{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T04:42:32Z","timestamp":1783485752414,"version":"3.55.0"},"reference-count":23,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,5]],"date-time":"2022-01-05T00:00:00Z","timestamp":1641340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, wet-bulb globe temperature (WBGT) has attracted a lot of attention as a useful index for measuring heat strokes even when core body temperature cannot be available for the prevention. However, because the WBGT is only valid in the vicinity of the WBGT meter, the actual ambient heat could be different even in the same room owing to ventilation, clothes, and body size, especially in hot specific occupational environments. To realize reliable heat stroke prevention in hot working places, we proposed a new personalized vital sign index, which is combined with several types of vital data, including the personalized heat strain temperature (pHST) index based on the temperature\/humidity measurement to adjust the WBGT at the individual level. In this study, a wearable device was equipped with the proposed pHST meter, a heart rate monitor, and an accelerometer. Additionally, supervised machine learning based on the proposed personalized vital index was introduced to improve the prevention accuracy. Our developed system with the proposed vital sign index achieved a prevention accuracy of 85.2% in a hot occupational experiment in the summer season, where the true positive rate and true negative rate were 96.3% and 83.7%, respectively.<\/jats:p>","DOI":"10.3390\/s22010395","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"395","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Heat Stroke Prevention in Hot Specific Occupational Environment Enhanced by Supervised Machine Learning with Personalized Vital Signs"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9525-3374","authenticated-orcid":false,"given":"Takunori","family":"Shimazaki","sequence":"first","affiliation":[{"name":"Department of Clinical Engineering, Faculty of Health Care Sciences, Jikei University of Health Care Sciences, Osaka 532-0003, Japan"},{"name":"Graduate School of Engineering, Osaka City University, Osaka 558-8585, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4872-1539","authenticated-orcid":false,"given":"Daisuke","family":"Anzai","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Nagoya Institute of Technology, Naogya 466-8555, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenta","family":"Watanabe","sequence":"additional","affiliation":[{"name":"West Japan Railway Company, Osaka 564-0021, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atsushi","family":"Nakajima","sequence":"additional","affiliation":[{"name":"West Japan Railway Company, Osaka 564-0021, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitsuhiro","family":"Fukuda","sequence":"additional","affiliation":[{"name":"Mets Inc., Tokyo 120-0036, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shingo","family":"Ata","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Osaka City University, Osaka 558-8585, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,5]]},"reference":[{"key":"ref_1","first-page":"302","article-title":"Control of heat casualties at military training centers","volume":"16","author":"Yaglou","year":"1957","journal-title":"Amer. 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Available online: https:\/\/www.wbgt.env.go.jp\/record_data.php?region=07&prefecture=62&point=62078."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/395\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:36:25Z","timestamp":1760362585000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/395"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,5]]},"references-count":23,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010395"],"URL":"https:\/\/doi.org\/10.3390\/s22010395","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,5]]}}}