Evaluation of Data Processing and Artifact Removal Approaches Used for Physiological Signals Captured Using Wearable Sensing Devices during Construction Tasks
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 1
Abstract
Wearable sensing devices (WSDs) have enormous promise for monitoring construction worker safety. They can track workers and send safety-related information in real time, allowing for more effective and preventative decision making. WSDs are particularly useful on construction sites since they can track workers’ health, safety, and activity levels, among other metrics that could help optimize their daily tasks. WSDs may also assist workers in recognizing health-related safety risks (such as physical fatigue) and taking appropriate action to mitigate them. The data produced by these WSDs, however, is highly noisy and contaminated with artifacts that could have been introduced by the surroundings, the experimental apparatus, or the subject’s physiological state. These artifacts are very strong and frequently found during field experiments. So, when there is a lot of artifacts, the signal quality drops. Recently, artifacts removal has been greatly enhanced by developments in signal processing, which has vastly enhanced the performance. Thus, the proposed review aimed to provide an in-depth analysis of the approaches currently used to analyze data and remove artifacts from physiological signals obtained via WSDs during construction-related tasks. First, this study provides an overview of the physiological signals that are likely to be recorded from construction workers to monitor their health and safety. Second, this review identifies the most prevalent artifacts that have the most detrimental effect on the utility of the signals. Third, a comprehensive review of existing artifact-removal approaches were presented. Fourth, each identified artifact detection and removal approach was analyzed for its strengths and weaknesses. Finally, in conclusion, this review provides a few suggestions for future research for improving the quality of captured physiological signals for monitoring the health and safety of construction workers using artifact removal approaches.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors acknowledged the following two funding grants: (1) General Research Fund (GRF) Grant (15201621) titled “Monitoring and managing fatigue of construction plant and equipment operators exposed to prolonged sitting”; and (2) 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).
Ahn, C. R., S. Lee, C. Sun, H. Jebelli, K. Yang, and B. Choi. 2019. “Wearable sensing technology applications in construction safety and health.” J. Constr. Eng. Manage. 145 (11): 03119007. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001708.
Akhtar, M. T., W. Mitsuhashi, and C. J. James. 2012. “Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data.” Signal Process. 92 (2): 401–416. https://doi.org/10.1016/j.sigpro.2011.08.005.
Al-Jebrni, A. H., B. Chwyl, X. Y. Wang, A. Wong, and B. J. Saab. 2020. “AI-enabled remote and objective quantification of stress at scale.” Biomed. Signal Process. Control 59 (May): 101929. https://doi.org/10.1016/j.bspc.2020.101929.
Allen, J. 2007. “Photoplethysmography and its application in clinical physiological measurement.” Physiol. Meas. 28 (3): R1. https://doi.org/10.1088/0967-3334/28/3/R01.
An, X., and G. K. Stylios. 2020. “Comparison of motion artefact reduction methods and the implementation of adaptive motion artefact reduction in wearable electrocardiogram monitoring.” Sensors 20 (5): 1468. https://doi.org/10.3390/s20051468.
Antczak, K. 2018. “Deep recurrent neural networks for ECG signal denoising.” Preprint, submitted August 4, 2018. https://arxiv.org/abs/1807.11551.
Anton, D., L. D. Shibley, N. B. Fethke, J. Hess, T. M. Cook, and J. Rosecrance. 2001. “The effect of overhead drilling position on shoulder moment and electromyography.” Ergonomics 44 (5): 489–501. https://doi.org/10.1080/00140130120079.
Antwi-Afari, M. F., S. Anwer, W. Umer, H. Y. Mi, Y. Yu, S. Moon, and M. U. Hossain. 2023. “Machine learning-based identification and classification of physical fatigue levels: A novel method based on a wearable insole device.” Int. J. Ind. Ergon. 93 (Jan): 103404. https://doi.org/10.1016/j.ergon.2022.103404.
Antwi-Afari, M. F., H. Li, D. J. Webb, S. Anwer, S. Seo, K. S. Park, and A. Torku. 2021. “Automated recognition of construction workers’ physical fatigue based on foot plantar patterns captured from a wearable insole pressure system.” In Proc., 9th West Africa Built Environment Research (WABER) Conf. Reading, UK: Univ. of Reading.
Antwi-Afari, M. F., Y. Qarout, R. Herzallah, S. Anwer, W. Umer, Y. Zhang, and P. Manu. 2022. “Deep learning-based networks for automated recognition and classification of awkward working postures in construction using wearable insole sensor data.” Autom. Constr. 136 (Aug): 104181. https://doi.org/10.1016/j.autcon.2022.104181.
Anwer, S., H. Li, M. F. Antwi-Afari, W. Umer, I. Mehmood, M. Al-Hussein, and A. Y. L. Wong. 2021a. “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, I. Mehmood, and A. Y. L. Wong. 2022. “Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task.” Eng. Constr. Archit. Manage. 29 (9): 3415–3438. https://doi.org/10.1108/ECAM-03-2021-0245.
Anwer, S., H. Li, M. F. Antwi-Afari, W. Umer, and A. Y. 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.
Anwer, S., H. Li, M. F. Antwi-Afari, W. Umer, and A. Y. L. Wong. 2021b. “Evaluation of physiological metrics as real-time measurement of physical fatigue in construction workers: State-of-the-art review.” J. Constr. Eng. Manage. 147 (5): 03121001. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002038.
Anwer, S., H. Li, W. Umer, M. F. Antwi-Afari, I. Mehmood, Y. Yu, C. Haas, and A. Y. L. Wong. 2023. “Identification and classification of physical fatigue in construction workers using linear and nonlinear heart rate variability measurements.” J. Constr. Eng. Manage. 149 (7): 04023057. https://doi.org/10.1061/JCEMD4.COENG-13100.
