Sweat Analysis-Based Fatigue Monitoring during Construction Rebar Bending Tasks
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 9
Abstract
This study proposes a novel approach to monitor the fatigue levels of construction rebar benders by measuring chemical biomarkers using sweat sensors. Fatigue resulting from dehydration and energy depletion can severely endanger the safety and health of construction workers. Sodium, lactate, glucose, and sweat rate were chosen as detectable biomarkers in this study, as their concentrations can indicate hydration status, energy consumption, and electrolyte balance, making them suitable for fatigue monitoring. The results were used to construct a fatigue model using supervised machine learning approaches. Construction rebar experiments were conducted while the sweat-based biosensors were applied to rebar workers to evaluate their fatigue with five different classifiers, demonstrating accuracy rates ranging from 71.43% to 96.43%. The results suggested that sweat-based biomarkers offer a noninvasive and accessible fatigue monitoring alternative. This can potentially help alleviate fatigue-related adverse ill effects like dehydration or cramping by enabling instant fluid or nutrient supply recommendations during construction manual tasks. It also provides valuable insights into the physiological effects of rebar work. Besides, this study presents a valuable model for predicting workers’ fatigue levels, which could be applied in the construction industry to improve workers’ safety and productivity. Furthermore, the study highlights the importance of maintaining appropriate hydration, nutrition, and electrolyte balance during physically demanding tasks like construction manual work.
Practical Applications
The study demonstrates that sweat biomarkers, including sweat rate, sodium, lactate, and glucose, can be utilized to assess fatigue among construction rebar workers. Sweat biosensors offer advantages of small size and non-invasiveness, making them suitable for a wide range of scenarios in both the construction industry and sports fields. Moreover, sweat rate and sodium levels can indicate hydration status, and their measurements can be used to recommend immediate fluid intake. Also, lactate and glucose are essential resources that sustain the body, and their measurements can suggest appropriate nutritional intake. These instant recommendations can alleviate the adverse effects of fatigue, consequently reducing fatigue levels. Thus, it is a promising methodology for enhancing the health and safety of the construction industry. In addition, the results of the study will be shared with construction companies, allowing them to introspect fatigue development and chalk out further interventions and policies to manage fatigue effectively.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Acknowledgments
The authors acknowledge the following two funding grants: the General Research Fund (GRF) Grant (15201621) titled “Monitoring and managing fatigue of construction plant and equipment operators exposed to prolonged sitting”; and the General Research Fund (GRF) Grant (15210720) titled “The development and validation of a noninvasive tool to monitor mental and physical stress in construction workers.”
References
Abdelhamid, T. S., and J. G. Everett. 2002. “Physiological demands during construction work.” J. Constr. Eng. Manage. 128 (5): 427–437. https://doi.org/10.1061/(ASCE)0733-9364(2002)128:5(427).
Anwer, S., H. Li, M. F. Antwi-Afari, W. Umer, I. Mehmood, M. Al-Hussein, and A. Y. L. Wong. 2021. “Test-retest reliability, validity, and responsiveness of a textile-based wearable sensor for real-time assessment of physical fatigue in construction bar-benders.” J. Build. Eng. 44 (Dec): 103348. https://doi.org/10.1016/j.jobe.2021.103348.
Anwer, S., H. Li, M. F. Antwi-Afari, W. Umer, and A. Y. L. Wong. 2020. “Cardiorespiratory and thermoregulatory parameters are good surrogates for measuring physical fatigue during a simulated construction task.” Int. J. Environ. Res. Public Health 17 (15): 5418. https://doi.org/10.3390/ijerph17155418.
Aphamis, G., P. S. Stavrinou, E. Andreou, and C. D. Giannaki. 2019. “Hydration status, total water intake and subjective feelings of adolescents living in a hot environment, during a typical school day.” Int. J. Adolesc. Med. Health 33 (4): 20180230. https://doi.org/10.1515/ijamh-2018-0230.
Armstrong, L. E., M. S. Ganio, D. J. Casa, E. C. Lee, B. P. McDermott, J. F. Klau, L. Jimenez, L. Le Bellego, E. Chevillotte, and H. R. Lieberman. 2012. “Mild dehydration affects mood in healthy young women.” J. Nutr. 142 (2): 382–388. https://doi.org/10.3945/jn.111.142000.
