{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T11:27:08Z","timestamp":1786966028282,"version":"build-2736575974"},"reference-count":38,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["JUSRP202501098"],"award-info":[{"award-number":["JUSRP202501098"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"General Project of Philosophy and Social Science Research in Higher Education Institutions","award":["2024SJYB0651"],"award-info":[{"award-number":["2024SJYB0651"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Smart textiles require reliable physiological sensing despite signal degradation caused by fabric deformation, material fatigue, and unstable textile\u2013skin interfaces. This study presents a multi-modal GAN-based anomaly detection framework for signal reliability assessment using acceleration (ACC), electrodermal activity (EDA), and heart rate (HR). Controlled injection of baseline drift, amplitude scaling, and signal dropout generates normal\/anomalous labels for supervised training. Temporal encoding and cross-modal attention distinguish textile-mimicking anomalies from physiologically plausible variations. On the 36-subject PhysioNet dataset, the framework achieves an F1-score of 0.9197 and exceeds the evaluated single-modal models by more than 35%. Using PhysioNet-trained weights without fine-tuning, zero-shot evaluation on the textile-integrated WWBS Metrics dataset achieves an F1-score of 0.8723 with ACC and derived HR. These results demonstrate cross-dataset transfer under the controlled synthetic-fault protocol; validation using physically induced textile faults remains necessary.<\/jats:p>","DOI":"10.3390\/s26165158","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T13:12:56Z","timestamp":1786713176000},"page":"5158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Modal GAN-Based Anomaly Detection for Signal Reliability Assessment in Fabric-Integrated Smart Textiles"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6175-3486","authenticated-orcid":false,"given":"Jianbin","family":"Wu","sequence":"first","affiliation":[{"name":"School of Design, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2817-5506","authenticated-orcid":false,"given":"Ru","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Automation and Intelligent Science, Jiangnan University, Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangfang","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Design, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13301","DOI":"10.1021\/acsnano.2c06287","article-title":"Smart textiles for healthcare and sustainability","volume":"16","author":"Tat","year":"2022","journal-title":"ACS Nano"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"204","DOI":"10.3390\/jfb6020204","article-title":"Medical smart textiles based on fiber optic technology: An overview","volume":"6","author":"Massaroni","year":"2015","journal-title":"J. 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