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

Author

Survadevara, N.K. ; Mukhopadhyay, S.C. ; Rayudu, Ramesh K.

Author_Institution

Massey Univ., Palmerston North, New Zealand

fYear

2012

fDate

18-21 Dec. 2012

Firstpage

157

Lastpage

162

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.

Keywords

assisted living; autoregressive moving average processes; biomedical communication; geriatrics; home computing; sleep; time series; SARIMA time series; daily activity behavior; elderly monitoring; sensing durations; sleeping activity forecasting; smart home environment; wellness model; Forecasting; Monitoring; Predictive models; Senior citizens; Sensors; Smart homes; Time series analysis; ADL; ARIMA Time Series; Activity Recognition; Wellnes; Wireless Sensor Networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Sensing Technology (ICST), 2012 Sixth International Conference on

Conference_Location

Kolkata

ISSN

2156-8065

Print_ISBN

978-1-4673-2246-1

Type

conf

DOI

10.1109/ICSensT.2012.6461661

Filename

6461661