Aryal, A., A. Ghahramani, and B. Becerik-Gerber. 2017. “Monitoring fatigue in construction workers using physiological measurements.” Autom. Constr. 82 (Oct): 154–165. https://doi.org/10.1016/j.autcon.2017.03.003.
Baker, L. B. 2017. “Sweating rate and sweat sodium concentration in athletes: A review of methodology and intra/interindividual variability.” Sports Med. 47 (S1): 111–128. https://doi.org/10.1007/s40279-017-0691-5.
Baker, L. B. 2019. “Physiology of sweat gland function: The roles of sweating and sweat composition in human health.” Temperature (Austin) 6 (3): 211–259. https://doi.org/10.1080/23328940.2019.1632145.
Baker, L. B., et al. 2020. “Skin-interfaced microfluidic system with personalized sweating rate and sweat chloride analytics for sports science applications.” Sci. Adv. 6 (50): eabe3929. https://doi.org/10.1126/sciadv.abe3929.
Baker, L. B., K. A. Barnes, M. L. Anderson, D. H. Passe, and J. R. Stofan. 2016. “Normative data for regional sweat sodium concentration and whole-body sweating rate in athletes.” J. Sports Sci. 34 (4): 358–368. https://doi.org/10.1080/02640414.2015.1055291.
Bartlett, S., J. Espinal, P. Janssens, and B. D. Ross. 1984. “The influence of renal function on lactate and glucose metabolism.” Biochem. J. 219 (1): 73–78. https://doi.org/10.1042/bj2190073.
BIOSYSTEMS, E. 2022. “Gx Sweat Patch provides hydration biomarker analytics and recovery insights.” Accessed December 30, 2022. https://www.epicorebiosystems.com/gx-sweat-patch/.
Borg, G. A. V. 1982. “Psychophysical bases of perceived exertion.” Med. Sci. Sports Exercise 14 (5): 377–381. https://doi.org/10.1249/00005768-198205000-00012.
Brooks, G. A. 2002. “Lactate shuttles in nature.” Biochem. Soc. Trans. 30 (2): 258–264. https://doi.org/10.1042/bst0300258.
Brooks, G. A. 2018. “The science and translation of lactate shuttle theory.” Cell Metab. 27 (4): 757–785. https://doi.org/10.1016/j.cmet.2018.03.008.
Brooks, G. A. 2020. “Lactate as a fulcrum of metabolism.” Redox Biol. 35 (Aug): 101454. https://doi.org/10.1016/j.redox.2020.101454.
Brooks, G. A., J. A. Arevalo, A. D. Osmond, R. G. Leija, C. C. Curl, and A. P. Tovar. 2022. “Lactate in contemporary biology: A phoenix risen.” J. Physiol 600 (5): 1229–1251. https://doi.org/10.1113/JP280955.
Buono, M. J., N. V. Lee, and P. W. Miller. 2010. “The relationship between exercise intensity and the sweat lactate excretion rate.” J. Physiol. Sci. 60 (2): 103–107. https://doi.org/10.1007/s12576-009-0073-3.
Carvalho, V. O., E. A. Bocchi, and G. V. Guimarães. 2009. “The Borg scale as an important tool of self-monitoring and self-regulation of exercise prescription in heart failure patients during hydrotherapy: a randomized blinded controlled trial.” Circ. J.: Off. J. Jpn. Circ. Soc. 73 (10): 1871–1876. https://doi.org/10.1253/circj.CJ-09-0333.
Cheuvront, S. N., and R. W. Kenefick. 2014. “Dehydration: Physiology, assessment, and performance effects.” Compr. Physiol. 4 (1): 257–285. https://doi.org/10.1002/cphy.c130017.
Cian, C., N. Koulmann, P. A. Barraud, C. Raphel, C. Jimenez, and B. Melin. 2000. “Influence of variations in body hydration on cognitive function: Effect of hyperhydration, heat stress, and exercise-induced dehydration.” J. Psychophysiol. 14 (1): 29–36. https://doi.org/10.1027//0269-8803.14.1.29.
Cincotta, M. C., M. M. Engelhard, M. Stankey, and M. D. Goldman. 2016. “Fatigue and fluid hydration status in multiple sclerosis: A hypothesis.” Multiple Sclerosis 22 (11): 1438–1443. https://doi.org/10.1177/1352458516663854.
Coyle, E. F., and S. J. Montain. 1992. “Carbohydrate and fluid ingestion during exercise: Are there trade-offs?” Med. Sci. Sports Exercise 24 (6): 671–678. https://doi.org/10.1249/00005768-199206000-00009.
