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Sensors 2017, 17(8), 1902; https://doi.org/10.3390/s17081902

Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes

Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia
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Received: 29 June 2017 / Revised: 3 August 2017 / Accepted: 9 August 2017 / Published: 18 August 2017
(This article belongs to the Special Issue Advances in Sensors for Sustainable Smart Cities and Smart Buildings)
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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. View Full-Text
Keywords: sensor selection; tree alignment; needleman-wunsch algorithm; activity recognition; smart homes sensor selection; tree alignment; needleman-wunsch algorithm; activity recognition; smart homes
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Chua, S.-L.; Foo, L.K. Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes. Sensors 2017, 17, 1902.

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