{"id":211454,"updated":"2025-01-19T17:47:39.087979+00:00","links":{},"created":"2025-01-19T01:12:36.838625+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00211454","sets":["1164:4061:10490:10587"]},"path":["10587"],"owner":"44499","recid":"211454","title":["脳波指標と心拍変動指標の簡便な計測機器と特徴量選択による感情推定モデルの構築と精度検証"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-05-27"},"_buckets":{"deposit":"20319cef-ec35-4b4c-8068-93b47e07367f"},"_deposit":{"id":"211454","pid":{"type":"depid","value":"211454","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"脳波指標と心拍変動指標の簡便な計測機器と特徴量選択による感情推定モデルの構築と精度検証","author_link":["537283","537284","537286","537287","537282","537285"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"脳波指標と心拍変動指標の簡便な計測機器と特徴量選択による感情推定モデルの構築と精度検証"},{"subitem_title":"Construction and Accuracy Verification of Emotion Estimation Model Using a Simple Measurement Device and Feature Selection for Electroencephalography and Heart Rate Variability Indices","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人間状態推定・理解","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-05-27","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"芝浦工業大学"},{"subitem_text_value":"芝浦工業大学"},{"subitem_text_value":"芝浦工業大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Shibaura Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Shibaura Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Shibaura Institute of Technology","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/211454/files/IPSJ-UBI21070008.pdf","label":"IPSJ-UBI21070008.pdf"},"date":[{"dateType":"Available","dateValue":"2023-05-27"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-UBI21070008.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":"36"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"e65f3bf1-e4d1-4b1b-98ce-e4aaf63429a1","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"鈴木, 圭"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"ラオハカンワンウィット, ティポーン"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"菅谷, みどり"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kei, Suzuki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tipporn, Laohakangvalvit","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Midori, Sugaya","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":"機械学習を応用した脳波と心拍変動指標による感情推定において,2 つの課題が存在する.1 つ目は,生理指標の計測機器が高価であり,装着が簡便でない点である.2 つ目は,精度向上が期待できる,感情推定に不要な指標の削除が行われていない点である.そこで本研究では,これら 2 つの課題を解決しつつ,精度が良い感情推定モデルの構築を目的とする.安価かつ装着が簡便な単一電極の脳波計と光電式脈波計を用いた.また,多角的な複数手法により不要な指標の削除を行った.男性 18 名,女性 7 名の計 25 名の実験参加者から機械学習に用いるデータを収集した.そして,深層学習により感情推定モデルを構築し,そのモデルの精度検証には,推定を試みる対象人物の生理指標のデータを学習データに含める方法と学習データに含めない方法の 2 種類を用いた.その結果,前者では 4 感情分類において 99% の精度を得た.一方,後者では前者と比べ著しく低い 23% の精度を得た.これは学習をせず無作為な推定をするモデルの精度 25% を下回る結果である.この結果により,安価な機材でも感情推定をする対象人物の生理指標のデータを学習データに含めることができれば精度よく感情推定でき,目的が達成できることが示唆された.しかし,含めることができなければ低い感情推定精度となり,目的が達成できないことが示唆された.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","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":"2021-05-27","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"8","bibliographicVolumeNumber":"2021-UBI-70"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}