{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:15:19Z","timestamp":1792790119339,"version":"4.5.0"},"reference-count":57,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation","award":["#2026575"],"award-info":[{"award-number":["#2026575"]}]},{"name":"National Science Foundation","award":["#2222881"],"award-info":[{"award-number":["#2222881"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>About 40% of the US construction workforce experiences high-level fatigue, which leads to poor judgment, increased risk of injuries, a decrease in productivity, and a lower quality of work. Therefore, it is essential to monitor fatigue to reduce its adverse effects and prevent long-term health problems. However, since fatigue demonstrates itself in several complex processes, there is no single standard measurement method for fatigue detection. This study aims to develop a system for continuous workers\u2019 fatigue monitoring by predicting the aerobic fatigue threshold (AFT) using forearm muscle activity and motion data. The proposed system consists of five modules: Data acquisition, activity recognition, oxygen uptake prediction, maximum aerobic capacity (MAC) estimation, and continuous AFT monitoring. The proposed system was evaluated on the participants performing fourteen scaffold-building activities. The results show that the AFT features have achieved a higher accuracy of 92.31% in assessing the workers\u2019 fatigue level compared to heart rate (51.28%) and percentage heart rate reserve (50.43%) features. Moreover, the overall performance of the proposed system on unseen data using average two-min AFT features was 76.74%. The study validates the feasibility of using forearm muscle activity and motion data to workers\u2019 fatigue levels continuously.<\/jats:p>","DOI":"10.3390\/s22249729","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T05:42:00Z","timestamp":1670823720000},"page":"9729","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Automated and Continuous Fatigue Monitoring in Construction Workers Using Forearm EMG and IMU Wearable Sensors and Recurrent Neural Network"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2221-7959","authenticated-orcid":false,"given":"Srikanth Sagar","family":"Bangaru","sequence":"first","affiliation":[{"name":"ISC Constructors LLC, 20480 Highland Rd, Baton Rouge, LA 70817, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9322-7778","authenticated-orcid":false,"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Bert S. Turner Department of Construction Management, Louisiana State University, 3315D Patrick F. Taylor Hall, Baton Rouge, LA 70803, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fereydoun","family":"Aghazadeh","sequence":"additional","affiliation":[{"name":"Department of Mechanical & Industrial Engineering, Louisiana State University, 3250A Patrick F. Taylor Hall, Baton Rouge, LA 70803, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"key":"ref_1","unstructured":"Wang, T. (2020, July 05). U.S. Construction Industry\u2014Statistics & Facts. 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