{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:47:33Z","timestamp":1743112053282,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031068935"},{"type":"electronic","value":"9783031068942"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06894-2_8","type":"book-chapter","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T19:03:58Z","timestamp":1661972638000},"page":"81-91","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Human Action Detection, Classification and\u00a0Monitoring Based on\u00a0Micro-Doppler Processing for\u00a0Avoidance of\u00a0Work Accidents"],"prefix":"10.1007","author":[{"given":"Luca","family":"Dall\u2019Asta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georg","family":"Egger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,1]]},"reference":[{"unstructured":"European Statistics: Accidents at work statistics. https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php\/Accidents_at_work_statistics","key":"8_CR1"},{"unstructured":"European Statistics: Accidents at work, working sector. https:\/\/ec.europa.eu\/eurostat\/databrowser\/view\/hsw_n2_01\/default\/table?lang=en","key":"8_CR2"},{"unstructured":"European Statistics: Accidents at work, causes and circumstances. https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php\/Accidents_at_work_-_statistics_on_causes_and_circumstances","key":"8_CR3"},{"doi-asserted-by":"crossref","unstructured":"Sarkar, S., Ejaz, N.,\u00a0Maiti, J.: Application of hybrid clustering technique for pattern extraction of accident at work: a case study of a steel industry. In: 2018 4th International Conference on Recent Advances in Information Technology (RAIT), pp. 1\u20136. IEEE, March 2018","key":"8_CR4","DOI":"10.1109\/RAIT.2018.8389052"},{"doi-asserted-by":"crossref","unstructured":"Choi, Y., Park, J.-H., Jang, B.: Developing safety checklists for predicting accidents. In: 2018 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1426\u20131430. IEEE, October 2018","key":"8_CR5","DOI":"10.1109\/ICTC.2018.8539652"},{"issue":"7237","key":"8_CR6","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1136\/bmj.320.7237.768","volume":"320","author":"J Reason","year":"2000","unstructured":"Reason, J.: Human error: models and management. BMJ 320(7237), 768\u2013770 (2000)","journal-title":"BMJ"},{"doi-asserted-by":"crossref","unstructured":"Yang, H., Chew, D.A.S., Wu, W., Zhou, Z., Li, Q.: Design and implementation of an identification system in construction site safety for proactive accident prevention. Accid. Anal. Prev. 48, 193\u2013203 (2012). Intelligent Speed Adaptation + Construction Projects","key":"8_CR7","DOI":"10.1016\/j.aap.2011.06.017"},{"issue":"5","key":"8_CR8","doi-asserted-by":"publisher","first-page":"04019029","DOI":"10.1061\/(ASCE)CP.1943-5487.0000845","volume":"33","author":"H Son","year":"2019","unstructured":"Son, H., Seong, H., Choi, H., Kim, C.: Real-time vision-based warning system for prevention of collisions between workers and heavy equipment. J. