Development and evaluation of work support system by AR using HMD

  • Kohjiro Hashimoto  
  • bTadashi Miyosawa  
  • cMai Higuchi  
  • a,b,c Suwa University of Science
Cite as
K. Hashimoto, T. Miyosawa, M. Higuchi (2018). Development and evaluation of work support system by AR using HMD. Proceedings of the 4th International Conference of The Virtual And Augmented Reality In Education (VARE 2018), pp. 28-33. DOI: https://doi.org/10.46354/i3m.2018.vare.005

Abstract

"The serious concerns of rapid aging and very low birthrate are faced in Japan. The aging increases the
number of retired employee with work skill, and the declining birthrate decreases the number of employee.
Therefore, securing and fostering human resources are social issues in Japan. In particular, the work support
system is desired for several companies in rural areas. This is because that they can not secure human
resources. By the way, AR technique can add the virtual information to reality space. Therefore, it is possible to
show efficient information. Currently, AR technique has been used to education system. In this paper, AR technique is applied to work support system for laptop computer repair. In this system, the parts of laptop computer are recognized by image recognition technique and the repair procedures are presented to user through HMD. In the experiment, the sensibility evaluation was performed. According to these results, the usefulness of the developed work support system was confirmed

References

  1. Hayato Taki, Tetsuya Tsubokura, Takehiro Urano, 2015. Field Work Suuport System Using AR For Plant
    Operation, Forum on Information Technology in Japan.
  2. Takahiro Tohyama, Takahiro Totani, Toshiaki Miyao, Takehiro Kojima, Fumiya Kinoshita, Tatsuya Yamakawa, Ryota Kimura, Masaru Miyao. 2016. Effect for work efficiency by AR using seethrough smart glasses, IPSJ SIG Technical Report in Japan. No.46, pp.1-4.
  3. Kaijing Shi, Hong Bao, Nan Ma, 2017. Forward Vehicle Detection Based on Incremental Learning and Fast R-CNN, 13th International Conference on Computational Intelligence and Security.
  4. Kaijing Shi, Hong Bao, Nan Ma, 2017. Forward Vehicle Detection Based on Incremental Learning and Fast R-CNN, 13th International Conference on Computational Intelligence and Security.
  5. Kaijing Shi, Hong Bao, Nan Ma, 2017. Forward Vehicle Detection Based on Incremental Learning and Fast R-CNN, 13th International Conference on Computational Intelligence and Security.
  6. Kae Doki, Yuki Funabora, Shinji Doki, Akihiro Torii, 2016. Extraction of human action elements with transition network of partial time series data modeled by Hidden Markov Model, Proceedings of the 42th Annual Conference of the IEEE Industrial Electronics Society, pp.912-917.
  7. Wataru Takano, Yoshihiko Nakamura, 2011. Realtime Unsupervised Self tuning Segmentation of Behavioral Motion Patterns Based on Probabilistic Correlation and Its Application to Automatic Acquisition of Proto-Symbols, Journal of the Robotics Society of Japan, Vol.35, No.1, pp.47-54.
  8. Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, 2017. Grad-CAM:Visual Explanations from Deep Networks via Gradient-Based Localization, IEEE International Conference on Computer Vision, pp.618-626.