{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T02:16:52Z","timestamp":1772677012574,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T00:00:00Z","timestamp":1651536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korean government (MSIT)","award":["2019R1G1A1100365"],"award-info":[{"award-number":["2019R1G1A1100365"]}]},{"name":"Korean government (MSIT)","award":["21CTAP\u2013C163631-01"],"award-info":[{"award-number":["21CTAP\u2013C163631-01"]}]},{"name":"Ministry of Land, Infrastructure and Transport","award":["2019R1G1A1100365"],"award-info":[{"award-number":["2019R1G1A1100365"]}]},{"name":"Ministry of Land, Infrastructure and Transport","award":["21CTAP\u2013C163631-01"],"award-info":[{"award-number":["21CTAP\u2013C163631-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The construction industry experiences the highest rate of casualties from safety-related accidents at construction sites despite continuous social interest in safety management. Accordingly, various studies have been conducted on safety management, wherein recent studies have focused on its integration with Machine Learning (ML). In this study, we proposed a technology for recognizing struck-by hazards between construction equipment and workers, where a Convolutional Neural Network (CNN) and sound recognition were combined to analyze the changes in the Doppler effect caused by the movements of a subject. An experiment was conducted to evaluate the recognition performance in indoor and outdoor environments with respect to movement state, direction, speed, and near-miss situations. The proposed technology was able to classify the movement direction and speed with 84.4\u201397.4% accuracy and near-misses with 78.9% accuracy. This technology can be implemented using data obtained through the microphone of a smartphone, thus it is highly applicable and is also effective at ensuring that a worker becomes aware of a struck-by hazard near construction equipment. The findings of this study are expected to be applicable for the prevention of struck-by accidents occurring in various forms at construction sites in the vicinity of construction equipment.<\/jats:p>","DOI":"10.3390\/s22093482","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T08:26:35Z","timestamp":1651566395000},"page":"3482","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Mobile Device-Based Struck-By Hazard Recognition in Construction Using a High-Frequency Sound"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4961-5906","authenticated-orcid":false,"given":"Jaehoon","family":"Lee","sequence":"first","affiliation":[{"name":"School of Architecture, College of Engineering, Chonnam National University, Gwangju 61186, Korea"}]},{"given":"Kanghyeok","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Architecture, College of Engineering, Chonnam National University, Gwangju 61186, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,3]]},"reference":[{"key":"ref_1","unstructured":"SafeWork, I.L.O. (2005). Global Estimates of Fatal Work Related Diseases and Occupational Accidents, World Bank Regions."},{"key":"ref_2","unstructured":"Bureau of Labor Statistics (2022, May 02). National census of fatal occupational injuries in 2019. U.S. Department of Labor, Washington, DC, Available online: https:\/\/www.bls.gov\/news.release\/archives\/cfoi_12162020.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"04017121","DOI":"10.1061\/(ASCE)CO.1943-7862.0001433","article-title":"Fatal Construction Accidents in Hong Kong","volume":"144","author":"Chiang","year":"2018","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103613","DOI":"10.1016\/j.autcon.2021.103613","article-title":"Tactile-Based Wearable System for Improved Hazard Perception of Worker and Equipment Collision","volume":"125","author":"Sakhakarmi","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"04016039","DOI":"10.1061\/(ASCE)CO.1943-7862.0001118","article-title":"A Cognitive Model of Construction Workers\u2019 Unsafe Behaviors","volume":"142","author":"Fang","year":"2016","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.autcon.2016.03.008","article-title":"Heat Map Generation for Predictive Safety Planning: Preventing Struck-by and near Miss Interactions between Workers-on-Foot and Construction Equipment","volume":"71","author":"Golovina","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1061\/(ASCE)CO.1943-7862.0000438","article-title":"Image-Based Safety Assessment: Automated Spatial Safety Risk Identification of Earthmoving and Surface Mining Activities","volume":"138","author":"Chi","year":"2012","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"05016019","DOI":"10.1061\/(ASCE)CO.1943-7862.0001223","article-title":"Framework of Automated Construction-Safety Monitoring Using Cloud-Enabled BIM and BLE Mobile Tracking Sensors","volume":"143","author":"Park","year":"2017","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1111\/mice.12536","article-title":"Computer Vision-based Recognition of 3D Relationship between Construction Entities for Monitoring Struck-by Accidents","volume":"35","author":"Yan","year":"2020","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"04015075","DOI":"10.1061\/(ASCE)CP.1943-5487.0000562","article-title":"Vision-Based Object-Centric Safety Assessment Using Fuzzy Inference: Monitoring Struck-by Accidents with Moving Objects","volume":"30","author":"Kim","year":"2016","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.autcon.2019.01.018","article-title":"Adaptive Computer Vision-Based 2D Tracking of Workers in Complex Environments","volume":"103","author":"Konstantinou","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"04016023","DOI":"10.1061\/(ASCE)CP.1943-5487.0000573","article-title":"Visual Tracking of Construction Jobsite Workforce and Equipment with Particle Filtering","volume":"30","author":"Zhu","year":"2016","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Teizer, J., Lao, D., and Sofer, M. (2007, January 19\u201321). Rapid Automated Monitoring of Construction Site Activities Using Ultra-Wideband. Proceedings of the 24th International Symposium on Automation and Robotics in Construction, Kochi, Kerala, India.","DOI":"10.22260\/ISARC2007\/0008"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.compind.2018.03.012","article-title":"Towards an Automated Photogrammetry-Based Approach for Monitoring and Controlling Construction Site Activities","volume":"98","author":"Omar","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103177","DOI":"10.1016\/j.autcon.2020.103177","article-title":"Human Activity Classification Based on Sound Recognition and Residual Convolutional Neural Network","volume":"114","author":"Jung","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jiang, X., Cao, R., and Wang, X. (2015). Robust Indoor Human Activity Recognition Using Wireless Signals. Sensors, 15.","DOI":"10.3390\/s150717195"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"04015049","DOI":"10.1061\/(ASCE)CO.1943-7862.0001031","article-title":"Performance Test of Wireless Technologies for Personnel and Equipment Proximity Sensing in Work Zones","volume":"142","author":"Park","year":"2016","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1080\/01446193.2013.783705","article-title":"Method for Testing Proximity Detection and Alert Technology for Safe Construction Equipment Operation","volume":"31","author":"Marks","year":"2013","journal-title":"Constr. Manag. Econ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.autcon.2017.08.025","article-title":"Improving Dynamic Proximity Sensing and Processing for Smart Work-Zone Safety","volume":"84","author":"Park","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xie, Y., Lee, Y.-C., Shariatfar, M., Zhang, Z.D., Rashidi, A., and Lee, H.W. (2019). Historical Accident and Injury Database-Driven Audio-Based Autonomous Construction Safety Surveillance. Computing in Civil Engineering 2019: Data, Sensing and Analytics, American Society of Civil Engineers.","DOI":"10.1061\/9780784482438.014"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cho, C., Lee, Y.-C., and Zhang, T. (2017). Sound Recognition Techniques for Multi-Layered Construction Activities and Events. Computing in Civil Engineering 2017, American Society of Civil Engineers.","DOI":"10.1061\/9780784480847.041"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"04020030","DOI":"10.1061\/(ASCE)CP.1943-5487.0000911","article-title":"Advanced Sound Classifiers and Performance Analyses for Accurate Audio-Based Construction Project Monitoring","volume":"34","author":"Lee","year":"2020","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.autcon.2017.06.005","article-title":"Activity Analysis of Construction Equipment Using Audio Signals and Support Vector Machines","volume":"81","author":"Cheng","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"04019048","DOI":"10.1061\/(ASCE)CP.1943-5487.0000863","article-title":"Audio-Based Bayesian Model for Productivity Estimation of Cyclic Construction Activities","volume":"34","author":"Sabillon","year":"2020","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"114839","DOI":"10.1016\/j.eswa.2021.114839","article-title":"Deep Belief Network Based Audio Classification for Construction Sites Monitoring","volume":"177","author":"Scarpiniti","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dang, K., and Le, T. (2020). A Novel Audio-Based Machine Learning Model for Automated Detection of Collision Hazards at Construction Sites, IAARC Publications.","DOI":"10.22260\/ISARC2020\/0114"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103127","DOI":"10.1016\/j.autcon.2020.103127","article-title":"Evidence-Driven Sound Detection for Prenotification and Identification of Construction Safety Hazards and Accidents","volume":"113","author":"Lee","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fukushima, A., Yagi, R., Kawai, N., Honda, M., Nishina, E., and Oohashi, T. (2014). Frequencies of Inaudible High-Frequency Sounds Differentially Affect Brain Activity: Positive and Negative Hypersonic Effects. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0095464"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Diao, W., Liu, X., and Zhang, K. (2014). Acoustic Fingerprinting Revisited: Generate Stable Device ID Stealthily with Inaudible Sound, Association for Computing Machinery.","DOI":"10.1145\/2660267.2660300"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Nittala, A.S., Yang, X.-D., Bateman, S., Sharlin, E., and Greenberg, S. (2015, January 23\u201326). Phoneear: Interactions for Mobile Devices That Hear High-Frequency Sound-Encoded Data. Proceedings of the 7th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, Duisburg, Germany.","DOI":"10.1145\/2774225.2775082"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hershey, S., Chaudhuri, S., Ellis, D.P., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., and Seybold, B. (2017, January 5\u20139). CNN Architectures for Large-Scale Audio Classification. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952132"},{"key":"ref_32","unstructured":"(2003). ISO 226:2003; Normal Equal-Loudness-Level Contours, ISO."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"33313","DOI":"10.1007\/s11042-021-11372-3","article-title":"BAT: Real-Time Inaudible Sound Capture with Smartphones","volume":"80","author":"Tan","year":"2021","journal-title":"Multimed Tools Appl."},{"key":"ref_34","unstructured":"Deshotels, L. (2014, January 19). Inaudible Sound as a Covert Channel in Mobile Devices. Proceedings of the 8th USENIX Workshop for Offensive Technologies, San Diego, CA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"108258","DOI":"10.1016\/j.apacoust.2021.108258","article-title":"Acoustic Scene Classification Based on Mel Spectrogram Decomposition and Model Merging","volume":"182","author":"Zhang","year":"2021","journal-title":"Appl. Acoust."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.procs.2019.11.147","article-title":"Offline Signature Verification Using Deep Learning Convolutional Neural Network (CNN) Architectures GoogLeNet Inception-v1 and Inception-V3","volume":"161","author":"Sam","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Jin, L., and Xie, Z. (2015, January 23\u201326). High Performance Offline Handwritten Chinese Character Recognition Using Googlenet and Directional Feature Maps. Proceedings of the 2015 13th International Conference on Document Analysis and Recognition (ICDAR), Tunis, Tunisia.","DOI":"10.1109\/ICDAR.2015.7333881"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Etienne, C., Fidanza, G., Petrovskii, A., Devillers, L., and Schmauch, B. (2018). Cnn+ Lstm Architecture for Speech Emotion Recognition with Data Augmentation. arXiv.","DOI":"10.21437\/SMM.2018-5"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105765","DOI":"10.1016\/j.asoc.2019.105765","article-title":"Computer-Aided Diagnosis System Combining FCN and Bi-LSTM Model for Efficient Breast Cancer Detection from Histopathological Images","volume":"85","author":"Budak","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.aei.2018.05.003","article-title":"Automated Detection of Workers and Heavy Equipment on Construction Sites: A Convolutional Neural Network Approach","volume":"37","author":"Fang","year":"2018","journal-title":"Adv. Eng. Inform."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1016\/j.autcon.2011.03.005","article-title":"Progressive 3D Reconstruction of Infrastructure with Videogrammetry","volume":"20","author":"Brilakis","year":"2011","journal-title":"Autom. Constr."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.autcon.2018.01.003","article-title":"Transfer Learning and Deep Convolutional Neural Networks for Safety Guardrail Detection in 2D Images","volume":"89","author":"Kolar","year":"2018","journal-title":"Autom. Constr."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3482\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:05:46Z","timestamp":1760137546000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3482"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,3]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22093482"],"URL":"https:\/\/doi.org\/10.3390\/s22093482","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,3]]}}}