Cryer, P. E. 1993. “Glucose counterregulation: prevention and correction of hypoglycemia in humans.” Am. J. Physiol. Endocrinol. Metab. 264 (2): E149–E155. https://doi.org/10.1152/ajpendo.1993.264.2.E149.
Cunha, F. A. D., P. D. T. V. Farinatti, and A. W. Midgley. 2010. “Methodological and practical application issues in exercise prescription using the heart rate reserve and oxygen uptake reserve methods.” J. Sci. Med. Sport 14 (1): 46–57. https://doi.org/10.1016/j.jsams.2010.07.008.
Day, M. L., M. R. McGuigan, G. Brice, and C. Foster. 2004. “Monitoring exercise intensity during resistance training using the session RPE scale.” J. Strength Cond. Res. 18 (2): 353–358. https://doi.org/10.1519/00124278-200405000-00027.
De Souza, D., E. Campos, R. Gonçalves, J. Viana, J. De Lima, T. Santos, and V. Damasceno. 2023. “Validity of the Borg 6–20 categories obtegories obtained in incremental testing for prescribing aerobic exercise intensity: a systematic review.” Hum. Mov. 24 (1): 46–55. https://doi.org/10.5114/hm.2023.113715.
Divya Bharathi, K., et al. 2022. “Automated detection of muscle fatigue conditions from cyclostationary based geometric features of surface electromyography signals.” Comput. Methods Biomech. Biomed. Eng. 25 (3): 320–332. https://doi.org/10.1080/10255842.2021.1955104.
Elshafei, M., D. E. Costa, and E. Shihab. 2022. “Toward the personalization of biceps fatigue detection model for gym activity: An approach to utilize wearables & data from the crowd.” Sensors 22 (4): 1454. https://doi.org/10.3390/s22041454.
Emhoff, C.-A. W., L. A. Messonnier, M. A. Horning, J. A. Fattor, T. J. Carlson, and G. A. Brooks. 2013. “Direct and indirect lactate oxidation in trained and untrained men.” J. Appl. Physiol. 115 (6): 829–838. https://doi.org/10.1152/japplphysiol.00538.2013.
Eston, R. 2012. “Use of ratings of perceived exertion in sports.” Int. J. Sports Physiol. Perform 7 (2): 175–182. https://doi.org/10.1123/ijspp.7.2.175.
Fadda, R., G. Rapinett, D. Grathwohl, M. Parisi, R. Fanari, C. M. Calo, and J. Schmitt. 2012. “Effects of drinking supplementary water at school on cognitive performance in children.” Appetite 59 (3): 730–737. https://doi.org/10.1016/j.appet.2012.07.005.
Fardhosseini, S., M. Habibnezhad, H. Jebelli, G. Migliaccio, H. W. Lee, and J. Puckett. 2020. “Recognition of construction workers’ physical fatigue based on gait patterns driven from three-axis accelerometer embedded in a smartphone.” In Construction research congress 2020: Safety, workforce, and education, 453–462. Reston, VA: ASCE.
Fatisson, J., V. Oswald, and F. Lalonde. 2016. “Influence diagram of physiological and environmental factors affecting heart rate variability: An extended literature overview.” Heart Int. 11 (1): e32–e40. https://doi.org/10.5301/heartint.5000232.
Fernández, A., S. García López, M. Galar, R. C. Prati, B. Krawczyk, and F. Herrera. 2018. Learning from imbalanced data sets. Cham, Switzerland: Springer.
Hamouti, N., V. E. Fernández-Elías, J. F. Ortega, and R. Mora-Rodriguez. 2014. “Ingestion of sodium plus water improves cardiovascular function and performance during dehydrating cycling in the heat.” Scand. J. Med. Sci. Sports 24 (3): 507–518. https://doi.org/10.1111/sms.12028.
Han, J., J. Pei, and M. Kamber. 2011. Data mining: Concepts and techniques. Amsterdam: Elsevier Science & Technology.
Hart, S. G. 2006. “Nasa-task load index (Nasa-TLX) 20 years later.” Proc. Hum. Factors Ergon. Soc. Annu. Meet. 50 (9): 904–908. https://doi.org/10.1177/154193120605000909.
Huang, X., et al. 2021. “Epidermal self-powered sweat sensors for glucose and lactate monitoring.” Bio-des. Manuf. 5 (1): 201–209. https://doi.org/10.1007/s42242-021-00156-1.
