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DOI: 10.14569/IJACSA.2023.0140484
PDF

Intelligent Abnormal Residents’ Behavior Detection in Smart Homes for Risk Management using Fuzzy Logic Algorithm

Author 1: Bo Feng
Author 2: Lili Miao
Author 3: HuiXiang Liu

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 4, 2023.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: In recent years, the population of sick and elderly people who are alone and need care has increased. This issue increases the need to have a smart home to be aware of the patient's condition. Identifying the patient's activity using sensors embedded in the environment is the first step to reach a smart home where the people around the patient can leave the patient alone at home with less worry. In literature, a variety of methods for detecting the performance of users in the smart home are discussed. In this study, a method for abnormal behavior detection and identifying the level of risk is proposed, in which fuzzy logic is used in cases such as when the activity start. Experimental results demonstrates that the proposed method achieved satisfied performance with 90% accuracy rate that presented better results compared to other existing methods.

Keywords: Smart home; abnormal detection; behavior analysis; activity recognition; elderly people; fuzzy logic

Bo Feng, Lili Miao and HuiXiang Liu, “Intelligent Abnormal Residents’ Behavior Detection in Smart Homes for Risk Management using Fuzzy Logic Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 14(4), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140484

@article{Feng2023,
title = {Intelligent Abnormal Residents’ Behavior Detection in Smart Homes for Risk Management using Fuzzy Logic Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140484},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140484},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Bo Feng and Lili Miao and HuiXiang Liu}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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