久保 光徳

Abstract This paper explores the application of computer vision and mathematical modeling to analyze the intricate movements involved in weaving a traditional farming tool, the winnowing basket. By utilizing OpenPose algorithms, the study simplifies and visualizes the craftsmen's motions, particularly focusing on wrist movements. Video data of craftsmen in Chiba, Japan, creating Kizumi (place name) winnowing baskets is used as the basis for analysis. The extracted information is used to generate 2D motion trajectories of the wrist, allowing a comparison between beginners who watched parsed videos and those who watched the original videos in terms of skill acquisition and learning time. By visualizing human body behavior and combining statistical results, this study demonstrates the potential of artificial intelligence techniques such as computer vision for observing repetitive human movement and inheriting traditional skills.

本研究では異なる大きさの面岸箕を対象に、それを用いて大豆、アマランサスの風選を行う際の動作のデータをモーションキャプチャにより取得し、その動作の①スティックピクチャの観察、ならびに②高さ方向(Z軸方向)のマーカ変位による解析を試みた。その結果、動作の特徴の明確化がなされるとともに、箕の大きさや内容物によって、使い方に変化がみられる可能性が示唆された。