@article{oai:ipsj.ixsq.nii.ac.jp:00240014,
 author = {大島, 宏友 and 福田, 雅允 and 前川, 卓也 and 浪岡, 保男 and Hirotomo, Oshima and Masamitsu, Fukuda and Takuya, Maekawa and Yasuo, Namioka},
 issue = {10},
 journal = {情報処理学会論文誌},
 month = {Oct},
 note = {製造現場では，生産性の向上を目的に継続的な作業進捗把握を行っており，それらに必要な各種データを自動で取得する取組みが進められている．一方で，セル生産の組立工程のような人間の作業に依存している現場では，作業内容が複雑かつ多様であるため，作業内容を自動で推定することが難しい．本論文では，作業者，作業対象，およびそれらの作用点情報を統合的にモデル化し，組立工程の要素作業を自動推定する手法を新たに提案する．モデル化には，GNN（Graph Neural Network）を用いており，実際の組立工程で取得した作業データを用いて本手法を評価し，提案手法の有効性を確認した．, In a manufacturing, because understanding of work progress is crucial in productivity improvement, efforts are being made to automatically collect work-related sensor data. However, in an assembly process which is mainly done by workers, it is difficult to estimate work activities automatically because of complexity and variety of them. We thus propose a new method which estimates elemental works of assembly process automatically by integrating information about workers, a product of interest, and processing parts. We apply GNN for the modeling and the effectiveness of this method was investigated with actual data which are collected at an assembly process.},
 pages = {1501--1510},
 title = {作業者，作業対象，およびその作用点情報を統合的に用いた作業行動認識手法},
 volume = {65},
 year = {2024}
}