Predictability analysis of aperiodic and periodic model for long-term human mobility using ambient sensors

Danaipat Sodkomkham, Roberto Legaspi, Ken Ichi Fukui, Koichi Moriyama, Satoshi Kurihara, Masayuki Numao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationMining, Modeling, and Recommending Things in Social Media - 4th International Workshops MUSE 2013 and MSM 2013, Revised Selected Papers
PublisherSpringer Verlag
Pages131-149
Number of pages19
ISBN (Electronic)9783319147222
DOIs
Publication statusPublished - 2015 Jan 1
Externally publishedYes
Event4th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2013 in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2013 - Prague, Czech Republic
Duration: 2013 Sep 232013 Sep 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8940
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2013 in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2013
CountryCzech Republic
CityPrague
Period13/9/2313/9/23

Fingerprint

Predictability
Mobile phones
Global positioning system
Smart Environments
Sensor
Sensors
Mobile Phone
Cameras
Smart Spaces
Motion Detection
Tracking System
Model
High Accuracy
Camera
Predict
Human
Prediction
Evaluation
Movement

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Sodkomkham, D., Legaspi, R., Fukui, K. I., Moriyama, K., Kurihara, S., & Numao, M. (2015). Predictability analysis of aperiodic and periodic model for long-term human mobility using ambient sensors. In Mining, Modeling, and Recommending Things in Social Media - 4th International Workshops MUSE 2013 and MSM 2013, Revised Selected Papers (pp. 131-149). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8940). Springer Verlag. https://doi.org/10.1007/978-3-319-14723-9_8

Predictability analysis of aperiodic and periodic model for long-term human mobility using ambient sensors. / Sodkomkham, Danaipat; Legaspi, Roberto; Fukui, Ken Ichi; Moriyama, Koichi; Kurihara, Satoshi; Numao, Masayuki.

Mining, Modeling, and Recommending Things in Social Media - 4th International Workshops MUSE 2013 and MSM 2013, Revised Selected Papers. Springer Verlag, 2015. p. 131-149 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8940).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 Mining, Modeling, and Recommending Things in Social Media - 4th International Workshops MUSE 2013 and MSM 2013, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8940, Springer Verlag, pp. 131-149, 4th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2013 in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2013, Prague, Czech Republic, 13/9/23. https://doi.org/10.1007/978-3-319-14723-9_8
Sodkomkham D, Legaspi R, Fukui KI, Moriyama K, Kurihara S, Numao M. Predictability analysis of aperiodic and periodic model for long-term human mobility using ambient sensors. In Mining, Modeling, and Recommending Things in Social Media - 4th International Workshops MUSE 2013 and MSM 2013, Revised Selected Papers. Springer Verlag. 2015. p. 131-149. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-14723-9_8
Sodkomkham, Danaipat ; Legaspi, Roberto ; Fukui, Ken Ichi ; Moriyama, Koichi ; Kurihara, Satoshi ; Numao, Masayuki. / Predictability analysis of aperiodic and periodic model for long-term human mobility using ambient sensors. Mining, Modeling, and Recommending Things in Social Media - 4th International Workshops MUSE 2013 and MSM 2013, Revised Selected Papers. Springer Verlag, 2015. pp. 131-149 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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