Appathurai, A., J. J. Carol, C. Raja, S. N. Kumar, A. V. Daniel, A. J. G. Malar, A. L. Fred, and S. Krishnamoorthy. 2019. “A study on ECG signal characterization and practical implementation of some ECG characterization techniques.” Measurement 147 (Dec): 106384. https://doi.org/10.1016/j.measurement.2019.02.040.
Aryal, A., A. Ghahramani, and B. Becerik-Gerber. 2017. “Monitoring fatigue in construction workers using physiological measurements.” Autom. Constr. 82 (Mar): 154–165. https://doi.org/10.1016/j.autcon.2017.03.003.
Assarroudi, A., F. Heshmati Nabavi, M. R. Armat, A. Ebadi, and M. Vaismoradi. 2018. “Directed qualitative content analysis: The description and elaboration of its underpinning methods and data analysis process.” J. Res. Nurs. 23 (1): 42–55. https://doi.org/10.1177/1744987117741667.
Awolusi, I., E. Marks, and M. Hallowell. 2018. “Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices.” Autom. Constr. 85 (Jan): 96–106. https://doi.org/10.1016/j.autcon.2017.10.010.
Banaei, M., J. Hatami, A. Yazdanfar, and K. Gramann. 2017. “Walking through architectural spaces: The impact of interior forms on human brain dynamics.” Front. Hum. Neurosci. 11 (Sep): 477. https://doi.org/10.3389/fnhum.2017.00477.
Banaei, M., A. Yazdanfar, M. Nooreddin, and A. Yoonessi. 2015. “Enhancing urban trails design quality by using electroencephalography device.” Procedia-Social Behav. Sci. 201 (May): 386–396. https://doi.org/10.1016/j.sbspro.2015.08.191.
Bangaru, S. S., C. Wang, and F. Aghazadeh. 2020. “Data quality and reliability assessment of wearable EMG and IMU sensor for construction activity recognition.” Sensors 20 (18): 5264. https://doi.org/10.3390/s20185264.
Baraldi, P., M. Compare, S. Sauco, and E. Zio. 2013. “Ensemble neural network-based particle filtering for prognostics.” Mech. Syst. Signal Process. 41 (1–2): 288–300. https://doi.org/10.1016/j.ymssp.2013.07.010ff.
Benedek, M., and C. Kaernbach. 2010. “A continuous measure of phasic electrodermal activity.” J. Neurosci. Methods 190 (1): 80–91. https://doi.org/10.1016/j.jneumeth.2010.04.028.
Bengtsson, M. 2016. “How to plan and perform a qualitative study using content analysis.” NursingPlus Open 2 (Jan): 8–14. https://doi.org/10.1016/j.npls.2016.01.001.
Bent, B., B. A. Goldstein, W. A. Kibbe, and J. P. Dunn. 2020. “Investigating sources of inaccuracy in wearable optical heart rate sensors.” npj Digital Med. 3 (1): 18. https://doi.org/10.1038/s41746-020-0226-6.
Böttcher, S., et al. 2022. “Data quality evaluation in wearable monitoring.” Sci. Rep. 12 (1): 21412. https://doi.org/10.1038/s41598-022-25949-x.
Boucsein, W. 2012. Electrodermal activity. New York: Springer.
Bower, I., R. Tucker, and P. G. Enticott. 2019. “Impact of built environment design on emotion measured via neurophysiological correlates and subjective indicators: A systematic review.” J. Environ. Psychol. 66 (Dec): 101344. https://doi.org/10.1016/j.jenvp.2019.101344.
Braithwaite, J., D. Watson, R. Jones, and M. Rowe. 2013. A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments. 2nd ed. Birmingham, UK: Univ. of Birmingham.
Bunce, S. C., M. Izzetoglu, K. Izzetoglu, B. Onaral, and K. Pourrezaei. 2006. “Functional near-infrared spectroscopy.” IEEE Eng. Med. Biol. Mag. 25 (4): 54–62. https://doi.org/10.1109/MEMB.2006.1657788.
Cai, H., et al. 2018. “A pervasive approach to EEG-based depression detection.” Complexity 2018 (May): 1–13. https://doi.org/10.1155/2018/5238028.
Calvin, T. F., A. C. McDonald, and P. J. Keir. 2016. “Adaptations to isolated shoulder fatigue during simulated repetitive work. Part I: Fatigue.” J. Electromyography Kinesiology 29 (Aug): 34–41. https://doi.org/10.1016/j.jelekin.2015.07.003.
Chae, J., S. Hwang, W. Seo, and Y. Kang. 2021. “Relationship between rework of engineering drawing tasks and stress level measured from physiological signals.” Autom. Constr. 124 (Apr): 103560. https://doi.org/10.1016/j.autcon.2021.103560.
Chae, J., and Y. Kang. 2021. “Designing an experiment to measure the alert fatigue of different alarm sounds using the physiological signals.” In Vol. 38 of Proc., Int. Symp. on Automation and Robotics in Construction, 545–552. Oulu, Finland: International Association for Automation and Robotics in Construction.
Chan, A. P., M. C. Yam, J. W. Chung, and W. Yi. 2012. “Developing a heat stress model for construction workers.” J. Facil. Manage. 10 (1): 59–74. https://doi.org/10.1109/TBME.2012.2217959.
Chandra, M., P. Goel, A. Anand, and A. Kar. 2021. “Design and analysis of improved high-speed adaptive filter architectures for ECG signal denoising.” Biomed. Signal Process. Control 63 (May): 102221. https://doi.org/10.1016/j.bspc.2020.102221.
