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Indoor Infrared Sensor Layout Optimization for Elderly Monitoring Based on Fused Genetic Gray Wolf Optimization

Shuwang Chen1, Yajiang Chen1, Meng Feng2

  • 1School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

A new Fusion Genetic Gray Wolf Optimization (FGGWO) algorithm enhances elderly monitoring systems. This method optimizes sensor layout for improved accuracy, efficiency, and coverage, reducing sensor numbers while maintaining high performance.

Keywords:
elderly monitoringgenetic algorithmgray wolf optimization algorithmmonitoring system optimizationsensor layout

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Area of Science:

  • * Computer Science
  • * Artificial Intelligence
  • * Biomedical Engineering

Background:

  • * The global population is aging, increasing the need for efficient and accurate elderly monitoring systems.
  • * Current systems face challenges in sensor layout optimization, impacting coverage and accuracy.
  • * Optimizing sensor placement is critical for effective elderly care technology.

Purpose of the Study:

  • * To propose a novel sensor layout optimization method for elderly monitoring systems.
  • * To enhance the efficiency, accuracy, and coverage of indoor infrared sensor networks.
  • * To address the limitations of traditional optimization algorithms in complex surveillance scenarios.

Main Methods:

  • * Development of the Fusion Genetic Gray Wolf Optimization (FGGWO) algorithm.
  • * Integration of Genetic Algorithm (GA) for global search and Gray Wolf Optimization (GWO) for local search.
  • * Optimization of indoor infrared sensor layout within an elderly monitoring system framework.

Main Results:

  • * The FGGWO algorithm demonstrated superior performance compared to single optimization algorithms in monitoring coverage, accuracy, and system efficiency.
  • * The proposed method effectively avoided the local optimum problem inherent in traditional approaches.
  • * Reduced sensor count was achieved without compromising high monitoring accuracy.

Conclusions:

  • * The FGGWO algorithm offers a robust and efficient solution for elderly monitoring system sensor layout optimization.
  • * The algorithm's flexibility and adaptability suggest potential applications in broader intelligent surveillance contexts.
  • * Future work will focus on integrating deep learning for enhanced real-time adaptive capabilities.