TY - GEN
T1 - A study on the reduction of mowing work burden for maintaining landscapes in rural areas
T2 - 17th 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
AU - Wu, Bo
AU - Wu, Yuan
AU - Aoki, Yoko
AU - Nishimura, Shoji
AU - Kashiwagi, Masayuki
N1 - Funding Information:
This work was supported by 2019 Waseda University, Advanced Research Center for Human Sciences, Young Group Research Project “C Pro”.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - 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.
AB - 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.
KW - Elderly support
KW - Eye-tracking
KW - Motion capture
KW - Mowing analysis
UR - http://www.scopus.com/inward/record.url?scp=85075188126&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075188126&partnerID=8YFLogxK
U2 - 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00106
DO - 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00106
M3 - Conference contribution
AN - SCOPUS:85075188126
T3 - 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
SP - 533
EP - 536
BT - 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
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 August 2019 through 8 August 2019
ER -