Periodicity intensity for indicating behaviour shifts from lifelog data
Hu, FeiyanORCID: 0000-0001-7451-6438 and Smeaton, Alan F.ORCID: 0000-0003-1028-8389
(2016)
Periodicity intensity for indicating behaviour shifts from lifelog data.
In: The IEEE International Conference on Bioinformatics and Biomedicine 2016 International Workshop on Biomedical and Health Informatics, 15-18 Dec. 2016, Shenzhen, China.
ISBN 978-1-5090-1612-9
Periodic phenomena or oscillating signals can be found frequently in nature and recent research has observed pe- riodicity appearing in lifelog data, the automatic digital recording of everyday activities. In this paper we are exploring periodicity and intensity of periodicity in big data settings, especially when the data is noisy, unevenly sampled and incomplete. An interesting possibility is to compute the intensity or strength of detected periodicity across the time span of a lifelog to see if it reveals changes in this strength at different times, indicating shifts in underlying behaviour. In this paper we propose several metrics to estimate the intensity of periodicity, longitudinally. Evaluation of these metrics is conducted on simulated high-level activity data generated from a proposed model. We also explore periodicity intensity calculated from two real lifelog datasets using. One is “big” data consists of low-level accelerometer data and another one is high level athletic performance data.
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Funders:
Science Foundation Ireland SFI/12/RC/2289, Virginia G. Piper Charitable Trust, the ASU/DCU Catalyst Fund,, European Community 7th Framework Programme (FP7/2007-2013) grant agreement 288199 (Dem@Care).
ID Code:
21469
Deposited On:
15 Dec 2016 12:15 by
Feiyan Hu
. Last Modified 11 Oct 2018 12:45