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Efficient Active Sensing with Categorized Further Explorations for a Home Behavior-Monitoring Robot.

Wenwei Yu1, Keigo Nakahata2, Guang Hao Sun3

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Summary
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Mobile robots can monitor elderly home behavior despite perception uncertainties. Active sensing strategies improve activity recognition accuracy by strategically exploring detected irregularities.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Gerontology

Background:

  • Mobile robots offer potential for elderly home behavior monitoring.
  • Perception uncertainties, including object ambiguity and occlusions, challenge robot navigation and activity recognition in real-world environments.
  • Accurate activity recognition is crucial for home behavior monitoring systems to detect irregularities and ensure elderly safety.

Purpose of the Study:

  • To develop and evaluate active sensing schemes for mobile robots to enhance elderly home behavior monitoring.
  • To improve the accuracy of activity recognition in the presence of perception uncertainties.
  • To strategically maximize information acquisition for activity recognition while minimizing computational costs.

Main Methods:

  • Implementation of two active sensing schemes: heuristic-based and template-matching-based irregularity detection.
  • Body contour-based activity recognition utilizing categorized further explorations.
  • Evaluation of time cost and accuracy through experiments in controlled and home living scenarios.

Main Results:

  • Active sensing schemes guided the robot system to actively sense the target person.
  • The proposed approach demonstrated higher accuracy in activity recognition compared to baseline methods.
  • Experiments validated the effectiveness of categorized explorations in managing perception uncertainties.

Conclusions:

  • Active sensing strategies significantly enhance the accuracy of mobile robot-based elderly behavior monitoring.
  • The integration of irregularity detection and active exploration is effective in overcoming perception challenges.
  • This approach provides a robust framework for reliable elderly activity recognition in home environments.