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LifeLogging: Personal Big Data
Open accessAbstract
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, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses for information access and retrieval in general. This review is a suitable reference for those seeking an information retrieval scientist’s perspective on lifelogging and the quantified self. Lifelogging represents a phenomenon whereby people can digitally record their own daily lives in varying amounts of detail, for a variety of purposes. In a sense it represents a comprehensive “black box” of a human’s life activities and may offer the potential to mine or infer knowledge about how we live our lives. 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. LifeLogging: Personal Big Data provides 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; hence the book maintains this focus. However, it also reflects on the challenges lifelogging poses for information access and retrieval in general. LifeLogging: Personal Big Data is a suitable reference for those seeking an information retrieval scientist’s perspective on lifelogging and the quantified self.
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Self-citationThe SenseCam as a Tool for Task Observation
Byrne, D., Doherty, A., Jones, G. J. F., Smeaton, A. F., Kumpulainen, S., & Järvelin, K. (2008) · Electronic workshops in computing · doi:10.14236/ewic/hci2008.24 · Cited by 83
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Loveday, C., & Conway, M. A. (2011) · Memory · doi:10.1080/09658211.2011.610803 · Cited by 82
Context-based video retrieval system for the life-log applications
Hori, T., & Aizawa, K. (2003) · Multimedia Information Retrieval · doi:10.1145/973264.973270 · Cited by 79
Callan, J., Allan, J., Clarke, C. L. A., Dumais, S., Evans, D. A., Sanderson, M., & Zhai, C. (2007) · ACM SIGIR Forum · doi:10.1145/1328964.1328967 · Cited by 78
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LSCOM Lexicon Definitions and Annotations (Version 1.0)
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Self-citationNext-Generation Personal Memory Aids
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Multimodal segmentation of lifelog data
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Mann, S., Fung, J., & Aimone, C. (2005) · Cited by 44
VISOR: Towards On-the-Fly Large-Scale Object Category Retrieval
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The Video Browser Showdown: a live evaluation of interactive video search tools
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Building digital life stories for memory support
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Automatically Augmenting Lifelog Events Using Pervasively Generated Content from Millions of People
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Viewing and Controlling Personal Sensor Data: What Do Users Want?
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Semantics-based selection of everyday concepts in visual lifelogging
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Self-citationAugmenting and sharing memory with eyeBlog
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Combining Face Detection and Novelty to Identify Important Events in a Visual Lifelog
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Self-citationContextual Search: A Computational Framework
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Introduction: Time, Materiality, and the Work of Memory
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Replicating Web Structure in Small-Scale Test Collections
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VFerret: content-based similarity search tool for continuous archived video
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Designing a touch-screen sensecam browser to support an aging population
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AXES at TRECVID 2012: KIS, INS, and MED
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Self-citationContextual Search: A Computational Framework
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Self-citationVisualisation of Movement of Older Adults within their Homes based on PIR Sensor Data
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Self-citationSupporting Collaborative Reflection with Passive Image Capture
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A Real-Time Life Experience Logging Tool
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Self-citationAffective computing for wearable diary and lifelogging systems: An overview
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Web Information Retrieval Using Query Independent Page Rank Algorithm
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iForgot: A model of forgetting in robotic memories
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Self-citationUser Interaction Templates for the Design of Lifelogging Systems
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Self-citationBrowsing Large Personal Multimedia Archives in a Lean-Back Environment
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Self-citationInformation access tasks and evaluation for personal lifelogs
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Usage Data in Web Search: Benefits and Limitations
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Usage Data in Web Search: Benefits and Limitations
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An investigation into event decay from large personal media archives
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Self-citationAggregating semantic concepts for event representation in lifelogging
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Self-citationShareDay: A Novel Lifelog Management System for Group Sharing
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Addressing the Challenge of Managing Large-scale Digital Multimedia Libraries.
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Metadata
- Authors
- Gurrin, C., Smeaton, A. F., & Doherty, A. R.
- Venue
- Foundations and Trends® in Information Retrieval
- Publisher
- Emerald
- Published
- 2014-06-16
- Volume
- 8
- Issue
- 1
- Pages
- 1-125
- ISSN
- 1554-0669 (ISSN-L)
- 1554-0677
- OpenAlex
- W2060269880
- Cited by
- 434
- References
- 186
- FWCI
- 33.23
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.