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Human Occupancy Monitoring and Positioning with Speed-Responsive Adaptive Sliding Window Using an Infrared Thermal

Yukai Lin1, Qiangfu Zhao1

  • 1Department of Computer and Information Systems, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima, Japan.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an infrared thermal sensor system for accurate human occupancy monitoring. It overcomes privacy and environmental challenges, offering a stable solution for smart homes and elderly care.

Keywords:
adaptive thresholdindoor positioninginfrared thermal array sensoroccupancy monitoring

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Area of Science:

  • Engineering
  • Computer Science
  • Sensor Technology

Background:

  • Human occupancy monitoring is crucial for IoT applications like smart homes and public safety.
  • Traditional methods using cameras face limitations including privacy concerns, environmental factors, and cost.
  • Infrared thermal sensors offer a privacy-preserving alternative, effective in various lighting conditions and with low power consumption.

Purpose of the Study:

  • To develop and evaluate a novel human occupancy monitoring system utilizing infrared thermal sensing.
  • To address the limitations of existing technologies, particularly concerning privacy and environmental adaptability.
  • To enhance the stability and accuracy of human position estimation in dynamic environments.

Main Methods:

  • Utilized the AMG8833, an 8x8 Infrared Thermal Array Sensor for data acquisition.
  • Implemented data processing techniques including interpolation, adaptive thresholding, and blob detection for signature separation.
  • Developed a dynamic sliding window algorithm for stable human position estimation, adapting to movement speed.

Main Results:

  • Successfully processed infrared thermal data to detect and separate human heat signatures.
  • Demonstrated enhanced stability in human position estimation through the dynamic sliding window approach.
  • Validated the system's effectiveness in handling environmental changes and uncertainties for reliable monitoring.

Conclusions:

  • Infrared thermal sensing presents a viable and privacy-conscious solution for human occupancy monitoring.
  • The proposed system effectively overcomes limitations of traditional methods, offering robust performance.
  • The dynamic sliding window significantly improves the reliability of position estimation in diverse scenarios, suitable for security and care applications.