Academic Journal
Non-Invasive Showering Estimation Utilizing Household-Adaptive Models and Washing Time Data.
| Τίτλος: | Non-Invasive Showering Estimation Utilizing Household-Adaptive Models and Washing Time Data. |
|---|---|
| Συγγραφείς: | Nakata, Takuya, Hashizume, Jiro, Yanada, Akihiro, Nakamura, Masahide |
| Πηγή: | Electronics (2079-9292); Nov2025, Vol. 14 Issue 21, p4336, 21p |
| Θεματικοί όροι: | Smart homes, Residential water consumption, Empirical research |
| Περίληψη: | This study introduces a dual-proxy framework for household-adaptive, non-invasive shower detection using standard water-heater logs. The framework leverages proxy at two complementary levels: a feature-level proxy (washing_seconds) that captures washing duration, and a scheme-level proxy (proxy-driven training) that enables learning in periods without direct shower labels. The proxy feature (washing_seconds) serves as an indirect descriptor of washing behavior, enabling effective inference even under label scarcity. We investigated three research questions: (RQ1) the effectiveness of proxy features in improving shower detection, (RQ2) how proxy-driven evaluation identifies compact yet reliable feature subsets, and (RQ3) the robustness of these subsets in long-term, real-world scenarios. Experiments on two households showed that washing_seconds consistently improved discrimination (raising summer PR-AUC, lowering non-summer false alarms), and that compact subsets of only two or three features, anchored by the proxy feature, achieved stable performance across households. The evaluation represents an illustrative example based on two cooperating households, providing practical evidence of the framework's real-world applicability. Evaluation in real-world conditions confirmed robustness: representative subsets maintained micro PR-AUC 0.724–0.728, micro F1 0.66–0.69 (macro F1 0.55–0.58), and summer PR-AUC near 0.87, with generalization gaps within ±0.01 for discrimination and small positive shifts for F1 (+0.02–+0.05). These results demonstrate that proxy can function both as a feature and as a methodological principle, and that the proposed framework is model-agnostic and transferable to other learning architectures. It provides a foundation for adaptive, privacy-preserving smart home applications that can scale to broader household and healthcare contexts. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20799292&ISBN=&volume=14&issue=21&date=20251101&spage=4336&pages=4336-4356&title=Electronics (2079-9292)&atitle=Non-Invasive%20Showering%20Estimation%20Utilizing%20Household-Adaptive%20Models%20and%20Washing%20Time%20Data.&aulast=Nakata%2C%20Takuya&id=DOI:10.3390/electronics14214336 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Non-Invasive Showering Estimation Utilizing Household-Adaptive Models and Washing Time Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nakata%2C+Takuya%22">Nakata, Takuya</searchLink><br /><searchLink fieldCode="AR" term="%22Hashizume%2C+Jiro%22">Hashizume, Jiro</searchLink><br /><searchLink fieldCode="AR" term="%22Yanada%2C+Akihiro%22">Yanada, Akihiro</searchLink><br /><searchLink fieldCode="AR" term="%22Nakamura%2C+Masahide%22">Nakamura, Masahide</searchLink> – Name: TitleSource Label: Source Group: Src Data: Electronics (2079-9292); Nov2025, Vol. 14 Issue 21, p4336, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Smart+homes%22">Smart homes</searchLink><br /><searchLink fieldCode="DE" term="%22Residential+water+consumption%22">Residential water consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study introduces a dual-proxy framework for household-adaptive, non-invasive shower detection using standard water-heater logs. The framework leverages proxy at two complementary levels: a feature-level proxy (washing_seconds) that captures washing duration, and a scheme-level proxy (proxy-driven training) that enables learning in periods without direct shower labels. The proxy feature (washing_seconds) serves as an indirect descriptor of washing behavior, enabling effective inference even under label scarcity. We investigated three research questions: (RQ1) the effectiveness of proxy features in improving shower detection, (RQ2) how proxy-driven evaluation identifies compact yet reliable feature subsets, and (RQ3) the robustness of these subsets in long-term, real-world scenarios. Experiments on two households showed that washing_seconds consistently improved discrimination (raising summer PR-AUC, lowering non-summer false alarms), and that compact subsets of only two or three features, anchored by the proxy feature, achieved stable performance across households. The evaluation represents an illustrative example based on two cooperating households, providing practical evidence of the framework's real-world applicability. Evaluation in real-world conditions confirmed robustness: representative subsets maintained micro PR-AUC 0.724–0.728, micro F1 0.66–0.69 (macro F1 0.55–0.58), and summer PR-AUC near 0.87, with generalization gaps within ±0.01 for discrimination and small positive shifts for F1 (+0.02–+0.05). These results demonstrate that proxy can function both as a feature and as a methodological principle, and that the proposed framework is model-agnostic and transferable to other learning architectures. It provides a foundation for adaptive, privacy-preserving smart home applications that can scale to broader household and healthcare contexts. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/electronics14214336 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 4336 Subjects: – SubjectFull: Smart homes Type: general – SubjectFull: Residential water consumption Type: general – SubjectFull: Empirical research Type: general Titles: – TitleFull: Non-Invasive Showering Estimation Utilizing Household-Adaptive Models and Washing Time Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nakata, Takuya – PersonEntity: Name: NameFull: Hashizume, Jiro – PersonEntity: Name: NameFull: Yanada, Akihiro – PersonEntity: Name: NameFull: Nakamura, Masahide IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20799292 Numbering: – Type: volume Value: 14 – Type: issue Value: 21 Titles: – TitleFull: Electronics (2079-9292) Type: main |
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