Behavioral Analysis of Mowing Workers Based on Hilbert–Huang Transform: An Auxiliary Movement Analysis of Manual Mowing on the Slopes of Terraced Rice Fields

Bo Wu, Yuan Wu, Ran Dong (), Kiminori Sato, Soichiro Ikuno, Shoji Nishimura and Qun Jin
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Bo Wu: School of Computer Science, Tokyo University of Technology, 1404-1 Katakuramachi, Hachioji, Tokyo 192-0982, Japan
Yuan Wu: Advanced Research Center for Human Sciences, Waseda University, 2-579-15 Mikajima, Saitama Prefecture, Tokorozawa 359-1192, Japan
Ran Dong: School of Computer Science, Tokyo University of Technology, 1404-1 Katakuramachi, Hachioji, Tokyo 192-0982, Japan
Kiminori Sato: School of Computer Science, Tokyo University of Technology, 1404-1 Katakuramachi, Hachioji, Tokyo 192-0982, Japan
Soichiro Ikuno: School of Computer Science, Tokyo University of Technology, 1404-1 Katakuramachi, Hachioji, Tokyo 192-0982, Japan
Shoji Nishimura: Advanced Research Center for Human Sciences, Waseda University, 2-579-15 Mikajima, Saitama Prefecture, Tokorozawa 359-1192, Japan
Qun Jin: Advanced Research Center for Human Sciences, Waseda University, 2-579-15 Mikajima, Saitama Prefecture, Tokorozawa 359-1192, Japan

Agriculture, 2023, vol. 13, issue 2, 1-21

Abstract: In the mountainous areas of Japan, the weeds on the slopes of terraced rice paddies still need to be cut by the elderly manually. Therefore, more attention should be given to maintain proper postures while performing mowing actions (especially the pre-cutting actions) to reduce the risk of accidents. Given that complex mowing actions can be decomposed into different sub-actions, we proposed a joint angular calculation-based body movement analysis model based on the Hilbert–Huang transform to analyze the pre-cutting actions. We found that the two most important sub-actions were fast pre-cutting and slow pre-cutting. Based on field experiments, we analyzed the pre-cutting actions of workers with different experience levels and identified the factors that affected their falling risk (stability). The results showed differences and similarities in the actions’ frequency and amplitude in the sub-actions of workers with different mowing experience, confirmed the influence of body characteristics (body height, etc.) on body stability, and showed that workers should pay attention to their age and ankle part while mowing. The analysis results have identified factors for the mowing workers’ training and the development of equipment for use in complicated geographical conditions.

Keywords: agricultural work support; Hilbert–Huang transform method; body motion measurement; human behaviors analysis; pre-cutting analysis; mowing on slopes; empirical mode decomposition; visualization of agricultural experience (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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