{"updated":"2025-01-20T03:33:41.673362+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00183737","sets":["6164:6165:7006:9266"]},"path":["9266"],"owner":"11","recid":"183737","title":["スマートフォン装着型サーモグラフィを用いた機械学習に基づく深部体温推定"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-10-04"},"_buckets":{"deposit":"cbb141f2-1bc9-48e0-94dc-5bfffaa86f65"},"_deposit":{"id":"183737","pid":{"type":"depid","value":"183737","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"スマートフォン装着型サーモグラフィを用いた機械学習に基づく深部体温推定","author_link":["404167","404170","404169","404168"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"スマートフォン装着型サーモグラフィを用いた機械学習に基づく深部体温推定"}]},"item_type_id":"18","publish_date":"2017-10-04","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学"},{"subitem_text_value":"大阪大学"},{"subitem_text_value":"大阪大学"},{"subitem_text_value":"大阪大学"}]},"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/183737/files/IPSJ-DPSWS2017026.pdf","label":"IPSJ-DPSWS2017026.pdf"},"date":[{"dateType":"Available","dateValue":"2019-10-04"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DPSWS2017026.pdf","filesize":[{"value":"767.5 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"34"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"38eee4f6-10e5-4f25-b64d-1aa474fa760b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2017 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"吉川, 寛樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"濱谷, 尚志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"内山, 彰"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"東野, 輝夫"}],"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":"近年，人体の深部体温が健康状態を表す指標として注目されている．しかし，深部体温を計測するためには，直腸や口腔，鼓膜などの温度を専用の機器で測る必要があり，計測に伴う負担も大きいことから，1 日を通した継続的な深部体温の把握は困難である．一方，近年ではサーモグラフィの小型化が進んでおり，サーモグラフィ内蔵スマートフォンなどを用いて，モバイル環境での温度計測が容易に行える環境が整いつつある．そこで本研究では，サーモグラフィから得られた温度データから，額，頬，首の体表温度を抽出し，機械学習によって深部体温推定モデルを構築する．その際，体表温と深部体温の差を生み出す一因となっている気温やBody Mass Index (BMI) などの個人情報を組み合わせることによって，深部体温推定の精度を向上させる．さらに，BMI だけでは考慮できない個人差を考慮するため，あらかじめ取得した個人の学習データに重み付けを行なった上でモデルを構築する．提案手法の性能を評価するため，男性12名を対象に日常生活におけるのべ192 時間分のデータを収集した．その結果，個人差を考慮して個別にモデルを構築した場合には，平均絶対誤差が0.175 ℃となり，個人差を考慮することの有効性が確認できた．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"173","bibliographic_titles":[{"bibliographic_title":"第25回マルチメディア通信と分散処理ワークショップ論文集"}],"bibliographicPageStart":"169","bibliographicIssueDates":{"bibliographicIssueDate":"2017-10-04","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2017"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-19T00:51:14.223112+00:00","id":183737,"links":{}}