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Cumulative Belief Rule-Based Expert System for Multi-Resident Activity Recognition in Smart Home

  • Long-Hao Yang
  • , Yi-Xuan Lu
  • , Peng-Peng Huang
  • , Fei-Fei Ye
  • , Hai-Dong Wu
  • , Jun Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the aging of the population gradually serious in recent years, the research of multi-resident activity recognition in smart home has been paid much attention. For this reason, an advanced rule-based expert system, called cumulative belief rule-based expert system, is introduced to develop a novel multi-resident activity recognition model, which not only makes full use of the multiple labels of residents' activities, but also can overcome the problem of excessive data collected from smart home. In the case study, the experimental study shows that the proposed model is more efficient and accurate than the traditional machine learning models and the commonly used activity recognition model for achieving multi-resident activity recognition.
Original languageEnglish
Title of host publication2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)
PublisherIEEE
ISBN (Electronic)979-8-3503-1840-1, 979-8-3503-1839-5
ISBN (Print)979-8-3503-1841-8
DOIs
Publication statusPublished (in print/issue) - 17 Nov 2023
Event18th International Conference on Intelligent Systems and Knowledge Engineering - Fuzhou, China
Duration: 17 Nov 202319 Nov 2023
Conference number: 18
http://www.iske2023.com

Conference

Conference18th International Conference on Intelligent Systems and Knowledge Engineering
Abbreviated titleISKE 2023
Country/TerritoryChina
CityFuzhou
Period17/11/2319/11/23
Internet address

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • Sociology
  • Smart homes
  • Machine learning
  • Activity recognition
  • Aging
  • Predictive models
  • Safety

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