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Home> Published Issues> 2018> Volume 5, No. 3, September 2018

Grouping Method Using Graph Theory for Agricultural Workers Engaging in Manual Tasks

Shinji Kawakura 1 and Ryosuke Shibasaki 2
1. Department of Information Technology and Human Factors, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
2. Center for Spatial Information Science, The University of Tokyo, Meguro, Japan

Abstract—Agricultural directors and managers might employ several grouping methodologies of physical working members to enhance the quality of work and collaborations. In this prospective study, the authors aim to execute and demonstrate the results of a grouping analysis based on the spanning tree method in the field of graph theory. The participants—six experienced agricultural manual workers and six novices—were asked to crop middle-sized root vegetables by hand in a sitting position. The authors handled the data and executed the analyses in VBA. The authors also used Scilab libraries and packages to execute timeline wavelet analyses with accumulated acceleration data obtained from the outdoor agricultural workers’ dominant lower arms and the left side of their waists. The authors present qualitative data of the workers’ groupings using standard deviations (SD) of the vertical acceleration data. The main index values were SD values of the acceleration data in the dominant frequency zone. After performing the dotting process, the authors successively connected pairs of dots to separate them into two groups. The authors developed several charts and compared them with those from the common and validated analysis of principal components method. These methods could be used to improve the grouping and categorizing for agri-business and related research.
 
Index Terms—graph theory, grouping method, spanning tree, timeline acceleration data, wavelet analysis

Cite: Shinji Kawakura and Ryosuke Shibasaki, "Grouping Method Using Graph Theory for Agricultural Workers Engaging in Manual Tasks," Journal of Advanced Agricultural Technologies, Vol. 5, No. 3, pp. 173-181, September 2018. Doi: 10.18178/joaat.5.3.173-181

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