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

Journal: Journal of healthcare engineering
PMID: 29359038

Abstract

Mobile robotics is a potential solution to home behavior monitoring for the elderly. For a mobile robot in the real world, there are several types of uncertainties for its perceptions, such as the ambiguity between a target object and the surrounding objects and occlusions by furniture. The problem could be more serious for a home behavior-monitoring system, which aims to accurately recognize the activity of a target person, in spite of these uncertainties. It detects irregularities and categorizes situations requiring further explorations, which strategically maximize the information needed for activity recognition while minimizing the costs. Two schemes of active sensing, based on two irregularity detections, namely, heuristic-based and template-matching-based irregularity detections, were implemented and examined for body contour-based activity recognition. Their time cost and accuracy in activity recognition were evaluated through experiments in both a controlled scenario and a home living scenario. Experiment results showed that the categorized further explorations guided the robot system to sense the target person actively. As a result, with the proposed approach, the robot system has achieved higher accuracy of activity recognition.

Authors

  • Wenwei Yu
    Graduate School of Engineering, Chiba University, Nishi-Chiba, Chiba, Japan.
  • Keigo Nakahata
    Graduate School of Engineering, Chiba University, Nishi-Chiba, Chiba, Japan.
  • Guang Hao Sun
    Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu, Japan.
  • Akio Namiki
    Graduate School of Engineering, Chiba University, Chiba, Japan.
  • Sayuri Suwa
    Graduate School of Nursing, Chiba University, Chiba, Japan.
  • Mayuko Tsujimura
    Graduate School of Nursing, Chiba University, Chiba, Japan.
  • Le Xie
    Research Institute of Med-X, Shanghai Jiao Tong University, Shanghai, China.
  • Jinwu Wang
    Research Institute of Med-X, Shanghai Jiao Tong University, Shanghai, China.
  • Shao Ying Huang
    Engineering Product Development, Singapore University of Technology and Design, Singapore.

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