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

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 8940)

Abstract

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.

Keywords

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

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Notes

  1. 1.

    Placement of each point of interest \(x_i\) can be found in Fig. 1a.

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Acknowledgement

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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Correspondence to Danaipat Sodkomkham .

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Sodkomkham, D., Legaspi, R., Fukui, Ki., Moriyama, K., Kurihara, S., Numao, M. (2015). Predictability Analysis of Aperiodic and Periodic Model for Long-Term Human Mobility Using Ambient Sensors. In: Atzmueller, M., Chin, A., Scholz, C., Trattner, C. (eds) Mining, Modeling, and Recommending 'Things' in Social Media. MUSE MSM 2013 2013. Lecture Notes in Computer Science(), vol 8940. Springer, Cham. https://doi.org/10.1007/978-3-319-14723-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-14723-9_8

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