IMUセンサーを用いたパンチ検出と分類手法の提案

• Item type: Symposium(1)
• 公開日: 2021-06-23
• タイトル: IMUセンサーを用いたパンチ検出と分類手法の提案
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Internet of Things
• 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: 青山学院大学
• 著者所属: 青山学院大学
• 著者所属: 青山学院大学
• 著者名: 花田, 祥典; 横窪, 安奈; ロペズ, ギヨーム
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Maintaining healthy living requires habitual physical activities. Nonetheless, staying motivated to work out regularly is challenging for most people. To solve this problem, automated personal supporting systems could help. This paper presents boxercise, a fitness standard exercise that mainly includes shadow-boxing exercises. The paper introduces punch activity detection and classification methods using acceleration and angular velocity signals recorded using a single smartwatch on the participant’s rear hand wrist. The proposed method is evaluated on our 10 participants aged between 17 and 53 years old (8 male and 2 female, age 27.8±12.8). As a result, we achieved 98.8% detection accuracy, 98.9% classification accuracy with SVM in-person-dependent (PD) case, and 91.1% classification accuracy with SVM in person-independent (PI) case. In addition, we estimated the real-time performance of each classification method and found out all our methods could classify a single punch in less than 0.1 seconds. The paper also discussed some points of improvement towards a practical boxercise supporting system.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Maintaining healthy living requires habitual physical activities. Nonetheless, staying motivated to work out regularly is challenging for most people. To solve this problem, automated personal supporting systems could help. This paper presents boxercise, a fitness standard exercise that mainly includes shadow-boxing exercises. The paper introduces punch activity detection and classification methods using acceleration and angular velocity signals recorded using a single smartwatch on the participant’s rear hand wrist. The proposed method is evaluated on our 10 participants aged between 17 and 53 years old (8 male and 2 female, age 27.8±12.8). As a result, we achieved 98.8% detection accuracy, 98.9% classification accuracy with SVM in-person-dependent (PD) case, and 91.1% classification accuracy with SVM in person-independent (PI) case. In addition, we estimated the real-time performance of each classification method and found out all our methods could classify a single punch in less than 0.1 seconds. The paper also discussed some points of improvement towards a practical boxercise supporting system.
• 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2021論文集; 巻 2021, 号 1, p. 1304-1309, 発行日 2021-06-23
• 出版者 / 言語: ja
• 出版者: 情報処理学会