Development of a measurement and evaluation system for bed-making activity for self-training

  • Authors:

  • Ayanori Nagata;Zhifeng Huang;Masako Kanai-Pak;Jukai Maeda;Yasuko Kitajima;Mitsuhiro Nakamura;Kyoko Aida;Noriaki Kuwahara;Taiki Ogata;Jun Ota

  • Affiliations:

  • Research into Artifacts Center for Engineering (RACE), The University of Tokyo, Kashiwa-shi, Chiba, Japan;Research into Artifacts Center for Engineering (RACE), The University of Tokyo, Kashiwa-shi, Chiba, Japan;Faculty of Nursing, Tokyo Ariake University of Medical and Health Science, Koto-ku, Tokyo, Japan;Faculty of Nursing, Tokyo Ariake University of Medical and Health Science, Koto-ku, Tokyo, Japan;Faculty of Nursing, Tokyo Ariake University of Medical and Health Science, Koto-ku, Tokyo, Japan;Faculty of Nursing, Tokyo Ariake University of Medical and Health Science, Koto-ku, Tokyo, Japan;Faculty of Nursing, Tokyo Ariake University of Medical and Health Science, Koto-ku, Tokyo, Japan;Department of Advanced Fibro-Science, Kyoto Institute of Technology, Sakyo-ku, Kyoto, Japan;Research into Artifacts Center for Engineering (RACE), The University of Tokyo, Kashiwa-shi, Chiba, Japan;Research into Artifacts Center for Engineering (RACE), The University of Tokyo, Kashiwa-shi, Chiba, Japan

  • Venue:
  • DHM'13 Proceedings of the 4th International conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management: healthcare and safety of the environment and transport - Volume Part I
  • Year:
  • 2013

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Abstract

This study proposes a method to automatically measure multiple objects by image processing for constructing a system for nursing trainees of self-training in the skill of bed making. In a previous study, we constructed a system to measure and evaluate trainee performance using three RGB-D (RGB color and depth) sensors. Our previous system had a problem with recognition of equipment such as the bed pad and the sheet because of color change by the light condition, the automatic color correction by the sensors and color variability in one object. In this paper, we used color reduction and cluster selection for equipment recognition. The system reduced the color in images by using k-means clustering and recognized the clusters as separate objects by predetermined thresholds. Compared with the previous method, the recognition accuracy was higher and the accuracy achieved was 70%.