{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T14:26:06Z","timestamp":1768314366412,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T00:00:00Z","timestamp":1659744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National funds through the FCT\/MCTES (PIDDAC)","award":["UIDB\/04708\/2020"],"award-info":[{"award-number":["UIDB\/04708\/2020"]}]},{"name":"National funds through the FCT\/MCTES (PIDDAC)","award":["NORTE-06-3559-FSE-000176"],"award-info":[{"award-number":["NORTE-06-3559-FSE-000176"]}]},{"name":"European Social Fund (ESF), through the North Portugal Regional Operational Programme (Norte 2020)","award":["UIDB\/04708\/2020"],"award-info":[{"award-number":["UIDB\/04708\/2020"]}]},{"name":"European Social Fund (ESF), through the North Portugal Regional Operational Programme (Norte 2020)","award":["NORTE-06-3559-FSE-000176"],"award-info":[{"award-number":["NORTE-06-3559-FSE-000176"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Buildings"],"abstract":"<jats:p>The domain of data processing is essential to accelerate the delivery of information based on electronic performance monitoring (EPM). The classification of the activities conducted by craft workers can enhance the mechanisation and productivity of activities. However, research in this field is mainly based on simulations of binary activities (i.e., performing or not performing an action). To enhance EPM research in this field, a dynamic laboratory circuit-based simulation of ten common constructions activities was performed. A circuit feasibility case study of EPM using wearable devices was conducted, where two different data processing approaches were tested: machine learning and multivariate statistical analysis (MSA). Using the acceleration data of both wrists and the dominant leg, the machine-learning approach achieved an accuracy between 92 and 96%, while MSA achieved 47\u201376%. Additionally, the MSA approach achieved 32\u201376% accuracy by monitoring only the dominant wrist. Results highlighted that the processes conducted with manual tools (e.g., hammering and sawing) have prominent dominant-hand motion characteristics that are accurately detected with one wearable. However, free-hand performing (masonry), walking and not operating value (e.g., sitting) require more motion analysis data points, such as wrists and legs.<\/jats:p>","DOI":"10.3390\/buildings12081174","type":"journal-article","created":{"date-parts":[[2022,8,7]],"date-time":"2022-08-07T21:03:50Z","timestamp":1659906230000},"page":"1174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Construction Tasks Electronic Process Monitoring: Laboratory Circuit-Based Simulation Deployment"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9893-0377","authenticated-orcid":false,"given":"Diego","family":"Calvetti","sequence":"first","affiliation":[{"name":"CONSTRUCT\/GEQUALTEC, Construction Institute, Faculty of Engineering, Porto University, 4200-465 Porto, Portugal"}]},{"given":"Lu\u00eds","family":"Sanhudo","sequence":"additional","affiliation":[{"name":"BUILT CoLAB\u2014Digital Built Environment, 4150-171 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4380-5530","authenticated-orcid":false,"given":"Pedro","family":"M\u00eada","sequence":"additional","affiliation":[{"name":"CONSTRUCT\/GEQUALTEC, Construction Institute, Faculty of Engineering, Porto University, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9878-3792","authenticated-orcid":false,"given":"Jo\u00e3o Po\u00e7as","family":"Martins","sequence":"additional","affiliation":[{"name":"CONSTRUCT\/GEQUALTEC, Faculty of Engineering, Porto University, 4200-465 Porto, Portugal"}]},{"given":"Miguel Chichorro","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"CONSTRUCT\/GEQUALTEC, Faculty of Engineering, Porto University, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8335-0898","authenticated-orcid":false,"given":"Hip\u00f3lito","family":"Sousa","sequence":"additional","affiliation":[{"name":"CONSTRUCT\/GEQUALTEC, Faculty of Engineering, Porto University, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,6]]},"reference":[{"key":"ref_1","unstructured":"Barbosa, F., Woetzel, J., and Mischke, J. (2017). Reinventing Construction: A Route to Higher Productivity, McKinsey Global Institute."},{"key":"ref_2","unstructured":"Farmer, M. (2016). The Farmer Review of the UK Construction Labour Model: Modernise or Die, Construction Leadership Council."},{"key":"ref_3","unstructured":"Desruelle, P., Baldini, G., Barboni, M., Bono, F., Delipetrev, B., Duch Brown, N., Fernandez Macias, E., Gkoumas, K., Joossens, E., and Kalpaka, A. (2019). Digital Transformation in Transport, Construction, Energy, Government and Public Administration, Publications Office of the European Union."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1177\/0893318997010003001","article-title":"Electronic Performance Monitoring","volume":"10","author":"Alder","year":"1997","journal-title":"Manag. Commun. Q."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Calvetti, D., M\u00eada, P., Gon\u00e7alves, M.C., and Sousa, H. (2020). Worker 4.0: The Future of Sensored Construction Sites. Buildings, 10.","DOI":"10.3390\/buildings10100169"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1108\/ECAM-04-2017-0066","article-title":"Digital skin of the construction site: Smart sensor technologies towards the future smart construction site","volume":"26","author":"Edirisinghe","year":"2019","journal-title":"Eng. Constr. Archit. