Maneuver and Turn Classification in Wheelchair Basketball Using Inertial Sensors

• Item type: Journal(1)
• 公開日: 2021-01-15
• タイトル: Maneuver and Turn Classification in Wheelchair Basketball Using Inertial Sensors
• タイトル / 言語: en
• タイトル: Maneuver and Turn Classification in Wheelchair Basketball Using Inertial Sensors
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：5G時代の社会を創るモバイル・高度交通システム] wheelchair basketball, sports data analysis, activity recognition
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 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: Otemon Gakuin University
• 著者所属: Otemon Gakuin University
• 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): Otemon Gakuin University
• 著者所属(英): en
• 著者所属(英): Otemon Gakuin University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者名: Ryosuke, Hasegawa; Akira, Uchiyama; Takuya, Magome; Juri, Tatsumi; Teruo, Higashino
• 著者名(英): Ryosuke, Hasegawa; Akira, Uchiyama; Takuya, Magome; Juri, Tatsumi; Teruo, Higashino
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In wheelchair basketball (WB), players are constantly trying to improve their wheelchair maneuvering techniques since these are the most basic and important actions in all situations. However, assessing maneuvering quality is difficult due to the lack of quantitative metrics. In this paper, we propose two classification methods for maneuvering actions and turns by focusing on the specific wheelchair movement. For this purpose, inertial sensors are fixed to the left and right wheels of the wheelchair. In maneuver classification, the occurrence of maneuvers is detected using the angular velocity. Major maneuver activities in WB are classified into 2 types: PUSH and PULL. First, our method segments candidates of maneuver periods by the local maximum/minimum of the angular velocity since the rotation of the wheel generated by maneuvering that leads to sharp changes in the angular velocity. We then classify maneuvering actions based on thresholds. As for the turn classification, we first detect turns by calculating the amount of wheelchair rotation from the angular velocities of both wheels. We then classify the detected turns into PIVOT and TURN by using thresholds based on the typical movement of both wheels during each turn. To evaluate the performance of the proposed maneuver classification method, we collected real data from 6 players. From the result, we confirmed our method achieves an average recall and precision of 91.9% and 84.6% for maneuver classification, respectively. The results also show that our turn classification achieves an average recall and precision of 99.7% and 99.7%, respectively. Furthermore, we confirmed the effectiveness of the classification results for the assessment of maneuver quality.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.70; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In wheelchair basketball (WB), players are constantly trying to improve their wheelchair maneuvering techniques since these are the most basic and important actions in all situations. However, assessing maneuvering quality is difficult due to the lack of quantitative metrics. In this paper, we propose two classification methods for maneuvering actions and turns by focusing on the specific wheelchair movement. For this purpose, inertial sensors are fixed to the left and right wheels of the wheelchair. In maneuver classification, the occurrence of maneuvers is detected using the angular velocity. Major maneuver activities in WB are classified into 2 types: PUSH and PULL. First, our method segments candidates of maneuver periods by the local maximum/minimum of the angular velocity since the rotation of the wheel generated by maneuvering that leads to sharp changes in the angular velocity. We then classify maneuvering actions based on thresholds. As for the turn classification, we first detect turns by calculating the amount of wheelchair rotation from the angular velocities of both wheels. We then classify the detected turns into PIVOT and TURN by using thresholds based on the typical movement of both wheels during each turn. To evaluate the performance of the proposed maneuver classification method, we collected real data from 6 players. From the result, we confirmed our method achieves an average recall and precision of 91.9% and 84.6% for maneuver classification, respectively. The results also show that our turn classification achieves an average recall and precision of 99.7% and 99.7%, respectively. Furthermore, we confirmed the effectiveness of the classification results for the assessment of maneuver quality.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.70; ------------------------------
• 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: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 62, 号 1, 発行日 2021-01-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764