{"updated":"2025-01-19T18:02:26.809631+00:00","links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00210737","sets":["6164:6165:6640:10580"]},"path":["10580"],"owner":"44499","recid":"210737","title":["ExerSense: Real-time Exercise Segmentation, Classification and Counting using IMU"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-06-17"},"_buckets":{"deposit":"eccb2edd-5ca4-481d-a1f7-6f36bd0567e5"},"_deposit":{"id":"210737","pid":{"type":"depid","value":"210737","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ExerSense: Real-time Exercise Segmentation, Classification and Counting using IMU","author_link":["534207","534206","534212","534211","534208","534210","534213","534209"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ExerSense: Real-time Exercise Segmentation, Classification and Counting using IMU"},{"subitem_title":"ExerSense: Real-time Exercise Segmentation, Classification and Counting using IMU","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ユビキタスコンピューティングシステム","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2020-06-17","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Aoyama Gakuin University"},{"subitem_text_value":"Aoyama Gakuin University"},{"subitem_text_value":"Turku University of Applied Sciences"},{"subitem_text_value":"Aoyama Gakuin University"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Aoyama Gakuin University","subitem_text_language":"en"},{"subitem_text_value":"Aoyama Gakuin University","subitem_text_language":"en"},{"subitem_text_value":"Turku University of Applied Sciences","subitem_text_language":"en"},{"subitem_text_value":"Aoyama Gakuin University","subitem_text_language":"en"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/210737/files/IPSJ-DICOMO2020024.pdf","label":"IPSJ-DICOMO2020024.pdf"},"date":[{"dateType":"Available","dateValue":"2022-06-17"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2020024.pdf","filesize":[{"value":"1.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"7a7bd82b-a132-4696-9f3d-fa37278eda99","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Ishii, Shun"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yokokubo, Anna"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Luimura, Mika"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Lopez, Guillaume"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Ishii, Shun","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yokokubo, Anna","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Luimura, Mika","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Lopez, Guillaume","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Even though it is well known that physical exercises have numerous emotional and physical health benefits, maintaining a regular exercise routine is quite challenging. Fortunately, there exist technologies that promote us to do physical activities. Nonetheless, almost all of these technologies only target a narrow set of physical exercises (e.g., either running or physical workouts but not both) and are only applicable either in indoor or in outdoor environments, but do not work well in both environments. This paper introduces ExerSense, a real-time segmentation and classification algorithm that recognizes physical exercises, and that works well in both indoor and outdoor environments. The proposed algorithm achieves a 95% classification accuracy for five indoor and outdoor exercises, including segmentation error. This accuracy is similar or better than previous works that handled only indoor workouts, and those use a vision-based approach. Moreover, while comparable machine learning-based approaches need many training data, the proposed correlation-based method needs only one sample of motion data of each target exercise.","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Even though it is well known that physical exercises have numerous emotional and physical health benefits, maintaining a regular exercise routine is quite challenging. Fortunately, there exist technologies that promote us to do physical activities. Nonetheless, almost all of these technologies only target a narrow set of physical exercises (e.g., either running or physical workouts but not both) and are only applicable either in indoor or in outdoor environments, but do not work well in both environments. This paper introduces ExerSense, a real-time segmentation and classification algorithm that recognizes physical exercises, and that works well in both indoor and outdoor environments. The proposed algorithm achieves a 95% classification accuracy for five indoor and outdoor exercises, including segmentation error. This accuracy is similar or better than previous works that handled only indoor workouts, and those use a vision-based approach. Moreover, while comparable machine learning-based approaches need many training data, the proposed correlation-based method needs only one sample of motion data of each target exercise.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"154","bibliographic_titles":[{"bibliographic_title":"マルチメディア，分散協調とモバイルシンポジウム2043論文集"}],"bibliographicPageStart":"151","bibliographicIssueDates":{"bibliographicIssueDate":"2020-06-17","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2020"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":210737,"created":"2025-01-19T01:11:56.457604+00:00"}