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ADA-SR: Activity detection and analysis using security robots for reliable workplace safety.

Guangnan Zhang1, Wang Jing1, Hai Tao1,2

  • 1School of Computer Science, Baoji University of Arts and Sciences, Baoji, China.

Work (Reading, Mass.)
|February 22, 2021
PubMed
Summary

This study introduces activity detection and analysis (ADA) using security robots and convolution neural networks for enhanced workplace safety. The method improves event detection accuracy by analyzing image and sensor data for human activities like standing, walking, and running.

Keywords:
HRIMonitoringconvolution neural networksevent detectionsensor data processing

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Human-Robot Interaction (HRI) enhances real-time service provisioning and assisted daily activities.
  • Robotic systems in security services improve event detection precision and environmental monitoring.

Purpose of the Study:

  • To discuss activity detection and analysis (ADA) using security robots in workplaces.
  • To improve the accuracy of event and activity detection by mitigating deviations in convolution processes.

Main Methods:

  • Processing image and sensor data for event and activity detection.
  • Classifying detected events for abnormality using sensor and image data.
  • Utilizing a convolution neural network for data analysis and deviation mitigation.

Main Results:

  • The proposed method verifies performance for detecting human activities: standing, walking, and running.
  • Differences in data are identified through independent data correlation and information processing.

Conclusions:

  • Results are compared with existing methods based on accuracy, classification time, and recall.
  • The study demonstrates an improved approach to activity detection and analysis in security robotics.