A holistic multi-purpose life logging framework

Die Paradigm des Life-Loggings verspricht durch den Vorschlag eines elektronisches Gedächtnisses dem menschlichem Gedächtnis eine komplementäre Assistenz. Life-Logs sind Werkzeuge oder Systeme, die automatisch Ereignisse des Lebens des Benutzers aufnehmen. Im technischem Sinne sind es Systeme, die den Alltag durchdringen und kontinuierlich konzeptuelle Informationen aus der Umgebung des Benutzers erfassen. Teile eines so gesammelten Datensatzes könnten aufbewahrt und für die nächsten Generationen zugänglich gemacht werden. Einige Teile sind es wert zusätzlich auch noch mit der Gesellschaft geteilt zu werden, z.B. in sozialen Netzwerken. Vom Teilen solcher Informationen profitiert sowohl der Benutzer als auch die Gesellschaft, beispielsweise durch die Verbesserung der sozialen Interaktion des Users, das ermöglichen neuer Gruppenverhaltensstudien usw. Anderseits, im Sinne der individuellen Privatsphäre, sind Life-log Informationen sehr sensibel und entsprechender Datenschutz sollte schon beim Design solcher Systeme in Betracht gezogen werden. Momentan sind Life-Logs hauptsächlich für den spezifischen Gebrauch als Gedächtnisstützen vorgesehen. Sie sind konfiguriert um nur mit einem vordefinierten Sensorset zu arbeiten. Das bedeutet sie sind nicht flexibel genug um neue Sensoren zu akzeptieren. Sensoren sind Kernkomponenten von Life-Logs und mit steigender Sensoranzahl wächst auch die Menge der Daten die für die Erfassung verfügbar sind. Zusätzlich bietet die Anordnung von mehreren Sensordaten bessere qualitative und quantitative Informationen über den Status und die Umgebung (Kontext) des Benutzers. Offenheit für Sensoren wirkt sich also sowohl für den User als auch für die Gemeinschaft positiv aus, indem es Potential für multidisziplinnäre Studien bietet. Zum Beispiel können Benutzer Sensoren konfigurieren um ihren Gesundheitszustand in einem gewissen Zeitraum zu überwachen und das System danach ändern um es wieder als Gedächtnisstütze zu verwenden. In dieser Dissertation stelle ich ein Life-Log Framework vor, das offen für die Erweiterung und Konfiguration von Sensoren ist. Die Offenheit und Erweiterbarkeit des Frameworks wird durch eine Sensorklassiffzierung und ein flexibles Model für die Speicherung der Life-Log Informationen unterstützt. Das Framework ermöglicht es den Benützern ihre Life-logs mit anderen zu teilen und unterstützt die notwendigen Merkmale vom Life Logging. Diese beinhalten Informationssuche (durch Annotation), langfristige digitale Erhaltung, digitales Vergessen, Sicherheit und Datenschutz.The paradigm of life-logging promises a complimentary assistance to the human memory by proposing an electronic memory. Life-logs are tools or systems, which automatically record users' life events in digital format. In a technical sense, they are pervasive tools or systems which continuously sense and capture contextual information from the user's environment. A dataset will be created from the collected information and some records of this dataset are worth preserving in the long-term and enable others, in future generations, to access them. Additionally, some parts are worth sharing with society e.g. through social networks. Sharing this information with society benefits both users and society in many ways, such as augmenting users' social interaction, group behavior studies, etc. However, in terms of individual privacy, life-log information is very sensitive and during the design of such a system privacy and security should be taken into account. Currently life-logs are designed for specific purposes such as memory augmentation, but they are not flexible enough to accept new sensors. This means that they have been configured to work only with a predefined set of sensors. Sensors are the core component of life-logs and increasing the number of sensors causes more data to be available for acquisition. Moreover a composition of multiple sensor data provides better qualitative and quantitative information about users' status and their environment (context). On the other hand, sensor openness benefits both users and communities by providing appropriate capabilities for multidisciplinary studies. For instance, users can configure sensors to monitor their health status for a specific period, after which they can change the system to use it for memory augmentation. In this dissertation I propose a life-log framework which is open to extension and configuration of its sensors. Openness and extendibility, which makes the framework holistic and multi-purpose, is supported by a sensor classification and a flexible model for storing life-log information. The framework enables users to share their life-log information and supports required features for life logging. These features include digital forgetting, facilitating information retrieval (through annotation), long-term digital preservation, security and privacy