Chen, J., X. Song, and Z. Lin. 2016. “Revealing the ‘Invisible Gorilla’ in construction: Estimating construction safety through mental workload assessment.” Autom. Constr. 63 (Mar): 173–183. https://doi.org/10.1016/j.autcon.2015.12.018.
Chen, J., J. E. Taylor, and S. Comu. 2017. “Assessing task mental workload in construction projects: A novel electroencephalography approach.” J. Constr. Eng. Manage. 143 (8): 04017053. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001345.
Chen, K., D. Zhang, L. Yao, B. Guo, Z. Yu, and Y. Liu. 2021. “Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities.” ACM Comput. Surv. 54 (4): 1–40. https://doi.org/10.1145/3447744.
Chiang, H. S. 2015. “ECG-based mental stress assessment using fuzzy computing and associative petri net.” J. Med. Biol. Eng. 35 (Dec): 833–844. https://doi.org/10.1007/s40846-015-0095-7.
Chiarelli, A. M., E. L. Maclin, M. Fabiani, and G. Gratton. 2015. “A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data.” NeuroImage 112 (May): 128–137. https://doi.org/10.1016/j.neuroimage.2015.02.057.
Cifrek, M., V. Medved, S. Tonković, and S. Ostojić. 2009. “Surface EMG based muscle fatigue evaluation in biomechanics.” Clin. Biomech. 24 (4): 327–340. https://doi.org/10.1016/j.clinbiomech.2009.01.010.
Çınar, S., and N. Acır. 2017. “A novel system for automatic removal of ocular artefacts in EEG by using outlier detection methods and independent component analysis.” Expert Syst. Appl. 68 (Feb): 36–44. https://doi.org/10.1016/j.eswa.2016.10.009.
Cui, X., S. Bray, D. M. Bryant, G. H. Glover, and A. L. Reiss. 2011. “A quantitative comparison of NIRS and fMRI across multiple cognitive tasks.” Neuroimage 54 (4): 2808–2821. https://doi.org/10.1016/j.neuroimage.2010.10.069.
Daly, I., M. Billinger, R. Scherer, and G. Müller-Putz. 2013. “On the automated removal of artifacts related to head movement from the EEG.” IEEE Trans. Neural Syst. Rehabil. Eng. 21 (3): 427–434. https://doi.org/10.1109/TNSRE.2013.2254724.
Debener, S., F. Minow, R. Emkes, K. Gandras, and M. De Vos. 2012. “How about taking a low-cost, small, and wireless EEG for a walk?” Psychophysiology 49 (11): 1617–1621. https://doi.org/10.1111/j.1469-8986.2012.01471.x.
Delorme, A., T. Sejnowski, and S. Makeig. 2007. “Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis.” Neuroimage 34 (4): 1443–1449. https://doi.org/10.1016/j.neuroimage.2006.11.004.
De Luca, C. J., L. D. Gilmore, M. Kuznetsov, and S. H. Roy. 2010. “Filtering the surface EMG signal: Movement artifact and baseline noise contamination.” J. Biomech. 43 (8): 1573–1579. https://doi.org/10.1016/j.jbiomech.2010.01.027.
Djebbara, Z., L. B. Fich, and K. Gramann. 2020. “Architectural affordance impacts human sensorimotor brain dynamics.” Preprint, submitted October 18, 2020. https://arxiv.org/abs/10.1038/s41598-021-82504-w.
Fang, C., B. He, Y. Wang, J. Cao, and S. Gao. 2020. “EMG-centered multisensory based technologies for pattern recognition in rehabilitation: State of the art and challenges.” Biosensors 10 (8): 85. https://doi.org/10.3390/bios10080085.
Formenti, D., A. Merla, and J. I. Priego Quesada. 2017. “The use of infrared thermography in the study of sport and exercise physiology.” In Application of infrared thermography in sports science. Biological and medical physics, biomedical engineering, edited by J. Priego Quesada. Berlin: Springer. https://doi.org/10.1007/978-3-319-47410-6_5.
Gatti, U. C., S. Schneider, and G. C. Migliaccio. 2014. “Physiological condition monitoring of construction workers.” Autom. Constr. 44 (Feb): 227–233. https://doi.org/10.1016/j.autcon.2014.04.013.
Ghosh, A., J. M. M. Torres, M. Danieli, and G. Riccardi. 2015. “Detection of essential hypertension with physiological signals from wearable devices.” In Proc., 37th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), 8095–8098. New York: IEEE.
Gibbs, P., and H. H. Asada. 2005. “Reducing motion artifact in wearable biosensors using MEMS accelerometers for active noise cancellation.” In Proc., 2005 American Control Conf., 1581–1586. New York: IEEE.
Goldsack, J. C., et al. 2020. “Verification, analytical validation, and clinical validation (V3): The foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs).” npj Digital Med. 3 (1): 55. https://doi.org/10.1038/s41746-020-0260-4.
Gregory, A. T., and A. R. Denniss. 2018. “An introduction to writing narrative and systematic reviews—Tasks, tips, and traps for aspiring authors.” Heart Lung Circ. 27 (7): 893–898. https://doi.org/10.1016/j.hlc.2018.03.027.
Grimmer, J., and B. M. Stewart. 2013. “Text as data: The promise and pitfalls of automatic content analysis methods for political texts.” Political Anal. 21 (3): 267–297. https://doi.org/10.1093/pan/mps028.
Guerrero-Mosquera, C., and A. Navia-Vázquez. 2012. “Automatic removal of ocular artefacts using adaptive filtering and independent component analysis for electroencephalogram data.” IET Signal Proc. 6 (2): 99–106. https://doi.org/10.1049/iet-spr.2010.0135.
