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is essential for improving safety and productivity and preventing accidents. Machine-learning-based data-driven methods have shown promise in extracting valuable information from complex monitoring data. However, practical industrial settings still struggle with the data collection difficulties and low prediction accuracy of machine learning models due to the complex work environment. To tackle these challenges, a novel approach called a long short-term memory (LSTM)-based deep stacked sequence-to-sequence autoencoder is proposed for predicting the health status of workers in confined spaces. The first step involves implementing a wireless data acquisition system using edge-cloud platforms. Smart wearable devices are used to collect data from multiple sources, like temperature, heart rate, and pressure. These comprehensive data provide insights into the workers\u2019 health status within the closed space of a manufacturing factory. Next, a hybrid model combining deep learning and support vector machine (SVM) is constructed for anomaly detection. The LSTM-based deep stacked sequence-to-sequence autoencoder is specifically designed to learn deep discriminative features from the time-series data by reconstructing the input data and thus generating fused deep features. These features are then fed into a one-class SVM, enabling accurate recognition of workers\u2019 health status. The effectiveness and superiority of the proposed approach are demonstrated through comparisons with other existing approaches.<\/jats:p>","DOI":"10.3390\/s23187874","type":"journal-article","created":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T10:09:22Z","timestamp":1694686162000},"page":"7874","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Long-Short-Term-Memory-Based Deep Stacked Sequence-to-Sequence Autoencoder for Health Prediction of Industrial Workers in Closed Environments Based on Wearable Devices"],"prefix":"10.3390","volume":"23","author":[{"given":"Weidong","family":"Xu","sequence":"first","affiliation":[{"name":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingke","family":"He","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7493-1399","authenticated-orcid":false,"given":"Weihua","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"},{"name":"Pazhou Lab, Guangzhou 510005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"He","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyang","family":"Wan","sequence":"additional","affiliation":[{"name":"Future Tech, South China University of Technology, Guangzhou 510640, China"},{"name":"Department of Mathematics and Theories, Peng Cheng Laboratory, Shenzhen 518000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wu","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6100-7332","authenticated-orcid":false,"given":"Zhuyun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"},{"name":"Pazhou Lab, Guangzhou 510005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,14]]},"reference":[{"key":"ref_1","first-page":"31","article-title":"The use of smart glasses in healthcare\u2014Review","volume":"3","year":"2015","journal-title":"MEDtube Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mardonova, M., and Choi, Y. 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