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Context-Induced Activity Monitoring for On-Demand Things-of-Interest Recommendation in an Ambient Intelligent Environment

Author

Listed:
  • May Altulyan

    (School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
    College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia)

  • Lina Yao

    (School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

  • Chaoran Huang

    (School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

  • Xianzhi Wang

    (School of Computer Science, The University of Technology Sydney, Ultimo, NSW 2007, Australia)

  • Salil S. Kanhere

    (School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

Abstract

Recommendation systems are crucial in the provision of services to the elderly with Alzheimer’s disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their homes safely and independently. A contextual bandit approach is utilized in the formulation of the proposed recommendation system to tackle dynamicity in human activities and to construct accurate recommendations that meet user needs without their feedback. The system was evaluated based on three public datasets using a cumulative reward as a metric. Our experimental results demonstrate the feasibility and effectiveness of the proposed Reminder Care System for real-world IoT-based smart home applications.

Suggested Citation

  • May Altulyan & Lina Yao & Chaoran Huang & Xianzhi Wang & Salil S. Kanhere, 2021. "Context-Induced Activity Monitoring for On-Demand Things-of-Interest Recommendation in an Ambient Intelligent Environment," Future Internet, MDPI, vol. 13(12), pages 1-19, November.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:12:p:305-:d:689891
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