Guo, C., G. Pleiss, Y. Sun, and K. Q. Weinberger. 2017. “On calibration of modern neural networks.” In Proc., Int. Conf. on Machine Learning, 1321–1330. Cambridge, UK: Proceedings of Machine Learning Research.
Han, G., B. Lin, and Z. Xu. 2017. “Electrocardiogram signal denoising based on empirical mode decomposition technique: An overview.” J. Instrum. 12 (3): P03010. https://doi.org/10.1088/1748-0221/12/03/P03010.
Hayashi, K., Y. Honda, T. Ogawa, N. Kondo, and T. Nishiyasu. 2006. “Relationship between ventilatory response and body temperature during prolonged submaximal exercise.” J. Appl. Physiol. 100 (2): 414–420. https://doi.org/10.1152/japplphysiol.00541.2005.
Hekmatmanesh, A., M. Banaei, K. S. Haghighi, and A. Najafi. 2019. “Bedroom design orientation and sleep electroencephalography signals.” Acta Med. Int. 6 (1): 33. https://doi.org/10.4103/ami.ami_60_18.
Herrero-Fernández, D. 2016. “Psychophysiological, subjective and behavioral differences between high and low anger drivers in a simulation task.” Transp. Res. Part F: Traffic Psychol. Behav. 42 (Oct): 365–375. https://doi.org/10.1016/j.trf.2015.12.015.
Hesar, H. D., and M. Mohebbi. 2016. “ECG denoising using marginalized particle extended Kalman filter with an automatic particle weighting strategy.” IEEE J. Biomed. Health Inf. 21 (3): 635–644. https://doi.org/10.1109/JBHI.2016.2582340.
Holper, L., T. Muehlemann, F. Scholkmann, K. Eng, D. Kiper, and M. Wolf. 2010. “Testing the potential of a virtual reality neurorehabilitation system during performance of observation, imagery and imitation of motor actions recorded by wireless functional near-infrared spectroscopy (fNIRS).” J. Neuroeng. Rehabil. 7 (1): 1–13. https://doi.org/10.1186/1743-0003-7-57.
Hossain, M. S., M. E. Chowdhury, M. B. I. Reaz, S. H. M. Ali, A. A. A. Bakar, S. Kiranyaz, A. Khandakar, M. Alhatou, R. Habib, and M. M. Hossain. 2022. “Motion artifacts correction from single-channel EEG and fNIRS signals using novel wavelet packet decomposition in combination with canonical correlation analysis.” Sensors 22 (9): 3169. https://doi.org/10.3390/s22093169.
Huppert, T. J., S. G. Diamond, M. A. Franceschini, and D. A. Boas. 2009. “HomER: A review of time-series analysis methods for near-infrared spectroscopy of the brain.” Appl. Opt. 48 (10): D280–D298. https://doi.org/10.1364/AO.48.00D280.
Hwang, S., H. Jebelli, B. Choi, M. Choi, and S. Lee. 2018. “Measuring workers’ emotional state during construction tasks using wearable EEG.” J. Constr. Eng. Manage. 144 (7): 04018050. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001506.
Iriarte, J., E. Urrestarazu, M. Valencia, M. Alegre, A. Malanda, C. Viteri, and J. Artieda. 2003. “Independent component analysis as a tool to eliminate artifacts in EEG: A quantitative study.” J. Clin. Neurophysiol. 20 (4): 249–257. https://doi.org/10.1097/00004691-200307000-00004.
Islam, M. K., A. Rastegarnia, and Z. Yang. 2016. “Methods for artifact detection and removal from scalp EEG: A review.” Clin. Neurophysiol. 46 (4–5): 287–305. https://doi.org/10.1016/j.neucli.2016.07.002.
Izzetoglu, K., S. Bunce, B. Onaral, K. Pourrezaei, and B. Chance. 2004. “Functional optical brain imaging using near-infrared during cognitive tasks.” Int. J. Hum.-Comput. Interact. 17 (2): 211–227. https://doi.org/10.1207/s15327590ijhc1702_6.
Izzetoglu, K., A. Devaraj, S. Bunce, and B. Onaral. 2005. “IMotion artifact cancellation in NIR spectroscopy using Wiener filtering.” IEEE Trans. Biomed. Eng. 52 (5): 934–938. https://doi.org/10.1207/s15327590ijhc1702_6.
Izzetoglu, M., P. Chitrapu, S. Bunce, and B. Onaral. 2010. “Motion artifact cancellation in NIR spectroscopy using discrete Kalman filtering.” Biomed. Eng. 9 (Aug): 1–10. https://doi.org/10.1186/1475-925X-9-16.
Jebelli, H., B. Choi, H. Kim, and S. Lee. 2018a. “Feasibility study of a wristband-type wearable sensor to understand construction workers’ physical and mental status.” In Proc., Construction Research Congress 2018, 367–377. Reston, VA: ASCE.
Jebelli, H., B. Choi, and S. Lee. 2019a. “Application of wearable biosensors to construction sites. I: Assessing workers’ stress.” J. Constr. Eng. Manage. 145 (12): 04019079. https://doi.org/10.1061/(ASCE)CO.1943-7862.000172.
Jebelli, H., B. Choi, and S. Lee. 2019b. “Application of wearable biosensors to construction sites. II: Assessing workers’ physical demand.” J. Constr. Eng. Manage. 145 (12): 04019080. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001710.
Jebelli, H., S. Hwang, and S. Lee. 2017. “Feasibility of field measurement of construction workers’ valence using a wearable EEG device.” In Proc., Computing in Civil Engineering 2017, 99–106. Reston, VA: ASCE.
