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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Improving Recognition of Overlapping Activities with Less Interclass Variations in Smart Homes through

Muhammad Usman Sarwar1, Labiba Fahad Gillani1, Ahmad Almadhor2

  • 1Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan.

Computational Intelligence and Neuroscience
|June 13, 2022
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This study introduces a new method for smart home activity recognition, improving how overlapping activities are identified. The cluster-based classification approach enhances health monitoring and elderly care systems by accurately distinguishing similar actions.

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Area of Science:

  • * Pervasive computing
  • * Machine learning
  • * Human-computer interaction

Background:

  • * Smart home systems leverage sensing technology and machine learning for health monitoring, elderly care, and independent living.
  • * A significant challenge in smart homes is recognizing overlapping activities due to low inter-class variation.
  • * Similar sensor usage and activity locations contribute to the overlapping activity recognition problem.

Purpose of the Study:

  • * To address and improve the recognition performance of overlapping activities in smart home environments.
  • * To propose a novel approach, overlapping activity recognition using cluster-based classification (OAR-CbC), for generic overlapping activity recognition.
  • * To evaluate the effectiveness of OAR-CbC against existing methods.

Main Methods:

  • * Implemented a soft partitioning technique for coarse-grained separation of homogeneous and non-homogeneous activities.
  • * Utilized a fine-grained approach where classifiers are trained independently within each cluster after balancing activities.
  • * Compared four partitioning and classification techniques within a consistent hierarchical framework.

Main Results:

  • * The OAR-CbC approach demonstrated promising results on the Aruba and Milan smart home datasets.
  • * Evaluation using threefold and leave-one-day-out cross-validation confirmed the model's reliability via precision, recall, F score, accuracy, and confusion matrices.
  • * The proposed method significantly boosted the recognition rate of overlapping activities compared to state-of-the-art studies.

Conclusions:

  • * The OAR-CbC method effectively tackles the challenge of overlapping activity recognition in smart homes.
  • * This approach enhances the robustness of smart home systems for applications like health monitoring and elderly care.
  • * OAR-CbC offers a significant improvement over existing methods for recognizing complex, overlapping human activities.