TY - GEN
T1 - Analysis on Falling Risk of Elderly Workers when Mowing on a Slope via Motion Capture
AU - Wu, Bo
AU - Wu, Yuan
AU - Nishimura, Shoji
AU - Jin, Qun
N1 - Funding Information:
We thank all the workers who participated in the experiments. This work was supported by JSPS KAKENHI Grant Number 21K11876.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Due to the aging of rural population, in Japan's terraced field areas, many elderly people have to do the mowing works of paddy levees manually. However, it takes the high risk of falling when mowing on slops, and many accidents are caused by the unstable postures. The analysis of their body motion becomes necessary. Based on our previous study, the personal factors could be important on affecting the mowing behaviors. Therefore, in this paper, we focus more on the mowing workers' personal factors (such as age and physique) and try to analyze the effect on their falling risk via a high precision motion capture device. A set of experiments was conducted in September 2020, and four new mowing workers over 60 years old were invited to participate in the experiments. Their joints angles data and related personal factor data were used to analyze the factors that have the greatest influence on body stability during slop mowing. The results of stepwise regression showed that the mowing workers' age and physique have significant effects on the stability of their body motion. The results of this study can clarify the factors that prevent accidents in the fall detection systems and offer useful insights for the training of new mowing workers.
AB - Due to the aging of rural population, in Japan's terraced field areas, many elderly people have to do the mowing works of paddy levees manually. However, it takes the high risk of falling when mowing on slops, and many accidents are caused by the unstable postures. The analysis of their body motion becomes necessary. Based on our previous study, the personal factors could be important on affecting the mowing behaviors. Therefore, in this paper, we focus more on the mowing workers' personal factors (such as age and physique) and try to analyze the effect on their falling risk via a high precision motion capture device. A set of experiments was conducted in September 2020, and four new mowing workers over 60 years old were invited to participate in the experiments. Their joints angles data and related personal factor data were used to analyze the factors that have the greatest influence on body stability during slop mowing. The results of stepwise regression showed that the mowing workers' age and physique have significant effects on the stability of their body motion. The results of this study can clarify the factors that prevent accidents in the fall detection systems and offer useful insights for the training of new mowing workers.
KW - elderly support
KW - fall detection
KW - joints angles analysis
KW - motion capture
KW - mowing behaviors
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U2 - 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00148
DO - 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00148
M3 - Conference contribution
AN - SCOPUS:85127541528
T3 - Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
SP - 890
EP - 895
BT - Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
Y2 - 25 October 2021 through 28 October 2021
ER -