutzer auf ein mobiles Endgerät repliziert und diese somit in intelligenter Art und Weise unterstützt. Eine Zusammenführung kontextspezifischer Konzepte und semantischer Technologien stellt einen wesentlichen Bestandteil zur Verbesserung der mobilen Informationssuche dar und erhöht gleichzeitig die Präzision mobiler Informationsgewinnungsprozesse. Trotz des vorhandenen Potentials einer proaktiven, kontextabhängigen Replizierung von RDF-Daten, gestaltet sich die Verarbeitung auf mobilen Endgeräten schwierig. Die Gründe dafür liegen in den technischen und netzwerkspezifischen Beschränkungen, in der fehlenden Verarbeitungs- und Verwaltungsfunktionalität von ontologiebasierten Beschreibungsverfahren sowie in der Unzulänglichkeit bestehender Replikationsansätze, sich an verändernde Informationsbedürfnisse sowie an unterschiedliche technische, umgebungsspezifische und infrastrukturbezogene Eigenheiten anzupassen. Verstärkt wird diese Problematik durch das Fehlen ausdrucksstarker Beschreibungsverfahren zur Repräsentation kontextspezifischer Daten. Existierende Ansätze leiden dementsprechend unter der Verwendung proprietärer Datenformate, dem Einsatz serverabhängiger Applikationsinfrastrukturen sowie dem Unvermögen, kontextspezifische Daten auszutauschen. Dies äußert sich in Studien, welche die Berücksichtigung der Informationsbedürfnisse mobiler Nutzer als unzureichend einstuft und einen Großteil der benötigten Informationen als kontextrelevant auszeichnet. Obgleich Fortschritte bei der Adaption von semantischen Technologien und Beschreibungsverfahren zur kontextabhängigen Verarbeitung zu erkennen sind, bleibt eine auf semantische Technologien basierende, proaktive Replizierung von RDF-Daten auf mobile Endgeräte ein offenes Forschungsfeld. Die vorliegende Arbeit diskutiert Möglichkeiten zur Erweiterung der mobilen, kontextspezifischen Datenverarbeitung durch semantische Technologien und beinhaltet eine vergleichende Studie zur Leistungsfähigkeit aktueller mobiler RDF-Frameworks. Kernpunkt ist die formale Beschreibung eines abstrakten Modells zur effizienten Akquise, Repräsentation, Verwaltung und Verarbeitung von Kontextinformationen unter Berücksichtigung der technischen Gegebenheiten mobiler Informationssysteme. Ergänzt wird es durch die formale Spezifikation eines nebenläufigen, transaktionsbasierten Verarbeitungsmodells, welches Vollständigkeits- und Konsistenzbedingungen auf Daten- und Prozessebene berücksichtigt. Der praktische Nutzen des vorliegenden Ansatzes wird anhand typischer Informationsbedürfnisse eines Wissensarbeiters demonstriert. Der Ansatz reduziert Abhängigkeiten zu externen Systemen und ermöglicht Nutzern, unabhängig von zeitlichen, örtlichen und netzwerkspezifischen Gegebenheiten, auf die für sie relevanten Daten zuzugreifen und diese zu verarbeiten. Durch die lokale Verarbeitung kontextbezogener Daten wird sowohl die Privatssphäre des Nutzers gewahrt als auch sicherheitsrelevanten Aspekten Rechnung getragen.This work describes an infrastructure for the selective RDF data replication to mobile devices while considering current and future information needs of mobile users and the different contexts they are operating in. It presents a novel approach in synthesizing context-aware computing concepts with semantic technologies and distributed transaction management concepts for intelligently assisting mobile users while enhancing mobile information seeking behavior and increasing the precision of mobile information retrieval processes. Despite the huge potential of a proactive, context-dependent replication of RDF data, such data can not be efficiently processed on mobile devices due to (i) technical limitations and network-related constraints, (ii) missing processing and management capabilities of ontology-based description frameworks, (iii) the inability of traditional data replication strategies to adapt to changing user information needs and to consider technical, environmental, and infrastructural restrictions of mobile operating systems, and (iv) the dynamic and emergent nature of context, which requires flexible and extensible description frameworks that allow for elaborating on the semantics of contextual constellations as well as on the relationships that exist between them. As a consequence, existing approaches suffer from the deployment of proprietary data formats, server-dependent application infrastructures, and the inability to share and exchange contextual information across system borders. Moreover, results of recently conducted studies reveal that mobile users find their information needs inadequately addressed, where a large share can be attributed as context or context-relevant. Although progress has been made in applying semantic technologies, concepts, and languages to the domain of context-aware computing, a synthesis of those fields for the proactive provision of RDF data replicas on mobile devices remains an open research issue. This work discusses possible fields where context-aware computing can be enhanced using technologies, languages, and concepts from the Semantic Web and contains a comparative study about the performance of current mobile RDF frameworks in replication-specific tasks. The main contribution of this thesis is a formal description of an abstract model that allows for an efficient acquisition, representation, management, and processing of contextual information while taking into account the peculiarities and operating environments of mobile information systems. It is complemented by a formal specification of a concurrently operating transaction-based processing model that considers completeness and consistency requirements on data and process level. We demonstrate the practicability of the presented approach trough a prototypical implementation of context and data providers that satisfy typical information needs of a mobile knowledge worker. As a consequence, dependencies to external systems are reduced and users are equipped with relevant information that adheres to their information needs anywhere and at any time, independent of any network-related constraints. Since context-relevant data are processed directly on a mobile device, security and privacy issues are preserved