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User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

Serge Thomas Mickala Bourobou1, Younghwan Yoo2

  • 1Department of Electrical and Computer Engineering, Pusan National University, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 609-735, Korea. thomaserge@yahoo.fr.

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Summary
This summary is machine-generated.

This study enhances smart home activity recognition by combining K-pattern clustering and artificial neural networks. This integrated approach improves prediction accuracy in dynamic Internet of Things (IoT) environments.

Keywords:
Allen’s temporal relationsactivity recognitionanomaly predictionneural networkpattern clusteringsmart home

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Activity recognition in smart environments is crucial for personalized user experiences.
  • Existing methods often have limited performance due to focusing on single stages of activity recognition.
  • The complexity and variability of user activities pose challenges for accurate classification.

Purpose of the Study:

  • To develop a more accurate and robust system for recognizing and predicting user activities in Internet of Things (IoT) based smart environments.
  • To identify the optimal combination of pattern clustering and activity decision algorithms for enhanced performance.
  • To address the limitations of previous approaches by integrating both clustering and decision-making steps.

Main Methods:

  • Utilized the K-pattern clustering algorithm for unsupervised classification of diverse user activity patterns.
  • Employed artificial neural networks incorporating Allen's temporal relations for activity type decision and prediction.
  • Combined these two methods to create a comprehensive activity recognition framework.

Main Results:

  • The proposed combined method demonstrated higher recognition accuracy for various user activities compared to other data mining classification algorithms.
  • The system proved to be more suitable for dynamic environments, such as IoT-enabled smart homes.
  • Experimental results validated the effectiveness of integrating clustering and temporal relation-based prediction.

Conclusions:

  • The integration of K-pattern clustering and artificial neural networks with temporal relations offers a superior approach to user activity recognition in smart environments.
  • This method effectively handles the complexity and dynamism inherent in IoT settings.
  • The findings suggest a promising direction for developing more intelligent and responsive smart home systems.