Jebelli, H., S. Hwang, and S. Lee. 2018b. “EEG-based workers’ stress recognition at construction sites.” Autom. Constr. 93 (May): 315–324. https://doi.org/10.1016/j.autcon.2018.05.027.
Jebelli, H., S. Hwang, and S. Lee. 2018c. “EEG signal-processing framework to obtain high-quality brain waves from an off-the-shelf wearable EEG device.” J. Comput. Civ. Eng. 32 (1): 04017070. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000719.
Jeyhani, V., S. Mahdiani, M. Peltokangas, and A. Vehkaoja. 2015. “Comparison of HRV parameters derived from photoplethysmography and electrocardiography signals.” In Proc., 37th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), 5952–5955. New York: IEEE.
Jia, W., A. J. Bandodkar, G. Valdés-Ramírez, J. R. Windmiller, Z. Yang, J. Ramírez, G. Chan, and J. Wang. 2013. “Electrochemical tattoo biosensors for real-time noninvasive lactate monitoring in human perspiration.” Anal. Chem. 85 (14): 6553–6560. https://doi.org/10.1021/ac401573r.
Kang, S., A. Paul, and G. Jeon. 2017. “Reduction of mixed noise from wearable sensors in human-motion estimation.” Comput. Electr. Eng. 61 (Jul): 287–296. https://doi.org/10.1016/j.compeleceng.2017.05.030.
Kappeler-Setz, C., F. Gravenhorst, J. Schumm, B. Arnrich, and G. Tröster. 2013. “Towards long term monitoring of electrodermal activity in daily life.” Pers. Ubiquitous Comput. 17 (2): 261–271. https://doi.org/10.1007/s00779-011-0463-4.
Khan, M. J., and K. S. Hong. 2015. “Passive BCI based on drowsiness detection: An fNIRS study.” Biomed. Opt. Express 6 (10): 4063–4078. https://doi.org/10.1364/BOE.6.004063.
Khiter, A., A. B. Adamou Mitiche, and L. Mitiche. 2020. “Denoising electrocardiogram signal from electromyogram noise using adaptive filter combination.” Rev. d’Intelligence Artificielle 34 (1): 67–74. https://doi.org/10.18280/ria.340109.
Kim, J., M. Yadav, T. Chaspari, and C. R. Ahn. 2020. “Environmental distress and physiological signals: Examination of the saliency detection method.” J. Comput. Civ. Eng. 34 (6): 04020046. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000926.
Kleckner, I. R., R. M. Jones, O. Wilder-Smith, J. B. Wormwood, M. Akcakaya, K. S. Quigley, C. Lord, and M. S. Goodwin. 2017. “Simple, transparent, and flexible automated quality assessment procedures for ambulatory electrodermal activity data.” IEEE Trans. Biomed. Eng. 65 (7): 1460–1467. https://doi.org/10.1109/TBME.2017.2758643.
Lee, G., B. Choi, H. Jebelli, and S. Lee. 2021a. “Assessment of construction workers’ perceived risk using physiological data from wearable sensors: A machine learning approach.” J. Build. Eng. 42 (Feb): 102824. https://doi.org/10.1016/j.jobe.2021.102824.
Lee, M. B., J. K. Kang, H. S. Yoon, and K. R. Park. 2021b. “Enhanced iris recognition method by generative adversarial network-based image reconstruction.” IEEE Access 9 (Jan): 10120–10135. https://doi.org/10.1109/ACCESS.2021.3050788.
Lee, W., K. Y. Lin, E. Seto, and G. C. Migliaccio. 2017. “Wearable sensors for monitoring on-duty and off-duty worker physiological status and activities in construction.” Autom. Constr. 83 (Aug): 341–353. https://doi.org/10.1016/j.autcon.2017.06.012.
Li, D., J. Xu, J. Wang, X. Fang, and Y. Ji. 2020. “A multi-scale fusion convolutional neural network based on attention mechanism for the visualization analysis of EEG signals decoding.” IEEE Trans. Neural Syst. Rehabil. Eng. 28 (12): 2615–2626. https://doi.org/10.1109/TNSRE.2020.3037326.
Li, K. W., R. F. Yu, Y. Gao, R. V. Maikala, and H. H. Tsai. 2009. “Physiological and perceptual responses in male Chinese workers performing combined manual materials handling tasks.” Int. J. Ind. Ergon. 39 (2): 422–427. https://doi.org/10.1016/j.ergon.2008.08.004.
Li, M. A., W. Zhu, H. N. Liu, and J. F. Yang. 2017. “Adaptive feature extraction of motor imagery EEG with optimal wavelet packets and SE-isomap.” Appl. Sci. 7 (4): 390. https://doi.org/10.3390/app7040390.
Li, W. 2018. “Wavelets for electrocardiogram: Overview and taxonomy.” IEEE Access 7 (Nov): 25627–25649. https://doi.org/10.1109/ACCESS.2018.2877793.
Lu, G., J. S. Brittain, P. Holland, J. Yianni, A. L. Green, J. F. Stein, T. Z. Aziz, and S. Wang. 2009a. “Removing ECG noise from surface EMG signals using adaptive filtering.” Neurosci. Lett. 462 (1): 14–19. https://doi.org/10.1016/j.neulet.2009.06.063.
Lu, G., F. Yang, J. Taylor, and J. Stein. 2009b. “A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects.” J. Med. Eng. Technol. 33 (8): 634–641. https://doi.org/10.3109/03091900903150998.
Makeig, S., A. Bell, T. P. Jung, and T. J. Sejnowski. 1995. “Independent component analysis of electroencephalographic data.” Adv. Neural Inf. Process. Syst. 8: 145–151.
