TY - GEN
T1 - Predictability analysis of aperiodic and periodic model for long-term human mobility using ambient sensors
AU - Sodkomkham, Danaipat
AU - Legaspi, Roberto
AU - Fukui, Ken Ichi
AU - Moriyama, Koichi
AU - Kurihara, Satoshi
AU - Numao, Masayuki
N1 - Publisher Copyright:
©Springer International Publishing Switzerland 2015
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Fano’s inequality
KW - Human mobility
KW - Long-term prediction
KW - Predictability analysis
KW - Smart environment
UR - https://www.scopus.com/pages/publications/84927646679
UR - https://www.scopus.com/pages/publications/84927646679#tab=citedBy
U2 - 10.1007/978-3-319-14723-9_8
DO - 10.1007/978-3-319-14723-9_8
M3 - Conference contribution
AN - SCOPUS:84927646679
T3 - Lecture Notes in Computer Science
SP - 131
EP - 149
BT - Mining, Modeling, and Recommending Things in Social Media - 4th International Workshops MUSE 2013 and MSM 2013, Revised Selected Papers
A2 - Atzmueller, Martin
A2 - Scholz, Christoph
A2 - Chin, Alvin
A2 - Trattner, Christoph
PB - Springer Verlag
T2 - 4th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2013 held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2013 and 4th International Workshop on Modeling Social Media, MSM 2013, held in conjunction with ACM Hypertext 2013
Y2 - 23 September 2013 through 23 September 2013
ER -