227 research outputs found
On the Place of Text Data in Lifelogs, and Text Analysis via Semantic Facets
Current research in lifelog data has not paid enough attention to analysis of
cognitive activities in comparison to physical activities. We argue that as we
look into the future, wearable devices are going to be cheaper and more
prevalent and textual data will play a more significant role. Data captured by
lifelogging devices will increasingly include speech and text, potentially
useful in analysis of intellectual activities. Analyzing what a person hears,
reads, and sees, we should be able to measure the extent of cognitive activity
devoted to a certain topic or subject by a learner. Test-based lifelog records
can benefit from semantic analysis tools developed for natural language
processing. We show how semantic analysis of such text data can be achieved
through the use of taxonomic subject facets and how these facets might be
useful in quantifying cognitive activity devoted to various topics in a
person's day. We are currently developing a method to automatically create
taxonomic topic vocabularies that can be applied to this detection of
intellectual activity
An interactive lifelog search engine for LSC2018
In this work, we describe an interactive lifelog search engine developed for the LSC 2018 search challenge at ACM ICMR 2018. The paper introduces the four-step process required to support lifelog search engines and describes the source data for the search engine as well as the approach to ranking chosen for the iterative search engine. Finally the interface used is introduced before we highlight the limits of the current prototype and suggest opportunities for future work.Peer ReviewedPostprint (published version
Socio-technical lifelogging: deriving design principles for a future proof digital past
Lifelogging is a technically inspired approach that attempts to address the problem of human forgetting by developing systems that ‘record everything’. Uptake of lifelogging systems has generally been disappointing, however. One reason for this lack of uptake is the absence of design principles for developing digital systems to support memory. Synthesising multiple studies, we identify and evaluate 4 new empirically motivated design principles for lifelogging: Selectivity, Embodiment, Synergy and Reminiscence. We first summarise 4 empirical studies that motivate the principles, then describe the evaluation of 4 novel systems built to embody these principles. The design principles were generative, leading to the development of new classes of lifelogging system, as well as providing strategic guidance about how those systems should be built. Evaluations suggest support for Selection and Embodiment principles, but more conceptual and technical work is needed to refine the Synergy and Reminiscence principles
Experiments in lifelog organisation and retrieval at NTCIR
Lifelogging can be described as the process by which individuals use various software and hardware devices to gather large archives of multimodal personal data from multiple sources and store them in a personal data archive, called a lifelog. The Lifelog task at NTCIR was a comparative benchmarking exercise with the aim of encouraging research into the organisation and retrieval of data from multimodal lifelogs. The Lifelog task ran for over 4 years from NTCIR-12 until NTCIR-14 (2015.02–2019.06); it supported participants to submit to five subtasks, each tackling a different challenge related to lifelog retrieval. In this chapter, a motivation is given for the Lifelog task and a review of progress since NTCIR-12 is presented. Finally, the lessons learned and challenges within the domain of lifelog retrieval are presented
Designing and evaluating a user interface for continous embedded lifelogging based on physical context
PhD ThesisAn increase in both personal information and storage capacity has encouraged people to
store and archive their life experience in multimedia formats. The usefulness of such
large amounts of data will remain inadequate without the development of both retrieval
techniques and interfaces that help people access and navigate their personal collections.
The research described in this thesis investigates lifelogging technology from the
perspective of the psychology of memory and human-computer interaction. The
research described seeks to increase my understanding of what data can trigger
memories and how I might use this insight to retrieve past life experiences in interfaces
to lifelogging technology.
The review of memory and previous research on lifelogging technology allows and
support me to establish a clear understanding of how memory works and design novel
and effective memory cues; whilst at the same time I critiqued existing lifelogging
systems and approaches to retrieving memories of past actions and activities. In the
initial experiments I evaluated the design and implementation of a prototype which
exposed numerous problems both in the visualisation of data and usability. These
findings informed the design of novel lifelogging prototype to facilitate retrieval. I
assessed the second prototype and determined how an improved system supported
access and retrieval of users’ past life experiences, in particular, how users group their
data into events, how they interact with their data, and the classes of memories that it
supported.
In this doctoral thesis I found that visualizing the movements of users’ hands and
bodies facilitated grouping activities into events when combined with the photos and
other data captured at the same time. In addition, the movements of the user's hand and
body and the movements of some objects can promote an activity recognition or support
user detection and grouping of them into events. Furthermore, the ability to search for
specific movements significantly reduced the amount of time that it took to retrieve data
related to specific events. I revealed three major strategies that users followed to
understand the combined data: skimming sequences, cross sensor jumping and
continued scanning
Overview of NTCIR-12 Lifelog Task
In this paper we review the NTCIR12-Lifelog pilot task,
which ran at NTCIR-12. We outline the test collection employed,
along with the tasks, the eight submissions and the
findings from this pilot task. We finish by suggesting future
plans for the task
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
Overview of NTCIR-13 Lifelog-2 Task
In this paper we review the NTCIR13-Lifelog core task, which ran at NTCIR-13. We outline the test collection employed, along with the tasks, the submissions and the findings from this pilot task. We finish by suggesting future plans for the task
Providing effective memory retrieval cues through automatic structuring and augmentation of a lifelog of images
Lifelogging is an area of research which is concerned with the capture of many aspects of an individual's life digitally, and within this rapidly emerging field is the significant challenge of managing images passively captured by an individual of their daily life. Possible applications vary from helping those with neurodegenerative conditions recall events from memory, to the maintenance and augmentation of extensive image collections of a tourist's trips. However, a large lifelog of images can quickly amass, with an average of 700,000 images captured each year, using a device such as the SenseCam. We address the problem of managing this vast collection of personal images by investigating automatic techniques that: 1. Identify distinct events within a full day of lifelog images (which typically consists of 2,000 images) e.g. breakfast, working on PC, meeting, etc. 2. Find similar events to a given event in a person's lifelog e.g. "show me other events where I was in the park" 3. Determine those events that are more important or unusual to the user and also select a relevant keyframe image for visual display of an event e.g. a "meeting" is more interesting to review than "working on PC" 4. Augment the images from a wearable camera with higher quality images from external "Web 2.0" sources e.g. find me pictures taken by others of the U2 concert in Croke Park In this dissertation we discuss novel techniques to realise each of these facets and how effective they are. The significance of this work is not only of benefit to the lifelogging community, but also to cognitive psychology researchers studying the potential benefits of lifelogging devices to those with neurodegenerative diseases
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