Type

PhD Thesis

Authors

Zhengwei Qiu

Subjects

Computer Science

Topics
information storage and retrieval systems social environment system access data analysis lifelog data collection data storage sensor data

A lifelogging system supporting multimodal access (2013)

Abstract Today, technology has progressed to allow us to capture our lives digitally such as taking pictures, recording videos and gaining access to WiFi to share experiences using smartphones. People’s lifestyles are changing. One example is from the traditional memo writing to the digital lifelog. Lifelogging is the process of using digital tools to collect personal data in order to illustrate the user’s daily life (Smith et al., 2011). The availability of smartphones embedded with different sensors such as camera and GPS has encouraged the development of lifelogging. It also has brought new challenges in multi-sensor data collection, large volume data storage, data analysis and appropriate representation of lifelog data across different devices. This study is designed to address the above challenges. A lifelogging system was developed to collect, store, analyse, and display multiple sensors’ data, i.e. supporting multimodal access. In this system, the multi-sensor data (also called data streams) is firstly transmitted from smartphone to server only when the phone is being charged. On the server side, six contexts are detected namely personal, time, location, social, activity and environment. Events are then segmented and a related narrative is generated. Finally, lifelog data is presented differently on three widely used devices which are the computer, smartphone and E-book reader. Lifelogging is likely to become a well-accepted technology in the coming years. Manual logging is not possible for most people and is not feasible in the long-term. Automatic lifelogging is needed. This study presents a lifelogging system which can automatically collect multi-sensor data, detect contexts, segment events, generate meaningful narratives and display the appropriate data on different devices based on their unique characteristics. The work in this thesis therefore contributes to automatic lifelogging development and in doing so makes a valuable contribution to the development of the field.
Collections Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Thesis Type = Doctoral Thesis
Ireland -> Dublin City University -> Publication Type = Thesis
Ireland -> Dublin City University -> Subject = Computer Science
Ireland -> Dublin City University -> Subject = Computer Science: Information storage and retrieval systems
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools
Ireland -> Dublin City University -> Status = Unpublished
Ireland -> Dublin City University -> Subject = Computer Science: Lifelog
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing

Full list of authors on original publication

Zhengwei Qiu

Experts in our system

1
Zhengwei Qiu
Dublin City University
Total Publications: 18