Predictability Analysis of Aperiodic and Periodic Model for Long-Term Human Mobility Using Ambient Sensors

  • Danaipat SodkomkhamEmail author
  • Roberto Legaspi
  • Ken-ichi Fukui
  • Koichi Moriyama
  • Satoshi Kurihara
  • Masayuki Numao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8940)


The predictive technique proposed in this project was initially designed for an indoor smart environment wherein intrusive tracking techniques, such as cameras, mobile phones, and GPS tracking systems, could not be appropriately utilized. Instead, we installed simple motion detection sensors in various areas of the experimental space and observed movements. However, the data collected cannot provide as much information about human mobility as data from a GPS or mobile phone. In this paper, we conducted an exhaustive analysis to determine the predictability of future mobility of people using only this limited dataset. Furthermore, we proposed an aperiodic and periodic predictive technique for long-term human mobility prediction that works well with our limited dataset. The evaluation of the dataset collected of the movement and daily activity in the smart space for three months shows that our model is able to predict future mobility and activities of participants in the smart environment setting with high accuracy – even for a month in advance.


Human mobility Smart environment Long-term prediction Fano’s inequality Predictability analysis 



This work was partly supported by JSPS Strategic Young Researcher Overseas Visits Program for Accelerating Brain Circulation and JSPS Core-to-Core Program, A. Advanced Research Networks.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Danaipat Sodkomkham
    • 1
    Email author
  • Roberto Legaspi
    • 2
  • Ken-ichi Fukui
    • 1
  • Koichi Moriyama
    • 1
  • Satoshi Kurihara
    • 1
  • Masayuki Numao
    • 1
  1. 1.Institute of Scientific and Industrial ResearchOsaka UniversityOsakaJapan
  2. 2.Research Organization of Information and SystemsTransdisciplinary Research Integration Center, The Institute of Statistical MathematicsTokyoJapan

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