Applying SARIMA time series to forecast sleeping activity for wellness model of elderly monitoring in smart home

N. K. Survadevara; S. C. Mukhopadhyay; R. K. Rayudu

doi:10.1109/ICSensT.2012.6461661

Applying SARIMA time series to forecast sleeping activity for wellness model of elderly monitoring in smart home

N. K. Survadevara^*, S. C. Mukhopadhyay, R. K. Rayudu

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution

14 Citations (Scopus)

Abstract

In this paper, we have reported a mechanism to forecast the sensing durations of various object usages in a smart home environment. Prognosis will assist in determining the quantitative well-being of an elderly and notify the daily activity behavior as regular or irregular. Prediction process involved in wellness model is the seasonal auto regression integration moving average routines based on the recorded sensing active status of everyday objects used by an elderly living alone.

Original language	English
Title of host publication	2012 6th International Conference on Sensing Technology, ICST 2012
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	157-162
Number of pages	6
ISBN (Electronic)	9781467322485
ISBN (Print)	9781467322461
DOIs	https://doi.org/10.1109/ICSensT.2012.6461661
Publication status	Published - 2012
Externally published	Yes
Event	2012 6th International Conference on Sensing Technology, ICST 2012 - Kolkata, India Duration: 18 Dec 2012 → 21 Dec 2012

Other

Other	2012 6th International Conference on Sensing Technology, ICST 2012
Country	India
City	Kolkata
Period	18/12/12 → 21/12/12

Access to Document

10.1109/ICSensT.2012.6461661

Link to publication in Scopus

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Survadevara, N. K., Mukhopadhyay, S. C., & Rayudu, R. K. (2012). Applying SARIMA time series to forecast sleeping activity for wellness model of elderly monitoring in smart home. In 2012 6th International Conference on Sensing Technology, ICST 2012 (pp. 157-162). [6461661] Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICSensT.2012.6461661

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title = "Applying SARIMA time series to forecast sleeping activity for wellness model of elderly monitoring in smart home",

abstract = "In this paper, we have reported a mechanism to forecast the sensing durations of various object usages in a smart home environment. Prognosis will assist in determining the quantitative well-being of an elderly and notify the daily activity behavior as regular or irregular. Prediction process involved in wellness model is the seasonal auto regression integration moving average routines based on the recorded sensing active status of everyday objects used by an elderly living alone.",

author = "Survadevara, {N. K.} and Mukhopadhyay, {S. C.} and Rayudu, {R. K.}",

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Survadevara, NK, Mukhopadhyay, SC & Rayudu, RK 2012, Applying SARIMA time series to forecast sleeping activity for wellness model of elderly monitoring in smart home. in 2012 6th International Conference on Sensing Technology, ICST 2012., 6461661, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 157-162, 2012 6th International Conference on Sensing Technology, ICST 2012, Kolkata, India, 18/12/12. https://doi.org/10.1109/ICSensT.2012.6461661

Applying SARIMA time series to forecast sleeping activity for wellness model of elderly monitoring in smart home. / Survadevara, N. K.; Mukhopadhyay, S. C.; Rayudu, R. K.

2012 6th International Conference on Sensing Technology, ICST 2012. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2012. p. 157-162 6461661.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution

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Survadevara NK, Mukhopadhyay SC, Rayudu RK. Applying SARIMA time series to forecast sleeping activity for wellness model of elderly monitoring in smart home. In 2012 6th International Conference on Sensing Technology, ICST 2012. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2012. p. 157-162. 6461661 https://doi.org/10.1109/ICSensT.2012.6461661