A study on the reduction of mowing work burden for maintaining landscapes in rural areas: Experiment design for mowing behaviors analyze

Bo Wu, Yuan Wu, Yoko Aoki, Shoji Nishimura, Masayuki Kashiwagi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Although the researches on automatic mowing has made some achievements, most of the mowing work is done by hand work. Incorrect postures while mowing can put a heavy labor burden and may lead to slip and fall and sometimes brings a result of death especially in slope areas. In this paper, we focus on the mowing work on the slope areas and designed an experiment which can capture subjects' behaviors data with motion capture and eye tracking technology. The experiment system can combine the data which collected from mowing experts, and finally a highly accurate composite behavioral model for mowing will be proposed, which can be used to training the mowing works who doesn't have much experience. The finding may helpful to promote safe and efficient mowing behaviors and benefit to many other areas such as labor education, elderly healthcare care and environmental conservation.

Original languageEnglish
Title of host publicationProceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages533-536
Number of pages4
ISBN (Electronic)9781728130248
DOIs
Publication statusPublished - 2019 Aug
Event17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 - Fukuoka, Japan
Duration: 2019 Aug 52019 Aug 8

Publication series

NameProceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

Conference

Conference17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
CountryJapan
CityFukuoka
Period19/8/519/8/8

Keywords

  • Elderly support
  • Eye-tracking
  • Motion capture
  • Mowing analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Information Systems
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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  • Cite this

    Wu, B., Wu, Y., Aoki, Y., Nishimura, S., & Kashiwagi, M. (2019). A study on the reduction of mowing work burden for maintaining landscapes in rural areas: Experiment design for mowing behaviors analyze. In Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 (pp. 533-536). [8890353] (Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00106