{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:11:10Z","timestamp":1772118670354,"version":"3.50.1"},"reference-count":17,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>With the rapid development of industrial Internet of Things (IIoT), its educational applications extend from equipment monitoring to mental health management. Addressing the limitations of traditional methods (e.g., subjective self\u2010assessment scales) in real\u2010time psychological stress evaluation, this paper proposes a neural network integrating multimodal IIoT data\u2014physiological signals (EEG, HRV), behavioral data (expression, gesture), and interaction logs (text, clickstream)\u2014to build a dynamic fusion and lightweight warning system. The model employs a cross\u2010modal attention mechanism to adaptively allocate data weights (e.g., prioritizing EEG signals by 58% in exam scenarios) and a tensor fusion network (TFN) for feature extraction. An edge\u2010cloud collaborative framework based on federated learning enhances generalization while ensuring privacy through AES\u2010256 encryption, local feature preprocessing, and differential privacy protections during model updates. Experiments on a campus dataset show 89.2% stress classification accuracy (10.7% higher than unimodal approaches), sub\u2010105\u2009ms alert latency, and a 4.7% false alarm rate. Personalized interventions (e.g., counseling) improved stress alleviation by 61.7% for moderate\u2010to\u2010severe cases. This study advances IIoT's role in intelligent mental health management and adaptive human\u2010computer interaction systems.<\/jats:p>","DOI":"10.1002\/itl2.70093","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T15:15:14Z","timestamp":1755270914000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Neural Network Modeling Based on Multimodal\n                    <scp>IIoT<\/scp>\n                    Sensing Data: Psychological Stress Assessment and Industrial Human\u2010Machine Collaboration Early Warning System for University Students"],"prefix":"10.1002","volume":"8","author":[{"given":"Chen Shao","family":"Hong","sequence":"first","affiliation":[{"name":"School of Accounting Guangzhou Huashang College  Guangzhou Guangdong Province China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2971-8561","authenticated-orcid":false,"given":"Zhong","family":"Chun","sequence":"additional","affiliation":[{"name":"School of Creativity and Design Guangzhou Huashang College  Guangzhou Guangdong Province China"}]}],"member":"311","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"P.Prajod \u201cIn the Face and Heart of Data Scarcity in Industry 5.0: Exploring the Applicability of Facial and Physiological AI Models for Operator Well\u2010Being in Human\u2010Robot Collaboration \u201d 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Minimization for Heterogenous Traffic Coexistence With Puncturing in Mobile Edge Computing\u2010Based Industrial Internet of Things","volume":"21","author":"Wang X.","year":"2024","journal-title":"China Communications"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2021.102581"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3116785"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jai.2024.07.004"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.23919\/JSEE.2024.000074"}],"container-title":["Internet Technology 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