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.obhdp.2005.03.003","article-title":"An examination of the effect of computerized performance monitoring feedback on monitoring fairness, performance, and satisfaction","volume":"97","author":"Alder","year":"2005","journal-title":"Organ. Behav. Hum. Decis. Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/S1047-8310(01)00042-6","article-title":"Employee reactions to electronic performance monitoring: A consequence of organizational culture","volume":"12","author":"Alder","year":"2001","journal-title":"J. High Technol. Manag. Res."},{"key":"ref_9","unstructured":"U.S. Congress Office of Technology Assessment (1987). The Electronic Supervisor: New Technology, New Tensions."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/0003-6870(92)90004-F","article-title":"Electronic performance monitoring (EPM)","volume":"23","author":"Schleifer","year":"1992","journal-title":"Appl. Ergon."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.aei.2015.01.011","article-title":"Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future","volume":"29","author":"Yang","year":"2015","journal-title":"Adv. Eng. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1061\/(ASCE)0733-9364(1986)112:1(90)","article-title":"Work Sampling Can Predict Unit Rate Productivity","volume":"112","author":"Liou","year":"1986","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"781","DOI":"10.30638\/eemj.2021.073","article-title":"Labour productivity as a means for assessing environmental impact in the construction industry","volume":"20","author":"Calvetti","year":"2021","journal-title":"Environ. Eng. Manag. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.ijproman.2007.06.003","article-title":"Major factors influencing productivity of water and wastewater treatment plant construction: Evidence from the deep south USA","volume":"26","author":"Mojahed","year":"2008","journal-title":"Int. J. Proj. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e14","DOI":"10.1017\/dce.2020.16","article-title":"Construction with digital twin information systems","volume":"1","author":"Sacks","year":"2020","journal-title":"Data-Centric Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"M\u00eada, P., Calvetti, D., Hjelseth, E., and Sousa, H. (2021). Incremental Digital Twin Conceptualisations Targeting Data-Driven Circular Construction. Buildings, 11.","DOI":"10.3390\/buildings11110554"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zheng, X., Wang, M., and Ordieres-Mer\u00e9, J. (2018). Comparison of data preprocessing approaches for applying deep learning to human activity recognition in the context of industry 4.0. Sensors, 18.","DOI":"10.3390\/s18072146"},{"key":"ref_18","unstructured":"Ryu, J., Seo, J., Liu, M., Lee, S., and Haas, C.T. (June, January 31). Action Recognition Using a Wristband-Type Activity Tracker: Case Study of Masonry Work. Proceedings of the Construction Research Congress, San Juan, Puerto Rico."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"04018114","DOI":"10.1061\/(ASCE)CO.1943-7862.0001579","article-title":"Automated Action Recognition Using an Accelerometer-Embedded Wristband-Type Activity Tracker","volume":"145","author":"Ryu","year":"2019","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bao, L., and Intille, S.S. (2004, January 21\u201323). Activity recognition from user-annotated acceleration data. Proceedings of the Second International Conference, PERVASIVE 2004, Vienna, Austria.","DOI":"10.1007\/978-3-540-24646-6_1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1108\/IJPPM-05-2013-0099","article-title":"Automated recognition of construction labour activity using accelerometers in field situations","volume":"63","author":"Joshua","year":"2014","journal-title":"Int. J. Product. Perform. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Akhavian, R., and Behzadan, A. (2015, January 6\u20139). Wearable sensor-based activity recognition for data-driven simulation of construction workers\u2019 activities. Proceedings of the Winter Simulation Conference, Huntington Beach, CA, USA.","DOI":"10.1109\/WSC.2015.7408495"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.autcon.2016.08.015","article-title":"Smartphone-based construction workers\u2019 activity recognition and classification","volume":"71","author":"Akhavian","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_24","unstructured":"Akhavian, R., Brito, L., and Behzadan, A. (2015, January 2\u20133). Integrated Mobile Sensor-Based Activity Recognition of Construction Equipment and Human Crews. Proceedings of the Conference on Autonomous and Robotic Construction of Infrastructure, Ames, IA, USA."},{"key":"ref_25","first-page":"1","article-title":"Coupling human activity recognition and wearable sensors for data-driven construction simulation","volume":"23","author":"Akhavian","year":"2018","journal-title":"J. Inf. Technol. Constr."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, M., Chen, S., Zhao, X., and Yang, Z. (2018). Research on construction workers\u2019 activity recognition based on smartphone. Sensors, 18.","DOI":"10.3390\/s18082667"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sztyler, T., and Stuckenschmidt, H. (2016, January 14\u201319). On-body localization of wearable devices: An investigation of position-aware activity recognition. Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016, Sydney, NSW, Australia.","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1016\/j.promfg.2018.07.152","article-title":"Worker Activity Recognition in Smart Manufacturing Using IMU and sEMG Signals with Convolutional Neural Networks","volume":"26","author":"Tao","year":"2018","journal-title":"Procedia Manuf."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, K., Jebelli, H., Ahn, C.R., and Vuran, M.C. (2015, January 21\u201323). Threshold-Based Approach to Detect Near-Miss Falls of Iron-Workers Using Inertial Measurement Units. Proceedings of the Computing in Civil Engineering 2015, Austin, TX, USA.","DOI":"10.1061\/9780784479247.019"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.autcon.2016.04.007","article-title":"Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit","volume":"68","author":"Yang","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Joshua, L., and Varghese, K. (2012, January 21\u201323). Classification of bricklaying activities in work sampling categories using accelerometers. Proceedings of the Construction Research Congress 2012: Construction Challenges in a Flat World, West Lafayette, IN, USA.","DOI":"10.1061\/9780784412329.093"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102001","DOI":"10.1016\/j.jobe.2020.102001","article-title":"Activity Classification using Accelerometers and Machine Learning for Complex Construction Worker Activities","volume":"35","author":"Sanhudo","year":"2021","journal-title":"J. Build. Eng."},{"key":"ref_34","unstructured":"Freivalds, A. (2009). Niebel\u2019s Methods, Standards, and Work Design, Mcgraw-Hill Higher Education."},{"key":"ref_35","unstructured":"Meyers, F.E., and Stewart, J.R. (2002). Motion and Time Study for Lean Manufacturing, Prentice Hall. [3rd ed.]."},{"key":"ref_36","unstructured":"Groover, M.P. (2007). Work Systems and the Methods, Measurement, and Management of Work, Pearson Prentice Hall."},{"key":"ref_37","unstructured":"Adrian, J.J. (2004). Construction Productivity: Measurement and Improvement, Stipes Publishing."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"49","DOI":"10.24840\/2183-6493_004.002_0005","article-title":"Agile Methodology to Performance Measure and Identification of Impact Factors in the Labour Productivity of Industrial Workers","volume":"4","author":"Calvetti","year":"2018","journal-title":"U.Porto J. Eng."},{"key":"ref_39","unstructured":"Akhavian, R., and Behzadan, A.H. (2016, January 6\u20138). Productivity Analysis of Construction Worker Activities Using Smartphone Sensors. Proceedings of the 16th International Conference on Computing in Civil and Building Engineering (ICCCBE), Osaka, Japan."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1061\/(ASCE)CP.1943-5487.0000097","article-title":"Accelerometer-based activity recognition in construction","volume":"25","author":"Joshua","year":"2011","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bangaru, S.S., Wang, C., and Aghazadeh, F. (2020). Data quality and reliability assessment of wearable emg and IMU sensor for construction activity recognition. Sensors, 20.","DOI":"10.3390\/s20185264"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"16","DOI":"10.24840\/2183-6493_004.002_0002","article-title":"Multivariate Statistical Analysis Approach to Cluster Construction Workers based on Labor Productivity Performance","volume":"4","author":"Calvetti","year":"2018","journal-title":"U.Porto J. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.eswa.2018.03.056","article-title":"Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges","volume":"105","author":"Nweke","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.aei.2018.08.020","article-title":"Automated ergonomic risk monitoring using body-mounted sensors and machine learning","volume":"38","author":"Nath","year":"2018","journal-title":"Adv. Eng. Inform."},{"key":"ref_45","unstructured":"Marr, B. (2020, October 22). Artificial Intelligence: What\u2019s the Difference Between Deep Learning and Reinforcement Learning?. Available online: https:\/\/www.forbes.com\/sites\/bernardmarr\/2018\/10\/22\/artificial-intelligence-whats-the-difference-between-deep-learning-and-reinforcement-learning\/#27bf005f271e."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1111\/ced.14029","article-title":"Artificial intelligence, machine learning and deep learning: Definitions and differences","volume":"45","author":"Jakhar","year":"2020","journal-title":"Clin. Exp. Dermatol."},{"key":"ref_47","unstructured":"Mar\u00f4co, J. (2011). An\u00e1lise Estat\u00edstica com o SPSS Statistics, ReportNumber, Lda."}],"container-title":["Buildings"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-5309\/12\/8\/1174\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:05:03Z","timestamp":1760141103000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-5309\/12\/8\/1174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,6]]},"references-count":47,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["buildings12081174"],"URL":"https:\/\/doi.org\/10.3390\/buildings12081174","relation":{},"ISSN":["2075-5309"],"issn-type":[{"value":"2075-5309","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,6]]}}}