@techreport{oai:ipsj.ixsq.nii.ac.jp:00220077,
 author = {西條, 涼平 and 宮下, 広夢 and 松尾, 翔平 and Ryohei, Saijo and Hiromu, Miyashita and Shohei, Matsuo},
 issue = {3},
 month = {Sep},
 note = {熟練度の異なる複数人での共同作業においては，作業者同士の作業に対する熟練度の差に起因するコミュニケーション齟齬が生じる可能性がある．こうしたコミュニケーション齟齬は作業の効率や質の低下につながる．本研究では，作業者の熟練度に応じた補足情報を作業中のユーザに対し提示することで，コミュニケーション齟齬の解消を目指す．本稿では，はじめに熟練度の推定手法について検討を行い，VR 空間で行う詰将棋課題を題材に作業中の視線データ収集を行った．その結果，課題の回答開始から 3 秒間の視線データを切り出し，SVM により学習したモデルで熟練者と非熟練者を識別できる可能性が示唆された．これにより，コミュニケーション齟齬を解消・低減する情報提示に向けた熟練度推定技術の確立を前進させた．, In collaborative work among multiple workers with different skill levels, communication discrepancies may occur due to differences in the skill level of the workers. Such discrepancies in communication lead to a deterioration in the efficiency and quality of work. This study aims to eliminate such discrepancies by presenting supplementary information to users during work according to their skill level. This paper first studied a method for estimating skill level based on collected gaze data during work on the Tsume-Shogi (Japanese chess) task performed in a VR space. The results suggest that it is possible to discriminate skilled from unskilled players using a model learned by SVM by extracting gaze data during the first 3 seconds of response to the task. It advanced the development of the skill level estimation method for assisting collaborative work.},
 title = {ユーザに応じた共同作業支援に向けた視線情報に基づく熟練度推定手法の基礎検討},
 year = {2022}
}