Citation

Chua, Sook Ling and Foo, Lee Kien (2017) Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes. Sensors, 17 (8). p. 1902. ISSN 1424-8220

Text sensors-17-01902.pdf Restricted to Repository staff only Download (346kB)

Abstract

Activity recognition in smart homes aims to infer the particular activities of the inhabitant, the aim being to monitor their activities and identify any abnormalities, especially for those living alone. In order for a smart home to support its inhabitant, the recognition system needs to learn from observations acquired through sensors. One question that often arises is which sensors are useful and how many sensors are required to accurately recognise the inhabitant’s activities? Many wrapper methods have been proposed and remain one of the popular evaluators for sensor selection due to its superior accuracy performance. However, they are prohibitively slow during the evaluation process and may run into the risk of overfitting due to the extent of the search. Motivated by this characteristic, this paper attempts to reduce the cost of the evaluation process and overfitting through tree alignment. The performance of our method is evaluated on two public datasets obtained in two distinct smart home environments.

Item Type:	Article
Uncontrolled Keywords:	Sensor networks
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	21 Oct 2020 19:36
Last Modified:	21 Oct 2020 19:36
URII:	http://shdl.mmu.edu.my/id/eprint/7057

Downloads

Edit (login required)