Lesson Learned from Collecting Quantified Self Information via Mobile and Wearable Devices

The ubiquity and affordability of mobile and wearable devices has enabled us to continually and digitally record our daily life activities. Consequently, we are seeing the growth of data collection experiments in several scientific disciplines. Although these have yielded promising results, mobile and wearable data collection experiments are often restricted to a specific configuration that has been designed for a unique study goal. These approaches do not address all the real-world challenges of “continuous data collection” systems. As a result, there have been few discussions or reports about such issues that are faced when “implementing these platforms” in a practical situation. To address this, we have summarized our technical and user-centric findings from three lifelogging and Quantified Self data collection studies, which we have conducted in real-world settings, for both smartphones and smartwatches. In addition to (i) privacy and (ii) battery related issues; based on our findings we recommend further works to consider (iii) implementing multivariate reflection of the data; (iv) resolving the uncertainty and data loss; and (v) consider to minimize the manual intervention required by users. These findings have provided insights that can be used as a guideline for further Quantified Self or lifelogging studies

Hierarchical video surveillance architecture: a chassis for video big data analytics and exploration

There is increasing reliance on video surveillance systems for systematic derivation, analysis and interpretation of the data needed for predicting, planning, evaluating and implementing public safety. This is evident from the massive number of surveillance cameras deployed across public locations. For example, in July 2013, the British Security Industry Association (BSIA) reported that over 4 million CCTV cameras had been installed in Britain alone. The BSIA also reveal that only 1.5% of these are state owned. In this paper, we propose a framework that allows access to data from privately owned cameras, with the aim of increasing the efficiency and accuracy of public safety planning, security activities, and decision support systems that are based on video integrated surveillance systems. The accuracy of results obtained from government-owned public safety infrastructure would improve greatly if privately owned surveillance systems ‘expose’ relevant video-generated metadata events, such as triggered alerts and also permit query of a metadata repository. Subsequently, a police officer, for example, with an appropriate level of system permission can query unified video systems across a large geographical area such as a city or a country to predict the location of an interesting entity, such as a pedestrian or a vehicle. This becomes possible with our proposed novel hierarchical architecture, the Fused Video Surveillance Architecture (FVSA). At the high level, FVSA comprises of a hardware framework that is supported by a multi-layer abstraction software interface. It presents video surveillance systems as an adapted computational grid of intelligent services, which is integration-enabled to communicate with other compatible systems in the Internet of Things (IoT)

Video big data: an agile architecture for systematic exploration and analytics

Video is currently at the forefront of most business and natural environments. In surveillance, it is the most important technology as surveillance systems reveal information and patterns for solving many security problems including crime prevention. This research investigates technologies that currently drive video surveillance systems with a view to optimization and automated decision support. The investigation reveals some features and properties that can be optimised to improve performance and derive further benefits from surveillance systems. These aspects include system-wide architecture, meta-data generation, meta-data persistence, object identification, object tagging, object tracking, search and querying sub-systems. The current less-than-optimum performance is attributable to many factors, which include massive volume, variety, and velocity (the speed at which streaming video transmit to storage) of video data in surveillance systems. Research contributions are 2-fold. First, we propose a system-wide architecture for designing and implementing surveillance systems, based on the authors’ system architecture for generating meta-data. Secondly, we design a simulation model of a multi-view surveillance system from which the researchers generate simulated video streams in large volumes. From each video sequence in the model, the authors extract meta-data and apply a novel algorithm for predicting the location of identifiable objects across a well-connected camera cluster. This research provide evidence that independent surveillance systems (for example, security cameras) can be unified across a geographical location such as a smart city, where each network is administratively owned and managed independently. Our investigation involved 2 experiments - first, the implementation of a web-based solution where we developed a directory service for managing, cataloguing, and persisting metadata generated by the surveillance networks. The second experiment focused on the set up, configuration and the architecture of the surveillance system. These experiments involved the investigation and demonstration of 3 loosely coupled service-oriented APIs – these services provided the capability to generate the query-able metadata. The results of our investigations provided answers to our research questions - the main question being “to what degree of accuracy can we predict the location of an object in a connected surveillance network”. Our experiment also provided evidence in support of our hypothesis – “it is feasible to ‘explore’ unified surveillance data generated from independent surveillance networks”