Recovering human mobility flow models and daily routine patterns in a smart environment

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

With the recent advent of ubiquitous computing and sensor technologies, human mobility data can be acquired for monitoring and analysis purposes, e.g., Daily routine identification. Mining mobility data is challenging due to the spatial and temporal variations of the human mobility, even for the same activity. In this paper, we propose a methodology to first summarize indoor human mobility traces as a flow graph using a probabilistic grammar induction algorithm. Then, we recover salient mobility patterns as sub flows in the flow graph. Thus, such patterns/sub flows are expected to be corresponding to the activities that often last for a while, e.g., Cooking and cleaning. The weighted kernel k-means algorithm is adopted for the sub flow extraction. Finally, we detect the occurrences of the sub flows along the mobility traces and obtain their daily routines via the eigen-decomposition. To evaluate the effectiveness of the proposed methodology, we applied it to a publicly available smart home data set containing digital traces of an elder living in a smart house for 219 days. We illustrate how the flow graphs, sub flows and daily routine patterns can be inferred from the mobility data. Our preliminary experimental results show that the proposed approach can detect sub flows which are more specific in terms of their correspondence to activities when compared with a frequent pattern clustering approach.

Original languageEnglish
Title of host publicationProceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
EditorsZhi-Hua Zhou, Wei Wang, Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
PublisherIEEE Computer Society
Pages541-548
Number of pages8
EditionJanuary
ISBN (Electronic)9781479942749
DOIs
Publication statusPublished - 26 Jan 2015
Event14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 - Shenzhen, China
Duration: 14 Dec 2014 → …

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
NumberJanuary
Volume2015-January
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
Country/TerritoryChina
CityShenzhen
Period14/12/14 → …

Scopus Subject Areas

  • Computer Science Applications
  • Software

User-Defined Keywords

  • daily routine mining
  • graph-based clustering
  • probabilistic grammar model
  • smart house

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