The value of information cues for lifelog video navigation

With the advent of lifelogging cameras the amount of personal video material is massively growing to an extent that easily overwhelms the user. To efficiently review lifelog data, we need well designed video navigation tools. In this paper, we analyze which cues are most beneficial for lifelog video navigation. We show that the information kind determines the most appropriate cue in single cue systems, but that multicue approaches are more appreciated. These findings can inspire to design video players with multiple navigation cues, including time, place, persons, and events for easier and more efficient lifelog video retrieval

Experiences of aiding autobiographical memory Using the SenseCam

Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible

Experiences of aiding autobiographical memory using the sensecam

Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible

Information access for personal media archives

It is now possible to archive much of our life experiences in digital form using a variety of sources, e.g. blogs written, tweets made, photographs taken, etc. Information can be captured from a myriad of personal information devices. In this workshop, researchers from diverse disciplines discussed how we can advance towards the goal of effective capture, retrieval and exploration of e-memories. Proposed solutions included advanced textile sensors to capture new data, P2P methods to store this data, and personal reflection applications to review this data. Much discussion centered around search and navigation strategies, interactive interfaces, and the cognitive basis in using digitally captured information as memorabilia

Communicating with your E-memory: finding and refinding in personal lifelogs

The rapid development of technology enables the digital capture and storage of our life experiences in an “E-Memory” (electronic–memory) or personal lifelog (PLL). This offers the potential for people to store the details of their life in a permanent archive, so that the information is still available even when its physical existence has vanished and when memory traces of it have faded away. A major challenge for PLLs is enabling people to access information when it is needed. Many people may also want to share or transfer some of their memory to their friends and descendants, so that their experiences can be appreciated and their knowledge can be kept even after they have passed away. This thesis further explores people’s potential needs from their own PLLs, discuss the possible methods people may use and potential problems that they may encounter while accessing their PLLs, and hypothesize that better support of users’ own memory can provide better user experience and improved efficiency for accessing their E-memories (or PLLs). As part of a larger project, three lifeloggers collected their own prototype lifelog collection for about 20 months’ time. To complete this study, the author developed a prototype PLL system, called the iCLIPS Lifelog Archive Browser (LAB), based on the author’s theoretical exploration and empirical studies, and evaluated it using our prototype lifelog collections through a user study with the three lifeloggers. The results of this study provide promising evidence which support the hypothesis. The end of this thesis also discusses the issues that the lifeloggers encountered in using their lifelogs and future technologies that are desirable based the studies in this thesis

IAPMA 2011: 2nd Workshop on information access to personal media archives

Towards e-Memories: challenges of capturing, summarising, presenting, understanding, using, and retrieving relevant information from heterogeneous data contained in personal media archives. Welcome to IAPMA 2011, the second international workshop on "Information Access for Personal Media Archives". It is now possible to archive much of our life experiences in digital form using a variety of sources, e.g. blogs written, tweets made, social network status updates, photographs taken, videos seen, music heard, physiological monitoring, locations visited and environmentally sensed data of those places, details of people met, etc. Information can be captured from a myriad of personal information devices including desktop computers, PDAs, digital cameras, video and audio recorders, and various sensors, including GPS, Bluetooth, and biometric devices

LifeLogging: personal big data

We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging in order to capture life details of life activities, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses to an information retrieval scientist. This review is a suitable reference for those seeking a information retrieval scientist’s perspective on lifelogging and the quantified self

Semantic interpretation of events in lifelogging

The topic of this thesis is lifelogging, the automatic, passive recording of a person’s daily activities and in particular, on performing a semantic analysis and enrichment of lifelogged data. Our work centers on visual lifelogged data, such as taken from wearable cameras. Such wearable cameras generate an archive of a person’s day taken from a first-person viewpoint but one of the problems with this is the sheer volume of information that can be generated. In order to make this potentially very large volume of information more manageable, our analysis of this data is based on segmenting each day’s lifelog data into discrete and non-overlapping events corresponding to activities in the wearer’s day. To manage lifelog data at an event level, we define a set of concepts using an ontology which is appropriate to the wearer, applying automatic detection of concepts to these events and then semantically enriching each of the detected lifelog events making them an index into the events. Once this enrichment is complete we can use the lifelog to support semantic search for everyday media management, as a memory aid, or as part of medical analysis on the activities of daily living (ADL), and so on. In the thesis, we address the problem of how to select the concepts to be used for indexing events and we propose a semantic, density- based algorithm to cope with concept selection issues for lifelogging. We then apply activity detection to classify everyday activities by employing the selected concepts as high-level semantic features. Finally, the activity is modeled by multi-context representations and enriched by Semantic Web technologies. The thesis includes an experimental evaluation using real data from users and shows the performance of our algorithms in capturing the semantics of everyday concepts and their efficacy in activity recognition and semantic enrichment