筋骨格モデリングシミュレータと姿勢推定AI を組み合わせた筋活動量の推定

• Item type: SIG Technical Reports(1)
• 公開日: 2022-06-09
• タイトル: 筋骨格モデリングシミュレータと姿勢推定AI を組み合わせた筋活動量の推定
• タイトル / 言語: en
• タイトル: Muscle activity estimation by combining a musculoskeletal modeling simulator and AI based pose estimation Estimation of muscle activity
• 言語: jpn
• キーワード / 主題Scheme: Other
• キーワード / 主題: 身体運動・計測
• 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: 兵庫県立工業技術センター
• 著者所属: 兵庫県立工業技術センター
• 著者所属: 兵庫県立工業技術センター
• 著者所属: 兵庫県立工業技術センター
• 著者所属(英): en
• 著者所属(英): Hyogo Prefectural Institute of Technology
• 著者所属(英): en
• 著者所属(英): Hyogo Prefectural Institute of Technology
• 著者所属(英): en
• 著者所属(英): Hyogo Prefectural Institute of Technology
• 著者所属(英): en
• 著者所属(英): Hyogo Prefectural Institute of Technology
• 著者名: 福田, 純; 福井, 航; 平田, 一郎; 後藤, 泰徳
• 著者名(英): Atsushi, Fukuda; Wataru, Fukui; Ichiro, Hirata; Yasunori, Goto
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: One concrete example of human-centered design (HCD) is design that takes into account ergonomic influences using musculoskeletal models. Methods using motion capture are often used to calculate muscle loading on musculoskeletal models, but these methods cannot be tested in an arbitrary field. Therefore, we thought that if AI-based pose estimation technique adequately combined with a musculoskeletal simulator, this method could be a powerful tool in performing human-centered design. In this study, the results of AI-based pose estimation were used as input to a musculoskeletal simulator to estimate muscle activity. When limited to movements on a plane, the 2D pose estimation confirmed the output as intended. On the other hand, for 3D pose estimation from multiple cameras, keypoint accuracy was insufficient and correct calculation results could not be obtained.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: One concrete example of human-centered design (HCD) is design that takes into account ergonomic influences using musculoskeletal models. Methods using motion capture are often used to calculate muscle loading on musculoskeletal models, but these methods cannot be tested in an arbitrary field. Therefore, we thought that if AI-based pose estimation technique adequately combined with a musculoskeletal simulator, this method could be a powerful tool in performing human-centered design. In this study, the results of AI-based pose estimation were used as input to a musculoskeletal simulator to estimate muscle activity. When limited to movements on a plane, the 2D pose estimation confirmed the output as intended. On the other hand, for 3D pose estimation from multiple cameras, keypoint accuracy was insufficient and correct calculation results could not be obtained.
• 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: AA12049625
• 書誌情報: 研究報告エンタテインメントコンピューティング（EC）; 巻 2022-EC-64, 号 1, p. 1-2, 発行日 2022-06-09
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8914
• 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