The DOI matches its Crossref record; 186 of the 186 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · An Integrated Approach to Communication Theory and Research · doi:10.4324/9780203710753-35 · Cited by 37,322
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · cited 8×
“Bell and Gemmell (2009) in their book “Total Recall” provide a positive and optimistic bigger picture of the potential of lifelogging.”
BackgroundMethodInfluentialValente, T. W. (2003) · Genetics in Medicine · doi:10.1097/01.gim.0000061743.67794.c4 · Cited by 13,723
Michie, S., van Stralen, M. M., & West, R. (2011) · Implementation Science · doi:10.1186/1748-5908-6-42 · Cited by 13,697
(1999) · Cited by 12,854
Physical Activity in the United States Measured by Accelerometer
TROIANO, R. P., BERRIGAN, D., DODD, K. W., MÂSSE, L. C., TILERT, T., & MCDOWELL, M. (2008) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31815a51b3 · Cited by 7,638
Content-based image retrieval at the end of the early years
Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895972 · Cited by 6,056
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. J. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 4,568
Cited by
Design Blueprint for Stress-Sensitive Adaptive Enterprise Systems
Adam, M. T. P., Gimpel, H., Maedche, A., & Riedl, R. (2017) · Business & Information Systems Engineering · doi:10.1007/s12599-016-0451-3 · Cited by 78
It's Just My History Isn't It?: Understanding Smart Journaling Practices
Elsden, C., Durrant, A. C., & Kirk, D. S. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858103 · Cited by 53
Memory Manipulations in Extended Reality
Bonnail, E., Tseng, W.-J., Mcgill, M., Lecolinet, E., Huron, S., & Gugenheimer, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580988 · Cited by 51
Event-based media processing and analysis: A survey of the literature
Tzelepis, C., Ma, Z., Mezaris, V., Ionescu, B., Kompatsiaris, I., Boato, G., Sebe, N., & Yan, S. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.05.005 · Cited by 49
Niforatos, E., Cinel, C., Mack, C. C., Langheinrich, M., & Ward, G. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3090086 · Cited by 40
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: LifeLogging: Personal Big Data. https://app.cochat.ai/papers/10.1561/1500000033
- The paper
- Gurrin, C., Smeaton, A. F., & Doherty, A. R. (2014). LifeLogging: Personal Big Data. Foundations and Trends® in Information Retrieval. https://doi.org/10.1561/1500000033
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.