Preliminary investigation on motif-guided multilevel knowledge transfer for logistic work activity recognition

• Item type: SIG Technical Reports(1)
• 公開日: 2024-02-22
• タイトル: Preliminary investigation on motif-guided multilevel knowledge transfer for logistic work activity recognition
• タイトル / 言語: en
• タイトル: Preliminary investigation on motif-guided multilevel knowledge transfer for logistic work activity recognition
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: AI for Supporting Workers
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
• 著者所属: Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
• 著者所属: Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
• 著者所属: Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
• 著者所属: Corporate Manufacturing Engineering Center, Toshiba Corporation
• 著者所属: Corporate Manufacturing Engineering Center, Toshiba Corporation
• 著者所属: Corporate Manufacturing Engineering Center, Toshiba Corporation
• 著者所属(英): en
• 著者所属(英): Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Corporate Manufacturing Engineering Center, Toshiba Corporation
• 著者所属(英): en
• 著者所属(英): Corporate Manufacturing Engineering Center, Toshiba Corporation
• 著者所属(英): en
• 著者所属(英): Corporate Manufacturing Engineering Center, Toshiba Corporation
• 著者名: Jaime, Morales; Qingxin, Xia; Naoya, Yoshimura; Takuya, Maekawa; Hirotomo, Oshima; Masamitsu, Fukuda; Yasuo, Namioka
• 著者名(英): Jaime, Morales; Qingxin, Xia; Naoya, Yoshimura; Takuya, Maekawa; Hirotomo, Oshima; Masamitsu, Fukuda; Yasuo, Namioka
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: When using human activity recognition (HAR) methods in the industrial domain two main problems exist, complex sensor data and lack of labeled data. The complexity of sensor data has been previously addressed by employing motif-guided attention-based models. However, these models provide user-specific solutions that cannot be applied to the general population. This study presents a knowledge transfer method that allows the application of such reliable methods on a previously unknown target user with minimal labeled data. To accomplish this we propose a new technique for selecting appropriate training data segments from an existing pool of known users by identifying multiple levels of similarity between the source and target users, such as work trend similarity, operation-specific motions, user unique key actions, etc. We call this method motif-guided multilevel knowledge transfer. We evaluate the ability of our framework using 3 separate logistic datasets (one real-life, two simulated).
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: When using human activity recognition (HAR) methods in the industrial domain two main problems exist, complex sensor data and lack of labeled data. The complexity of sensor data has been previously addressed by employing motif-guided attention-based models. However, these models provide user-specific solutions that cannot be applied to the general population. This study presents a knowledge transfer method that allows the application of such reliable methods on a previously unknown target user with minimal labeled data. To accomplish this we propose a new technique for selecting appropriate training data segments from an existing pool of known users by identifying multiple levels of similarity between the source and target users, such as work trend similarity, operation-specific motions, user unique key actions, etc. We call this method motif-guided multilevel knowledge transfer. We evaluate the ability of our framework using 3 separate logistic datasets (one real-life, two simulated).
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11838947
• 書誌情報: 研究報告ユビキタスコンピューティングシステム（UBI）; 巻 2024-UBI-81, 号 11, p. 1-9, 発行日 2024-02-22
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8698
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会