Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion
The physical workload evaluation of construction activities will help to prevent excess physical fatigue or overexertion. The workload determination involves measuring physiological responses such as oxygen uptake (VO<sub>2</sub>) while performing the work. The objective of this study is...
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3204 |
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| author | Srikanth Sagar Bangaru Chao Wang Fereydoun Aghazadeh Shashank Muley Sueed Willoughby |
| author_facet | Srikanth Sagar Bangaru Chao Wang Fereydoun Aghazadeh Shashank Muley Sueed Willoughby |
| author_sort | Srikanth Sagar Bangaru |
| collection | DOAJ |
| description | The physical workload evaluation of construction activities will help to prevent excess physical fatigue or overexertion. The workload determination involves measuring physiological responses such as oxygen uptake (VO<sub>2</sub>) while performing the work. The objective of this study is to develop a procedure for automatic oxygen uptake prediction using the worker’s forearm muscle activity and motion data. The fused IMU and EMG data were analyzed to build a bidirectional long-short-term memory (BiLSTM) model to predict VO<sub>2</sub>. The results show a strong correlation between the IMU and EMG features and oxygen uptake (R = 0.90, RMSE = 1.257 mL/kg/min). Moreover, measured (9.18 ± 1.97 mL/kg/min) and predicted (9.22 ± 0.09 mL/kg/min) average oxygen consumption to build one scaffold unit are significantly the same. This study concludes that the fusion of IMU and EMG features resulted in high model performance compared to IMU and EMG alone. The results can facilitate the continuous monitoring of the physiological status of construction workers and early detection of any potential occupational risks. |
| format | Article |
| id | doaj-art-82d2b89a26564c9c940915bc868bf3aa |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-82d2b89a26564c9c940915bc868bf3aa2025-08-20T01:56:39ZengMDPI AGSensors1424-82202025-05-012510320410.3390/s25103204Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data FusionSrikanth Sagar Bangaru0Chao Wang1Fereydoun Aghazadeh2Shashank Muley3Sueed Willoughby4Inncircles Technologies Inc., Baton Rouge, LA 70810, USABert S. Turner Department of Construction Management, Louisiana State University, Baton Rouge, LA 70803, USADepartment of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USAPerformance Contractors Inc., Baton Rouge, LA 70809, USABert S. Turner Department of Construction Management, Louisiana State University, Baton Rouge, LA 70803, USAThe physical workload evaluation of construction activities will help to prevent excess physical fatigue or overexertion. The workload determination involves measuring physiological responses such as oxygen uptake (VO<sub>2</sub>) while performing the work. The objective of this study is to develop a procedure for automatic oxygen uptake prediction using the worker’s forearm muscle activity and motion data. The fused IMU and EMG data were analyzed to build a bidirectional long-short-term memory (BiLSTM) model to predict VO<sub>2</sub>. The results show a strong correlation between the IMU and EMG features and oxygen uptake (R = 0.90, RMSE = 1.257 mL/kg/min). Moreover, measured (9.18 ± 1.97 mL/kg/min) and predicted (9.22 ± 0.09 mL/kg/min) average oxygen consumption to build one scaffold unit are significantly the same. This study concludes that the fusion of IMU and EMG features resulted in high model performance compared to IMU and EMG alone. The results can facilitate the continuous monitoring of the physiological status of construction workers and early detection of any potential occupational risks.https://www.mdpi.com/1424-8220/25/10/3204oxygen uptakewearable sensorselectromyographyinertial measurement unitmachine learningconstruction worker safety |
| spellingShingle | Srikanth Sagar Bangaru Chao Wang Fereydoun Aghazadeh Shashank Muley Sueed Willoughby Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion Sensors oxygen uptake wearable sensors electromyography inertial measurement unit machine learning construction worker safety |
| title | Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion |
| title_full | Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion |
| title_fullStr | Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion |
| title_full_unstemmed | Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion |
| title_short | Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion |
| title_sort | oxygen uptake prediction for timely construction worker fatigue monitoring through wearable sensing data fusion |
| topic | oxygen uptake wearable sensors electromyography inertial measurement unit machine learning construction worker safety |
| url | https://www.mdpi.com/1424-8220/25/10/3204 |
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