{"links":{},"id":2009311,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02009311","sets":["1164:3865:1771204392599:1777334332473"]},"path":["1777334332473"],"owner":"80578","recid":"2009311","title":["Evidence-Grounded Guardrail Extraction for Activity Recognition in Smart Homes using Small Language Models"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2026-05-06"},"_buckets":{"deposit":"8738140c-b69e-44dd-8625-f6b4358b4c62"},"_deposit":{"id":"2009311","pid":{"type":"depid","value":"2009311","revision_id":0},"owners":[80578],"status":"published","created_by":80578},"item_title":"Evidence-Grounded Guardrail Extraction for Activity Recognition in Smart Homes using Small Language Models","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Evidence-Grounded Guardrail Extraction for Activity Recognition in Smart Homes using Small Language Models","subitem_title_language":"ja"},{"subitem_title":"Evidence-Grounded Guardrail Extraction for Activity Recognition in Smart Homes using Small Language Models","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"MBL","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2026-05-06","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Nara Institute of Science and Technology／University of the Philippines Tacloban College"},{"subitem_text_value":"Nara Institute of Science and Technology"},{"subitem_text_value":"Okayama University／Nara Institute of Science and Technology"},{"subitem_text_value":"Nara Institute of Science and Technology"},{"subitem_text_value":"Nara Institute of Science and Technology"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nara Institute of Science and Technology / University of the Philippines Tacloban College","subitem_text_language":"en"},{"subitem_text_value":"Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Okayama University / Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Nara Institute of Science and Technology","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/2009311/files/IPSJ-MBL26119031.pdf","label":"IPSJ-MBL26119031.pdf"},"date":[{"dateType":"Available","dateValue":"2028-05-06"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MBL26119031.pdf","filesize":[{"value":"4.9 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":"35"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"4577b225-536e-46ac-960b-f05442a5a500","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2026 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Victor,Romero II"}]},{"creatorNames":[{"creatorName":"Tomokazu,Matsui"}]},{"creatorNames":[{"creatorName":"Yuki,Matsuda"}]},{"creatorNames":[{"creatorName":"Hirohiko,Suwa"}]},{"creatorNames":[{"creatorName":"Keiichi,Yasumoto"}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Victor Romero II","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Tomokazu Matsui","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Yuki Matsuda","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Hirohiko Suwa","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Keiichi Yasumoto","creatorNameLang":"en"}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11851388","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":"2188-8817","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Activity recognition remains a significant challenge in pervasive computing, where models must infer user actions from sparse signals but often fail to enforce the contextual constraints required for consistent predictions. This study proposes a failure-triggered method for extracting decision guardrails using Small Language Models (SLMs) to improve the reliability of activity recognition systems. The framework constructs a Knowledge Graph of guardrails from model feedback, which serves as a grounded evidence base for subsequent inference. During inference, these grounded constraints are incorporated to guide predictions toward more contextually consistent activity recognition. We implement a pipeline that generates and reuses these constraints, and evaluate its impact on classification performance and reasoning behaviour. Experiments show that the proposed approach improves top-1 accuracy from 46.4% to 69.7%, with reduced class-level confusion and more consistent predictions. This work offers a training-free mechanism for transitioning from purely pattern-based activity recognition toward more constraint-aware systems.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Activity recognition remains a significant challenge in pervasive computing, where models must infer user actions from sparse signals but often fail to enforce the contextual constraints required for consistent predictions. This study proposes a failure-triggered method for extracting decision guardrails using Small Language Models (SLMs) to improve the reliability of activity recognition systems. The framework constructs a Knowledge Graph of guardrails from model feedback, which serves as a grounded evidence base for subsequent inference. During inference, these grounded constraints are incorporated to guide predictions toward more contextually consistent activity recognition. We implement a pipeline that generates and reuses these constraints, and evaluate its impact on classification performance and reasoning behaviour. Experiments show that the proposed approach improves top-1 accuracy from 46.4% to 69.7%, with reduced class-level confusion and more consistent predictions. This work offers a training-free mechanism for transitioning from purely pattern-based activity recognition toward more constraint-aware systems.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告モバイルコンピューティングと新社会システム（MBL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2026-05-06","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"31","bibliographicVolumeNumber":"2026-MBL-119"}]},"relation_version_is_last":true,"weko_creator_id":"80578"},"created":"2026-04-28T10:56:38.882259+00:00","updated":"2026-04-28T10:56:42.823708+00:00"}