Ivy, J. L. 1999. “Role of carbohydrate in physical activity.” Clin. Sports Med. 18 (3): 469–484. https://doi.org/10.1016/S0278-5919(05)70162-9.
James, G., D. Witten, T. Hastie, and R. Tibshirani. 2013. Vol. 103 of An introduction to statistical learning: With applications in R. New York: Springer.
Karpova, E. V., A. I. Laptev, E. A. Andreev, E. E. Karyakina, and A. A. Karyakin. 2020. “Relationship between sweat and blood lactate levels during exhaustive physical exercise.” ChemElectroChem 7 (1): 191–194. https://doi.org/10.1002/celc.201901703.
Kaya, T., G. Liu, J. Ho, K. Yelamarthi, K. Miller, J. Edwards, and A. Stannard. 2019. “Wearable sweat sensors: Background and current trends.” Electroanalysis 31 (3): 411–421. https://doi.org/10.1002/elan.201800677.
Krawczyk, B. 2016. “Learning from imbalanced data: Open challenges and future directions.” Prog. Artif. Intell. 5 (4): 221–232. https://doi.org/10.1007/s13748-016-0094-0.
Krupp, L. 2006. “Fatigue is intrinsic to multiple sclerosis (MS) and is the most commonly reported symptom of the disease.” Multiple Sclerosis 12 (4): 367–368. https://doi.org/10.1191/135248506ms1373ed.
Maher, F., S. J. Vannucci, and I. A. Simpson. 1994. “Glucose transporter proteins in brain.” FASEB J. 8 (13): 1003–1011. https://doi.org/10.1096/fasebj.8.13.7926364.
Marotta, L., J. H. Buurke, B.-J. F. van Beijnum, and J. Reenalda. 2021. “Towards machine learning-based detection of running-induced fatigue in real-world scenarios: Evaluation of IMU sensor configurations to reduce intrusiveness.” Sensors 21 (10): 3451. https://doi.org/10.3390/s21103451.
Maughan, R. J. 2003. “Impact of mild dehydration on wellness and on exercise performance.” Eur. J. Clin. Nutr. 57 (S2): S19–S23. https://doi.org/10.1038/sj.ejcn.1601897.
Michielsen, H. J., J. De Vries, and G. L. Van Heck. 2003. “Psychometric qualities of a brief self-rated fatigue measure.” J. Psychosomatic Res. 54 (4): 345–352. https://doi.org/10.1016/S0022-3999(02)00392-6.
Mitropoulos, P., and B. Memarian. 2013. “Task demands in masonry work: Sources, performance implications, and management strategies.” J. Constr. Eng. Manage. 139 (5): 581–590. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000586.
Montain, S. J., and W. J. Tharion. 2010. “Hypohydration and muscular fatigue of the thumb alter median nerve somatosensory evoked potentials.” Appl. Physiol. Nutr. Metab. 35 (4): 456–463. https://doi.org/10.1139/H10-032.
Moyer, J., D. Wilson, I. Finkelshtein, B. Wong, and R. Potts. 2012. “Correlation between sweat glucose and blood glucose in subjects with diabetes.” Diabetes Technol. Ther. 14 (5): 398–402. https://doi.org/10.1089/dia.2011.0262.
Navale, A. M., and A. N. Paranjape. 2016. “Glucose transporters: Physiological and pathological roles.” Biophys. Rev. 8 (1): 5–9. https://doi.org/10.1007/s12551-015-0186-2.
Nuccio, R. P., K. A. Barnes, J. M. Carter, and L. B. Baker. 2017. “Fluid balance in team sport athletes and the effect of hypohydration on cognitive, technical, and physical performance.” Sports Med. 47 (10): 1951–1982. https://doi.org/10.1007/s40279-017-0738-7.
Olarte, O., J. Chilo, J. Pelegri-Sebastia, K. Barbé, and W. V. Moer. 2013. “Glucose detection in human sweat using an electronic nose.” In Proc., 35th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), 1462–1465. New York: IEEE. https://doi.org/10.1109/EMBC.2013.6609787.
Pedregosa, F., et al. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12 (Nov): 2825–2830. https://doi.org/10.5555/1953048.2078195.
Perceived Exertion (Borg Rating of Perceived Exertion Scale). 2022. “Perceived Exertion (Borg Rating of Perceived Exertion Scale).” Accessed January 26, 2023. https://www.cdc.gov/physicalactivity/basics/measuring/exertion.htm.
