Home > Research > Publications & Outputs > Collecting shared experiences through lifelogging

Electronic data

  • PC_PC_2015_02_0011.R1_Clinch

    Rights statement: ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 937 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

Links

Text available via DOI:

View graph of relations

Collecting shared experiences through lifelogging: lessons learned

Research output: Contribution to journalJournal article

Published

Standard

Collecting shared experiences through lifelogging : lessons learned. / Clinch, Sarah; Davies, Nigel Andrew Justin; Mikusz, Mateusz; Metzger, Paul; Langheinrich, Marc; Schmidt, Albrecht; Ward, Geoff.

In: IEEE Pervasive Computing, Vol. 15, No. 1, 01.2016, p. 58-67.

Research output: Contribution to journalJournal article

Harvard

Clinch, S, Davies, NAJ, Mikusz, M, Metzger, P, Langheinrich, M, Schmidt, A & Ward, G 2016, 'Collecting shared experiences through lifelogging: lessons learned', IEEE Pervasive Computing, vol. 15, no. 1, pp. 58-67. https://doi.org/10.1109/MPRV.2016.6

APA

Vancouver

Author

Clinch, Sarah ; Davies, Nigel Andrew Justin ; Mikusz, Mateusz ; Metzger, Paul ; Langheinrich, Marc ; Schmidt, Albrecht ; Ward, Geoff. / Collecting shared experiences through lifelogging : lessons learned. In: IEEE Pervasive Computing. 2016 ; Vol. 15, No. 1. pp. 58-67.

Bibtex

@article{890bc8e4c1e34dd384f0373cd13fece7,
title = "Collecting shared experiences through lifelogging: lessons learned",
abstract = "The emergence of widespread pervasive sensing, personal recording technologies, and systems for the quantified self are creating an environment in which one can capture fine-grained activity traces. Such traces have wide applicability in domains such as human memory augmentation, behavior change, and healthcare. However, obtaining these traces for research is nontrivial, especially those containing photographs of everyday activities. To source data for their own work, the authors created an experimental setup in which they collected detailed traces of a group of researchers over 2.75 days. They share their experiences of this process and present a series of lessons learned for other members of the research community conducting similar studies.",
keywords = "pervasive computing, big data, data analysis, mobile, pervasive sensing, lifelogging, activity traces",
author = "Sarah Clinch and Davies, {Nigel Andrew Justin} and Mateusz Mikusz and Paul Metzger and Marc Langheinrich and Albrecht Schmidt and Geoff Ward",
note = "{\circledC}2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2016",
month = "1",
doi = "10.1109/MPRV.2016.6",
language = "English",
volume = "15",
pages = "58--67",
journal = "IEEE Pervasive Computing",
issn = "1536-1268",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Collecting shared experiences through lifelogging

T2 - lessons learned

AU - Clinch, Sarah

AU - Davies, Nigel Andrew Justin

AU - Mikusz, Mateusz

AU - Metzger, Paul

AU - Langheinrich, Marc

AU - Schmidt, Albrecht

AU - Ward, Geoff

N1 - ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2016/1

Y1 - 2016/1

N2 - The emergence of widespread pervasive sensing, personal recording technologies, and systems for the quantified self are creating an environment in which one can capture fine-grained activity traces. Such traces have wide applicability in domains such as human memory augmentation, behavior change, and healthcare. However, obtaining these traces for research is nontrivial, especially those containing photographs of everyday activities. To source data for their own work, the authors created an experimental setup in which they collected detailed traces of a group of researchers over 2.75 days. They share their experiences of this process and present a series of lessons learned for other members of the research community conducting similar studies.

AB - The emergence of widespread pervasive sensing, personal recording technologies, and systems for the quantified self are creating an environment in which one can capture fine-grained activity traces. Such traces have wide applicability in domains such as human memory augmentation, behavior change, and healthcare. However, obtaining these traces for research is nontrivial, especially those containing photographs of everyday activities. To source data for their own work, the authors created an experimental setup in which they collected detailed traces of a group of researchers over 2.75 days. They share their experiences of this process and present a series of lessons learned for other members of the research community conducting similar studies.

KW - pervasive computing

KW - big data

KW - data analysis

KW - mobile

KW - pervasive sensing

KW - lifelogging

KW - activity traces

U2 - 10.1109/MPRV.2016.6

DO - 10.1109/MPRV.2016.6

M3 - Journal article

VL - 15

SP - 58

EP - 67

JO - IEEE Pervasive Computing

JF - IEEE Pervasive Computing

SN - 1536-1268

IS - 1

ER -