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ウ ボ
武 博 所属 コンピュータサイエンス学部 コンピュータサイエンス学科 職種 専任講師 |
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| 言語種別 | 英語 |
| 発行・発表の年月 | 2025/11 |
| 形態種別 | 学術論文 |
| 査読 | 査読あり |
| 標題 | A framework of imitative behavior analysis for animal exercise courses via human pose estimation |
| 執筆形態 | 共著 |
| 掲載誌名 | Scientific Reports |
| 掲載区分 | 国外 |
| 出版社・発行元 | Nature |
| 巻・号・頁 | 15(40182) |
| 担当区分 | 最終著者,責任著者 |
| 著者・共著者 | Yu Qi, Chongyang Zhang, Siyu Xiong & Bo Wu |
| 概要 | In contemporary performance education, the Animal Exercise course is one of the core training modules for developing imitative behavior. Typically, instructors facilitate this process through guided demonstrations and task-based instruction, encouraging students to engage in both imitation and creative exploration. The pedagogical approach is therefore characterized by active student participation and a strong emphasis on experiential, practice-oriented learning. However, assessment in Animal Exercise courses still relies primarily on instructors’ subjective judgment, resulting in inconsistent and non-standardized evaluations. This hinders students’ ability to identify skill deficiencies and improve their course performance. To address this challenge, we propose a quantitative framework for evaluating imitative behavior using pose estimation, termed Human Pose Estimation–Imitative Behavior Analysis (HPE-IBA). Using this framework, we employ a standard RGB camera to collect motion data from both students and gorillas, extract three-dimensional joint coordinates, and compute dynamic joint angles with MediaPipe. We then apply correlation analysis to identify weakly correlated features and core joints, followed by two-way ANOVA to examine the effects of training status and gender on students’ imitation performance. Analysis of chest-beating and walking imitation reveals a statistically significant interaction between training status and gender (p< 0.01), primarily reflected in joint patterns such as the right elbow and right knee. The proposed framework not only enhances the application of pose estimation in acting education but also provides a foundation for broader applications in performance-based motion analysis. |
| 外部リンクURL | https://www.nature.com/articles/s41598-025-23829-8 |