Manoilov, P. 2006. “EEG eye-blinking artefacts power spectrum analysis.” In Proc., Int. Conf. Computer Systems and Technology, 15–16. New York: Association for Computing Machinery.
Masood, K., and M. A. Alghamdi. 2019. “Modeling mental stress using a deep learning framework.” IEEE Access 7 (Aug): 68446–68454. https://doi.org/10.1109/ACCESS.2019.2917718.
Matthews, F., B. A. Pearlmutter, T. E. Wards, C. Soraghan, and C. Markham. 2007. “Hemodynamics for brain-computer interfaces.” IEEE Signal Process Mag. 25 (1): 87–94. https://doi.org/10.1109/MSP.2008.4408445.
Mayeli, A., O. Al Zoubi, K. Henry, C. K. Wong, E. J. White, Q. Luo, V. Zotev, H. Refai, J. Bodurka, and Tulsa 1000 Investigators. 2021. “Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI.” J. Neural Eng. 18 (4): 0460b4.
McDonald, A. C., T. F. Calvin, and P. J. Keir. 2016. “Adaptations to isolated shoulder fatigue during simulated repetitive work. Part II: Recovery.” J. Electromyography Kinesiology 29 (May): 42–49. https://doi.org/10.1016/j.jelekin.2015.05.005.
McManus, L., G. De Vito, and M. M. Lowery. 2020. “Analysis and biophysics of surface EMG for physiotherapists and kinesiologists: Toward a common language with rehabilitation engineers.” Front. Neurol. 11 (Oct): 576729. https://doi.org/10.3389/fneur.2020.576729.
Mijović, B., M. De Vos, I. Gligorijević, J. Taelman, and S. Van Huffel. 2010. “Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis.” IEEE Trans. Biomed. Eng. 57 (9): 2188–2196. https://doi.org/10.1109/TBME.2010.2051440.
Miljković, N., N. Popović, O. Djordjević, L. Konstantinović, and T. B. Šekara. 2017. “ECG artifact cancellation in surface EMG signals by fractional order calculus application.” Comput. Methods Programs Biomed. 140 (Mar): 259–264. https://doi.org/10.1016/j.cmpb.2016.12.017.
Munos, B., et al. 2016. “Mobile health: The power of wearables, sensors, and apps to transform clinical trials.” Ann. N. Y. Acad. Sci. 1375 (1): 3–18. https://doi.org/10.1111/nyas.13117.
Nath, N. D., T. Chaspari, and A. H. Behzadan. 2018. “Automated ergonomic risk monitoring using body-mounted sensors and machine learning.” Adv. Eng. Inf. 38 (Jun): 514–526. https://doi.org/10.1016/j.aei.2018.08.020.
Nathan, V., and R. Jafari. 2017. “Particle filtering and sensor fusion for robust heart rate monitoring using wearable sensors.” IEEE J. Biomed. Health Inf. 22 (6): 1834–1846. https://doi.org/10.1109/JBHI.2017.2783758.
Newton, S. 2022. “Measuring the perceptual, physiological and environmental factors that impact stress in the construction industry.” Constr. Innovation. https://doi.org/10.1108/CI-02-2022-0040.
Nguyen, H. D., S. H. Yoo, M. R. Bhutta, and K. S. Hong. 2018. “Adaptive filtering of physiological noises in fNIRS data.” Biomed. Eng. Online 17 (Dec): 1–23. https://doi.org/10.1186/s12938-018-0613-2.
Nicolò, A., I. Bazzucchi, J. Haxhi, F. Felici, and M. Sacchetti. 2014. “Comparing continuous and intermittent exercise: An ‘Isoeffort’ and ‘Isotime’ approach.” PLoS One 9 (4): e94990. https://doi.org/10.1371/journal.pone.0094990.
Nicolò, A., S. M. Marcora, I. Bazzucchi, and M. Sacchetti. 2017a. “Differential control of respiratory frequency and tidal volume during high-intensity interval training.” Exp. Physiol. 102 (8): 934–949. https://doi.org/10.1113/EP086352.
Nicolò, A., S. M. Marcora, and M. Sacchetti. 2016a. “Respiratory frequency is strongly associated with perceived exertion during time trials of different duration.” J. Sports Sci. 34 (13): 1199–1206. https://doi.org/10.1080/02640414.2015.1102315.
Nicolò, A., C. Massaroni, and L. Passfield. 2017b. “Respiratory frequency during exercise: The neglected physiological measure.” Front. Physiol. 8 (Dec): 922. https://doi.org/10.3389/fphys.2017.00922.
Nicolò, A., L. Passfield, and M. Sacchetti. 2016b. “Investigating the effect of exercise duration on functional and biochemical perturbations in the human heart: Total work or ‘isoeffort’ matching?” J. Physiol. 594 (11): 3157. https://doi.org/10.1113/JP272421.
Niu, Y., D. Wang, Z. Wang, F. Sun, K. Yue, and N. Zheng. 2019. “User experience evaluation in virtual reality based on subjective feelings and physiological signals.” J. Imaging Sci. Technol. 63 (6): 60413. https://doi.org/10.2352/J.ImagingSci.Technol.2019.63.6.060413.
Nnaji, C., I. Awolusi, J. Park, and A. Albert. 2021. “Wearable sensing devices: Towards the development of a personalized system for construction safety and health risk mitigation.” Sensors 21 (3): 682. https://doi.org/10.3390/s21030682.
Nyein, H. Y. Y., et al. 2016. “A wearable electrochemical platform for noninvasive simultaneous monitoring of and pH.” ACS Nano 10 (7): 7216–7224. https://doi.org/10.1021/acsnano.6b04005.
