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

dc.contributor.author	Survadevara, N. K.
dc.contributor.author	Mukhopadhyay, S. C.
dc.contributor.author	Rayudu, R. K.
dc.date.accessioned	2022-03-27T05:58:04Z
dc.date.available	2022-03-27T05:58:04Z
dc.date.issued	2012-12-01
dc.description.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. © 2012 IEEE.
dc.identifier.citation	Proceedings of the International Conference on Sensing Technology, ICST
dc.identifier.issn	21568065
dc.identifier.uri	10.1109/ICSensT.2012.6461661
dc.identifier.uri	http://ieeexplore.ieee.org/document/6461661/
dc.identifier.uri	https://dspace.uohyd.ac.in/handle/1/8955
dc.subject	Activity Recognition
dc.subject	ADL
dc.subject	ARIMA Time Series
dc.subject	Wellnes
dc.subject	Wireless Sensor Networks
dc.title	Applying SARIMA time series to forecast sleeping activity for wellness model of elderly monitoring in smart home
dc.type	Conference Proceeding. Conference Paper
dspace.entity.type

Now showing 1 - 1 of 1