TY - GEN
T1 - Using periodicity intensity to detect long term behaviour change
AU - Hu, Feiyan
AU - Smeaton, Alan F.
AU - Newman, Eamonn
AU - Buman, Matthew
N1 - Funding Information:
This work was part funded by the EU 7th Framework Programme (FP7/2007-2013) under grant agreement 288199 (Dem@Care), Science Foundation Ireland under grant 12/RC/2289, the Virginia G. Piper Charitable Trust and the ASU/DCU Catalyst Fund.
Publisher Copyright:
© 2015 ACM.
PY - 2015/9/7
Y1 - 2015/9/7
N2 - This paper introduces a new way to analyse and visualize quantified-self or lifelog data captured from any lifelogging device over an extended period of time. The mechanism works on the raw, unstructured lifelog data by detecting periodicities, those repeating patters that occur within our lifestyles at different frequencies including daily, weekly, seasonal, etc. Focusing on the 24 hour cycle, we calculate the strength of the 24-hour periodicity at 24-hour intervals over an extended period of a lifelog. Changes in this strength of the 24-hour cycle can illustrate changes or shifts in underlying human behavior. We have performed this analysis on several lifelog datasets of durations from several weeks to almost a decade, from recordings of training distances to sleep data. In this paper we use 24 hour accelerometer data to illustrate the technique, showing how changes in human behavior can be identified.
AB - This paper introduces a new way to analyse and visualize quantified-self or lifelog data captured from any lifelogging device over an extended period of time. The mechanism works on the raw, unstructured lifelog data by detecting periodicities, those repeating patters that occur within our lifestyles at different frequencies including daily, weekly, seasonal, etc. Focusing on the 24 hour cycle, we calculate the strength of the 24-hour periodicity at 24-hour intervals over an extended period of a lifelog. Changes in this strength of the 24-hour cycle can illustrate changes or shifts in underlying human behavior. We have performed this analysis on several lifelog datasets of durations from several weeks to almost a decade, from recordings of training distances to sleep data. In this paper we use 24 hour accelerometer data to illustrate the technique, showing how changes in human behavior can be identified.
KW - Detecting behaviour change
KW - Lifelogging
KW - Periodicity
KW - Periodogram intensities
UR - http://www.scopus.com/inward/record.url?scp=84959411149&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959411149&partnerID=8YFLogxK
U2 - 10.1145/2800835.2800962
DO - 10.1145/2800835.2800962
M3 - Conference contribution
AN - SCOPUS:84959411149
T3 - UbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers
SP - 1069
EP - 1074
BT - UbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers
PB - Association for Computing Machinery, Inc
T2 - ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015
Y2 - 7 September 2015 through 11 September 2015
ER -