{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00216638","sets":["1164:5159:10869:10870"]},"path":["10870"],"owner":"44499","recid":"216638","title":["自動運転時における脳波・心電図からの異常ブレーキ検出に有効な特徴"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-02-22"},"_buckets":{"deposit":"ff6d64c1-7be8-4791-80e3-7e7d9a4a1bc1"},"_deposit":{"id":"216638","pid":{"type":"depid","value":"216638","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"自動運転時における脳波・心電図からの異常ブレーキ検出に有効な特徴","author_link":["559445","559454","559451","559448","559446","559450","559452","559449","559453","559447"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"自動運転時における脳波・心電図からの異常ブレーキ検出に有効な特徴"},{"subitem_title":"Effective Features for Detecting Abnormal Braking from Electroencephalogram and Electrocardiogram during Automatic Driving","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ポスターセッション3","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-02-22","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京農工大学大学院工学府電気電子工学専攻"},{"subitem_text_value":"東京農工大学大学院工学府電気電子工学専攻"},{"subitem_text_value":"ジヤトコエンジニアリング株式会社"},{"subitem_text_value":"コルラボ株式会社"},{"subitem_text_value":"ジヤトコ株式会社"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology","subitem_text_language":"en"},{"subitem_text_value":"Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology","subitem_text_language":"en"},{"subitem_text_value":"JATCO Engineering Ltd","subitem_text_language":"en"},{"subitem_text_value":"CorLab Inc., Tokyo, Japan","subitem_text_language":"en"},{"subitem_text_value":"Innovative Technology Development Department, JATCO Ltd","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/216638/files/IPSJ-SLP22140037.pdf","label":"IPSJ-SLP22140037.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLP22140037.pdf","filesize":[{"value":"2.3 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"22"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"c1c78222-362b-4361-8416-55da40e1f107","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"関口, 絵理香"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田中, 聡久"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"久保田, 健"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 俊"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"蒔田, 健一"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Erika, Sekiguchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Toshihisa, Tanaka","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ken, Kubota","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shun, Nakamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kenichi, Makita","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10442647","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":"2188-8663","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"自動運転の技術開発は日進月歩であるが，基本的に安全性の担保が主目的である．しかしながら，自動運転車の制動は，運転者にとって必ずしも快適であるとは言えない．そこで本稿では，自動制動時に運転者が感じる違和感について，本人の想定するブレーキタイミングと異なった場合に，生体反応が脳波と心電図に現れるとの仮説を立てた．仮説検証のため，通常・異常時のブレーキタイミングを呈示した際の，脳波と心電図を解析し，Support Vector Machine（SVM）によって異常ブレーキの識別をした．その結果，通常・異常ブレーキにおける α 帯域のパワーに有意な差（p < 0.01）があった．さらに，脳波と心電図の特徴量を用いて，SVM で異常ブレーキを識別した結果，脳波のパワー比と心拍特徴の組み合わせのモデルで 86.0%，心拍のみのモデルで 88.4%を達成した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Although automated driving technology is advancing rapidly, the main objective of the development is to ensure safety. However, the braking of an automated vehicle is not always comfortable for drivers. In this paper, we hypothesized that the discomfort felt by the driver during automatic braking would appear in the electroencephalogram (EEG) and electrocardiogram (ECG) when the braking timing differs from that assumed by the driver. We analyzed EEG and ECG during normal and abnormal braking timing and discriminated abnormal brakes using a Support Vector Machine to test our hypothesis. The results showed a significant difference (p < 0.01) in the power of the α band for normal and abnormal braking. Furthermore, the model with the combination of EEG power ratio and heart rate features achieved 86.0%, and the model with only heart rate features achieved 88.4%.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告音声言語情報処理（SLP）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-02-22","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"37","bibliographicVolumeNumber":"2022-SLP-140"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":216638,"updated":"2025-01-19T15:47:10.388141+00:00","links":{},"created":"2025-01-19T01:17:09.661167+00:00"}