A lifelogging system supporting multimodal access

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

Periodicity detection and its application in lifelog data

Wearable sensors are catching our attention not only in industry but also in the market. We can now acquire sensor data from different types of health tracking devices like smart watches, smart bands, lifelog cameras and most smart phones are capable of tracking and logging information using built-in sensors. As data is generated and collected from various sources constantly, researchers have focused on interpreting and understanding the semantics of this longitudinal multi-modal data. One challenge is the fusion of multi-modal data and achieving good performance on tasks such activity recognition, event detection and event segmentation. The classical approach to process the data generated by wearable sensors has three main parts: 1) Event segmentation 2) Event recognition 3) Event retrieval. Many papers have been published in each of the three fields. This thesis has focused on the longitudinal aspect of the data from wearable sensors, instead of concentrating on the data over a short period of time. The following aspects are several key research questions in the thesis. Does longitudinal sensor data have unique features than can distinguish the subject generating the data from other subjects ? In other words, from the longitudinal perspective, does the data from different subjects share more common structure/similarity/identical patterns so that it is difficult to identify a subject using the data. If this is the case, what are those common patterns ? If we are able to eliminate those similarities among all the data, does the data show more specific features that we can use to model the data series and predict the future values ? If there are repeating patterns in longitudinal data, we can use different methods to compute the periodicity of the recurring patterns and furthermore to identify and extract those patterns. Following that we could be able to compare local data over a short time period with more global patterns in order to show the regularity of the local data. Some case studies are included in the thesis to show the value of longitudinal lifelog data related to a correlation of health conditions and training performance

A multimodal approach for event detection from lifelogs

This paper analyzes how personal lifelog data which contains biometric, visual, activity data, can be leveraged to detect points in time where the individual is partaking in an eating activity. To answer this question, three artificial neural network models were introduced. Firstly, a image object detection model trained to detect eating related objects using the YOLO framework. Secondly, a feed-forward neural network (FANN) and a Long-Short-Term-Memory (LSTM) neural network model which attempts to detect ‘eating moments’ in the lifelog data. The results show promise, with F1-score and AUC score of 0.489 and 0.796 for the FANN model, and F1-score of 0.74 and AUC score of 0.835 respectively. However, there are clear rooms for improvement on all models. The models and methods introduced can help individuals monitor their nutrition habits so they are empowered to make healthy lifestyle decisions. Additionally, several methods for streamlining event detection in lifelog data are introduced.Masteroppgave i informasjonsvitenskapINFO390MASV-INFOMASV-IK

Learning and mining from personal digital archives

Given the explosion of new sensing technologies, data storage has become significantly cheaper and consequently, people increasingly rely on wearable devices to create personal digital archives. Lifelogging is the act of recording aspects of life in digital format for a variety of purposes such as aiding human memory, analysing human lifestyle and diet monitoring. In this dissertation we are concerned with Visual Lifelogging, a form of lifelogging based on the passive capture of photographs by a wearable camera. Cameras, such as Microsoft's SenseCam can record up to 4,000 images per day as well as logging data from several incorporated sensors. Considering the volume, complexity and heterogeneous nature of such data collections, it is a signifcant challenge to interpret and extract knowledge for the practical use of lifeloggers and others. In this dissertation, time series analysis methods have been used to identify and extract useful information from temporal lifelogging images data, without benefit of prior knowledge. We focus, in particular, on three fundamental topics: noise reduction, structure and characterization of the raw data; the detection of multi-scale patterns; and the mining of important, previously unknown repeated patterns in the time series of lifelog image data. Firstly, we show that Detrended Fluctuation Analysis (DFA) highlights the feature of very high correlation in lifelogging image collections. Secondly, we show that study of equal-time Cross-Correlation Matrix demonstrates atypical or non-stationary characteristics in these images. Next, noise reduction in the Cross-Correlation Matrix is addressed by Random Matrix Theory (RMT) before Wavelet multiscaling is used to characterize the `most important' or `unusual' events through analysis of the associated dynamics of the eigenspectrum. A motif discovery technique is explored for detection of recurring and recognizable episodes of an individual's image data. Finally, we apply these motif discovery techniques to two known lifelog data collections, All I Have Seen (AIHS) and NTCIR-12 Lifelog, in order to examine multivariate recurrent patterns of multiple-lifelogging users

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one