Pinto-Bernal, M. J., C. A. Cifuentes, O. Perdomo, M. Rincon-Roncancio, and M. Munera. 2021. “A data-driven approach to physical fatigue management using wearable sensors to classify four diagnostic fatigue states.” Sensors (Basel) 21 (19): 6401. https://doi.org/10.3390/s21196401.
Rahman, S. A. M. M., M. A. Ali, and M. A. Al Mamun. 2021. “The use of wearable sensors for the classification of electromyographic signal patterns based on changes in the elbow joint angle.” Procedia Comput. Sci. 185 (Jan): 338–344. https://doi.org/10.1016/j.procs.2021.05.043.
Seshadri, D. R., R. T. Li, J. E. Voos, J. R. Rowbottom, C. M. Alfes, C. A. Zorman, and C. K. Drummond. 2019. “Wearable sensors for monitoring the physiological and biochemical profile of the athlete.” NPJ Digit Med. 2 (1): 72. https://doi.org/10.1038/s41746-019-0150-9.
Shirreffs, S. M., S. J. Merson, S. M. Fraser, and D. T. Archer. 2004. “The effects of fluid restriction on hydration status and subjective feelings in man.” Br. J. Nutr. 91 (6): 951–958. https://doi.org/10.1079/BJN20041149.
Skiena, S. S. 2017. The data science design manual. Cham, Switzerland: Springer.
Sluiter, J. K. 2006. “High-demand jobs: age-related diversity in work ability?” Appl. Ergon. 37 (4): 429–440. https://doi.org/10.1016/j.apergo.2006.04.007.
Umer, W., Y. Yu, M. F. Antwi-Afari, L. Jue, M. K. Siddiqui, and H. Li. 2022. “Heart rate variability based physical exertion monitoring for manual material handling tasks.” Int. J. Ind. Ergon. 89 (May): 103301. https://doi.org/10.1016/j.ergon.2022.103301.
Varandas, R., R. Lima, I. B. S. Bermúdez, H. Silva, and H. Gamboa. 2022. “Automatic cognitive fatigue detection using wearable fNIRS and machine learning.” Sensors (Basel) 22 (11): 4010. https://doi.org/10.3390/s22114010.
Wang, J. 2008. “Electrochemical glucose biosensors.” Chem. Rev. 108 (2): 814–825. https://doi.org/10.1021/cr068123a.
Wittbrodt, M. T., and M. Millard-Stafford. 2018. “Dehydration impairs cognitive performance: A meta-analysis.” Med. Sci. Sports Exerc. 50 (11): 2360–2368. https://doi.org/10.1249/MSS.0000000000001682.
Yi, W., A. P. C. Chan, X. Wang, and J. Wang. 2016. “Development of an early-warning system for site work in hot and humid environments: A case study.” Autom. Constr. 62 (Feb): 101–113. https://doi.org/10.1016/j.autcon.2015.11.003.
Zhang, L., M. M. Diraneyya, J. Ryu, C. T. Haas, and E. M. Abdel-Rahman. 2019a. “Jerk as an indicator of physical exertion and fatigue.” Autom. Constr. 104 (Aug): 120–128. https://doi.org/10.1016/j.autcon.2019.04.016.
Zhang, M., E. H. Sparer, L. A. Murphy, J. T. Dennerlein, D. Fang, J. N. Katz, and A. J. Caban-Martinez. 2015. “Development and validation of a fatigue assessment scale for U.S. construction workers.” Am. J. Ind. Med. 58 (2): 220–228. https://doi.org/10.1002/ajim.22411.
Zhang, N., S. M. Du, J. F. Zhang, and G. S. Ma. 2019b. “Effects of dehydration and rehydration on cognitive performance and mood among male college students in Cangzhou, China: A self-controlled trial.” Int. J. Environ. Res. Public Health 16 (11): 1891. https://doi.org/10.3390/ijerph16111891.
Information & Authors
Information
Published In
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 12, 2022
Accepted: Apr 18, 2023
Published online: Jun 19, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 19, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Hongzhe Yue, Gui Ye, Qinjun Liu, Xiaohan Yang, Qingting Xiang, Yalan Luo, Impact of Cognitive Fatigue on Attention and the Implications for Construction Safety: A Neuroscientific Perspective, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-14711, 150, 8, (2024).