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.
UbiComp '15 Adjunct:Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. Ubiquitous Computing
.
Association for Computing Machinery. ISBN 978-1-4503-3575-1/15/09
This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:
Virginia G. Piper Charitable Trust, ASU/DCU Catalyst Fund, Science Foundation Ireland grant 12/RC/2289, European Community 7th Framework Programme (FP7/2007- 2013) under grant agreement 288199 (Dem@Care)
ID Code:
20782
Deposited On:
16 Sep 2015 10:18 by
Feiyan Hu
. Last Modified 11 Oct 2018 12:50