Pal, D., V. Vanijja, C. Arpnikanondt, X. Zhang, and B. Papasratorn. 2019. “A quantitative approach for evaluating the quality of experience of smart wearables from the quality of data and quality of information: An end user perspective.” IEEE Access 7 (Jun): 64266–64278. https://doi.org/10.1109/ACCESS.2019.2917061.
Phadikar, S., N. Sinha, and R. Ghosh. 2020. “A survey on feature extraction methods for EEG based emotion recognition.” In Vol. 1 of Proc., 1st Int. Conf. on Innovations in Modern Science and Technology, Intelligent Techniques and Applications in Science and Technology, 31–45. New York: Springer.
Phadikar, S., N. Sinha, R. Ghosh, and E. Ghaderpour. 2022. “Automatic muscle artifacts identification and removal from single-channel EEG using wavelet transform with meta-heuristically optimized non-local means filter.” Sensors 22 (8): 2948.
Picard, R. W., S. Fedor, and Y. Ayzenberg. 2016. “Multiple arousal theory and daily-life electrodermal activity asymmetry.” Emotion Rev. 8 (1): 62–75. https://doi.org/10.1177/1754073914565517.
Poh, M.-Z., N. C. Swenson, and R. W. Picard. 2010. “A wearable sensor for unobtrusive, long-term assessment of electrodermal activity.” IEEE Trans. Biomed. Eng. 57 (5): 1243–1252. https://doi.org/10.1109/TBME.2009.2038487.
Porr, B., S. Daryanavard, L. M. Bohollo, H. Cowan, and R. Dahiya. 2022. “Real-time noise cancellation with deep learning.” PLoS One 17 (11): e0277974. https://doi.org/10.1371/journal.pone.0277974.
Pourmohammadi, S., and A. Maleki. 2020. “Stress detection using ECG and EMG signals: A comprehensive study.” Comput. Methods Programs Biomed. 193 (May): 105482. https://doi.org/10.1016/j.cmpb.2020.105482.
Prakash, S., A. K. Manocha, and M. Singh. 2021. “A study on artifacts removal from physiological signals.” In Proc., 6th Int. Conf. on Signal Processing, Computing and Control (ISPCC), 15–20. New York: IEEE.
Quaresima, V., and M. Ferrari. 2019. “Functional near-infrared spectroscopy (fNIRS) for assessing cerebral cortex function during human behavior in natural/social situations: A concise review.” Organ. Res. Methods 22 (1): 46–68. https://doi.org/10.1177/1094428116658959.
Quiñones-Grueiro, M., A. Prieto-Moreno, C. Verde, and O. Llanes-Santiago. 2019. “Data-driven monitoring of multimode continuous processes: A review.” Chemom. Intell. Lab. Syst. 189 (Jun): 56–71. https://doi.org/10.1016/j.chemolab.2019.03.012.
Ram, M. R., K. V. Madhav, E. H. Krishna, N. R. Komalla, and K. A. Reddy. 2011. “A novel approach for motion artifact reduction in PPG signals based on AS-LMS adaptive filter.” IEEE Trans. Instrum. Meas. 61 (5): 1445–1457. https://doi.org/10.1109/TIM.2011.2175832.
Rheinberger, K., T. Steinberger, K. Unterkofler, M. Baubin, A. Klotz, and A. Amann. 2007. “Removal of CPR artifacts from the ventricular fibrillation ECG by adaptive regression on lagged reference signals.” IEEE Trans. Biomed. Eng. 55 (1): 130–137. https://doi.org/10.1109/TBME.2007.902235.
Robertson, F. C., T. S. Douglas, and E. M. Meintjes. 2010. “Motion artifact removal for functional near infrared spectroscopy: A comparison of methods.” IEEE Trans. Biomed. Eng. 57 (6): 1377–1387. https://doi.org/10.1109/TBME.2009.2038667.
Roy, V., S. Shukla, P. K. Shukla, and P. Rawat. 2017. “Gaussian elimination-based novel canonical correlation analysis method for EEG motion artifact removal.” J. Healthcare Eng. 2017 (Oct): 9674712. https://doi.org/10.1155/2017/9674712.
Sameni, R., M. B. Shamsollahi, C. Jutten, and G. D. Clifford. 2007. “A nonlinear Bayesian filtering framework for ECG denoising.” IEEE Trans. Biomed. Eng. 54 (12): 2172–2185. https://doi.org/10.1109/TBME.2007.897817.
Sanei, S., and J. A. Chambers. 2013. EEG signal processing. New York: Wiley.
Sangani, S., A. Lamontagne, and J. Fung. 2015. “Cortical mechanisms underlying sensorimotor enhancement promoted by walking with haptic inputs in a virtual environment.” Prog. Brain Res. 218 (Sep): 313–330. https://doi.org/10.1016/bs.pbr.2014.12.003.
Schmidt-Daffy, M. 2013. “Fear and anxiety while driving: Differential impact of task demands, speed and motivation.” Transp. Res. Part F: Traffic Psychol. Behav. 16 (Jan): 14–28. https://doi.org/10.1016/j.trf.2012.07.002.
Shayesteh, S., A. Ojha, Y. Liu, and H. Jebelli. 2023. “Human-robot teaming in construction: Evaluative safety training through the integration of immersive technologies and wearable physiological sensing.” Saf. Sci. 159 (Aug): 106019. https://doi.org/10.1016/j.ssci.2022.106019.
