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Related Experiment Videos

Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes.

Sook-Ling Chua1, Lee Kien Foo2

  • 1Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia. slchua@mmu.edu.my.

Sensors (Basel, Switzerland)
|August 19, 2017
PubMed
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This study introduces tree alignment to improve smart home activity recognition sensor selection. The method efficiently identifies useful sensors, reducing evaluation costs and preventing overfitting for better inhabitant monitoring.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Smart home activity recognition systems infer inhabitant behavior for monitoring and abnormality detection, crucial for individuals living alone.
  • Effective activity recognition relies on learning from sensor data, raising questions about optimal sensor selection and quantity.
  • Existing wrapper methods for sensor selection offer high accuracy but suffer from slow evaluation and overfitting risks.

Purpose of the Study:

  • To reduce the computational cost and prevent overfitting in smart home activity recognition sensor selection.
  • To propose and evaluate a novel tree alignment method for efficient sensor evaluation.

Main Methods:

  • Developed a tree alignment approach to optimize the sensor selection process in smart home environments.
Keywords:
activity recognitionneedleman-wunsch algorithmsensor selectionsmart homestree alignment

Related Experiment Videos

  • Evaluated the proposed method's performance using two public datasets from distinct smart home settings.

    • Main Results:

    • The tree alignment method demonstrated a reduction in the cost of the sensor evaluation process.
    • The approach mitigated the risk of overfitting commonly associated with traditional wrapper methods.
    • Performance was validated across diverse smart home environments using established datasets.

    Conclusions:

    • Tree alignment offers a more efficient and robust solution for sensor selection in smart home activity recognition.
    • This method enhances the practical deployment of smart home systems for inhabitant monitoring.
    • Further research can explore the scalability and adaptability of tree alignment in complex smart home ecosystems.