Activity Mining in a Smart Home from Sequential and Temporal Databases

Josky Aízan, Cina Motamed, Eugene Ezin

Abstract

In this paper, we implement the Sequential Pattern Mining from Temporal Databases to learn activity in a smart home. The Pre-processing is firstly conducted on sensor data by taking into account the timestamp of sensor events. Then we extract typical activities using a sequential pattern mining algorithm. In order to perform activities’ recognition, features are extracted and activities are modeled. Experiments are carried out on the Massachusetts Institute of Technology (MIT) smart home data set. The results show the effectiveness of the proposed approach with 99% as recognition rate.

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Paper Citation


in Harvard Style

Aízan J., Motamed C. and Ezin E. (2020). Activity Mining in a Smart Home from Sequential and Temporal Databases.In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 542-547. DOI: 10.5220/0009061105420547


in Bibtex Style

@conference{icpram20,
author={Josky Aízan and Cina Motamed and Eugene Ezin},
title={Activity Mining in a Smart Home from Sequential and Temporal Databases},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={542-547},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009061105420547},
isbn={978-989-758-397-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Activity Mining in a Smart Home from Sequential and Temporal Databases
SN - 978-989-758-397-1
AU - Aízan J.
AU - Motamed C.
AU - Ezin E.
PY - 2020
SP - 542
EP - 547
DO - 10.5220/0009061105420547