{"id":218188,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218188","sets":["1164:4061:10837:10917"]},"path":["10917"],"owner":"44499","recid":"218188","title":["Automatic Eating Stage Classification using ASMR videos"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-05-30"},"_buckets":{"deposit":"668049a2-0ad4-4235-83af-5c7d92972d7e"},"_deposit":{"id":"218188","pid":{"type":"depid","value":"218188","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Automatic Eating Stage Classification using ASMR videos","author_link":["566816","566818","566820","566821","566822","566823","566819","566817"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Automatic Eating Stage Classification using ASMR videos"},{"subitem_title":"Automatic Eating Stage Classification using ASMR videos","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"感覚・知覚","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-05-30","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Faculty of Policy Management, Keio University"},{"subitem_text_value":"Graduate School of Media and Governance, Keio University"},{"subitem_text_value":"Faculty of Environment and Information Studies, Keio University"},{"subitem_text_value":"Faculty of Environment and Information Studies, Keio University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Policy Management, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Media and Governance, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Environment and Information Studies, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Environment and Information Studies, Keio University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/218188/files/IPSJ-UBI22074009.pdf","label":"IPSJ-UBI22074009.pdf"},"date":[{"dateType":"Available","dateValue":"2024-05-30"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-UBI22074009.pdf","filesize":[{"value":"8.4 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":"36"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"d5d70eac-f55c-4625-9ef1-61b04726212a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Mari, Izumikawa"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takafumi, Kawasaki"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tadashi, Okoshi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Jin, Nakazawa"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Mari, Izumikawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takafumi, Kawasaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tadashi, Okoshi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Jin, Nakazawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11838947","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8698","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"A balanced diet and an appropriate calorie intake are the keys to both preventing and treating type II diabetes. Meanwhile, widespread techniques such as manual food logs and food image captures have been posing burdens on those with diabetes and have made diet monitoring difficult to become part of one's routine. The ultimate aim of this study is to develop an earable device that monitors a volume of food intake automatically. However, an automatic food intake volume monitoring requires a detection of biting, chewing, and swallowing sounds with foods of various sizes and textures. The present research therefore attempted to classify an eating sound, collected from YouTube eating ASMR, into one of the following labels: bite/chew, swallow, or other. A CNN machine learning model using sound features as input achieved an accuracy of 86%.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"A balanced diet and an appropriate calorie intake are the keys to both preventing and treating type II diabetes. Meanwhile, widespread techniques such as manual food logs and food image captures have been posing burdens on those with diabetes and have made diet monitoring difficult to become part of one's routine. The ultimate aim of this study is to develop an earable device that monitors a volume of food intake automatically. However, an automatic food intake volume monitoring requires a detection of biting, chewing, and swallowing sounds with foods of various sizes and textures. The present research therefore attempted to classify an eating sound, collected from YouTube eating ASMR, into one of the following labels: bite/chew, swallow, or other. A CNN machine learning model using sound features as input achieved an accuracy of 86%.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告ユビキタスコンピューティングシステム（UBI）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-05-30","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"9","bibliographicVolumeNumber":"2022-UBI-74"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T15:14:19.712304+00:00","created":"2025-01-19T01:18:36.022963+00:00","links":{}}