Preliminary Investigation of Unsupervised Factory Activity Recognition with Wearable Sensors via Temporal Structure of Multiple Motifs

• Item type: Symposium(1)
• 公開日: 2020-06-17
• タイトル: Preliminary Investigation of Unsupervised Factory Activity Recognition with Wearable Sensors via Temporal Structure of Multiple Motifs
• タイトル / 言語: en
• タイトル: Preliminary Investigation of Unsupervised Factory Activity Recognition with Wearable Sensors via Temporal Structure of Multiple Motifs
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 行動認識
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Corporate Manufacturing Engineering Center, Toshiba Corporation
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): 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): Graduate School of Information Science and Technology, Osaka University
• 著者名: Qingxin, Xia; Korpela, Joseph; Namioka, Yasuo; Maekawa, Takuya
• 著者名(英): Qingxin, Xia; Korpela, Joseph; Namioka, Yasuo; Maekawa, Takuya
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In this study, we propose a robust unsupervised learning technique that makes use of two types of sensor data motifs to track the starting time of each operation in every iteration of work periods. A period motif only occurs once in each work period that is used to roughly detect the duration of work periods. An action motif occurs many times in each work period, corresponding to some basic actions in the period. A temporal structure is then constructed based on the temporal distances among motifs in the first period, which is used to improve motif tracking in the following periods as well as roughly detect the location of outliers. We run particle filters to track the starting time of operations and select a best particle series based on the extracted motifs. We evaluate the proposed method using sensor data collected from workers in actual factories and achieved state-of-the-art performance.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In this study, we propose a robust unsupervised learning technique that makes use of two types of sensor data motifs to track the starting time of each operation in every iteration of work periods. A period motif only occurs once in each work period that is used to roughly detect the duration of work periods. An action motif occurs many times in each work period, corresponding to some basic actions in the period. A temporal structure is then constructed based on the temporal distances among motifs in the first period, which is used to improve motif tracking in the following periods as well as roughly detect the location of outliers. We run particle filters to track the starting time of operations and select a best particle series based on the extracted motifs. We evaluate the proposed method using sensor data collected from workers in actual factories and achieved state-of-the-art performance.
• 書誌情報: マルチメディア，分散協調とモバイルシンポジウム2076論文集; 巻 2020, p. 374-381, 発行日 2020-06-17
• 出版者 / 言語: ja
• 出版者: 情報処理学会