Time Series Segmentation for Context Recognition in Mobile Devices

  • Authors:
  • Johan Himberg;Kalle Korpiaho;Heikki Mannila;Johanna Tikanmäki;Hannu Toivonen

  • Affiliations:
  • -;-;-;-;-

  • Venue:
  • ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
  • Year:
  • 2001

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Abstract

Recognizing the context of se is important in making mobile devices as simple to use as possible. Finding out what the user's situation is can help the device andunderlying service in providing an adaptive and personalized user interface. The device can infer parts of the context of the user from sensor data: the mobile device can includesensors for acceleration, noise level, luminosity, humidity, etc. In this paper we consider context recognition by unsupervisedsegmentation of time series produced by sensors.Dynamic programming can be used to find segments that minimize the intra-segment variances. While this method produces optimal solutions, it is too slow for long sequencesof data. We present and analyze randomized variations of the algorithm. One of them, Global Iterative Replacement or GIR, gives approximately optimal results in a fraction of the time required by dynamic programming. Wedemonstrate the se of time series segmentation in contextrecognition for mobile phone applications.