Sweeney, K. T., H. Ayaz, T. E. Ward, M. Izzetoglu, S. F. McLoone, and B. Onaral. 2012a. “A methodology for validating artifact removal techniques for physiological signals.” IEEE Trans. Inf. Technol. Biomed. 16 (5): 918–926. https://doi.org/10.1109/TITB.2012.2207400.
Sweeney, K. T., S. F. McLoone, and T. E. Ward. 2012b. “The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique.” IEEE Trans. Biomed. Eng. 60 (1): 97–105. https://doi.org/10.1109/TBME.2012.2225427.
Sweeney, K. T., T. E. Ward, and S. F. McLoone. 2012c. “Artifact removal in physiological signals—Practices and possibilities.” IEEE Trans. Inf. Technol. Biomed. 16 (3): 488–500. https://doi.org/10.1109/TITB.2012.2188536.
Tripathi, P. M., A. Kumar, R. Komaragiri, and M. Kumar. 2021. “A review on computational methods for denoising and detecting ECG signals to detect cardiovascular diseases.” Arch. Comput. Methods Eng. 29 (Oct): 1875–1914. https://doi.org/10.1007/s11831-021-09642-2.
Ueno, S., Y. Sakakibara, N. Hisanaga, T. Oka, and S. Yamaguchi-Sekino. 2018. “Heat strain and hydration of Japanese construction workers during work in summer.” Ann. Work Exposures Health 62 (5): 571–582. https://doi.org/10.1093/annweh/wxy012.
Umer, W., H. Li, Y. Yantao, M. F. Antwi-Afari, S. Anwer, and X. Luo. 2020. “Physical exertion modeling for construction tasks using combined cardiorespiratory and thermoregulatory measures.” Autom. Constr. 112 (Jun): 103079. https://doi.org/10.1016/j.autcon.2020.103079.
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 (Aug): 103301. https://doi.org/10.1016/j.ergon.2022.103301.
Urigüen, J. A., and B. Garcia-Zapirain. 2015. “EEG artifact removal-state-of-the-art and guidelines.” J. Neural Eng. 12 (3): 031001. https://doi.org/10.1088/1741-2560/12/3/031001.
Villeneuve, E., W. Harwin, W. Holderbaum, R. S. Sherratt, and R. White. 2016. “Signal quality and compactness of a dual-accelerometer system for gyro-free human motion analysis.” IEEE Sens. J. 16 (16): 6261–6269. https://doi.org/10.1109/JSEN.2016.2582262.
Wirawan, I. M. A., R. Wardoyo, and D. Lelono. 2022. “The challenges of emotion recognition methods based on electroencephalogram signals: A literature review.” Int. J. Electr. Comput. Eng. 12 (2): 1508. https://doi.org/10.11591/ijece.v12i2.pp1508-1519.
Wong, D. P. L., J. W. Y. Chung, A. P. C. Chan, F. K. W. Wong, and W. Yi. 2014. “Comparing the physiological and perceptual responses of construction workers (bar benders and bar fixers) in a hot environment.” Appl. Ergon. 45 (6): 1705–1711. https://doi.org/10.1016/j.apergo.2014.06.002.
Xu, Y., I. Hübener, A. K. Seipp, S. Ohly, and K. David. 2017. “From the lab to the real-world: An investigation on the influence of human movement on emotion recognition using physiological signals.” In Proc., IEEE Int. Conf. on Pervasive Computing and Communications Workshops (PerCom Workshops), 345–350. New York: IEEE.
Yang, Z. M., H. J. Wu, C. N. Li, and Y. H. Shao. 2016. “Least squares recursive projection twin support vector machine for multi-class classification.” Int. J. Mach. Learn. Cybern. 7 (3): 411–426. https://doi.org/10.1007/s13042-015-0394-x.
Yin, P., L. Yang, C. Wang, and S. Qu. 2019. “Effects of wearable power assist device on low back fatigue during repetitive lifting tasks.” Clin. Biomech. 70 (Aug): 59–65. https://doi.org/10.1016/j.clinbiomech.2019.07.023.
Zhan, Y., S. Guo, K. M. Kendrick, and J. Feng. 2009. “Filtering noise for synchronised activity in multi-trial electrophysiology data using Wiener and Kalman filters.” BioSystems 96 (1): 1–13. https://doi.org/10.1016/j.biosystems.2008.11.007.
Zhang, Y., M. Zhang, and Q. Fang. 2019. “Scoping review of EEG studies in construction safety.” Int. J. Environ. Res. Public Health 16 (21): 4146. https://doi.org/10.3390/ijerph16214146.
Zhang, Z., T. P. Jung, S. Makeig, and B. D. Rao. 2012. “Compressed sensing of EEG for wireless telemonitoring with low energy consumption and inexpensive hardware.” IEEE Trans. Biomed. Eng. 60 (1): 221–224. https://doi.org/10.1109/TBME.2012.2217959.
Zhou, X., Y. Hu, P. C. Liao, and D. Zhang. 2021. “Hazard differentiation embedded in the brain: A near-infrared spectroscopy-based study.” Autom. Constr. 122 (Jun): 103473. https://doi.org/10.1016/j.autcon.2020.103473.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 150 • Issue 1 • January 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Published online: Oct 25, 2023
Published in print: Jan 1, 2024
Discussion open until: Mar 25, 2024
ASCE Technical Topics:
- Analysis (by type)
- Business management
- Construction engineering
- Construction equipment
- Construction management
- Construction sites
- Data analysis
- Employment
- Engineering fundamentals
- Equipment and machinery
- Labor
- Measurement (by type)
- Methodology (by type)
- Occupational safety
- Personnel management
- Practice and Profession
- Public administration
- Public health and safety
- Research methods (by type)
- Safety
- Sensors and sensing
- Signal processing
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.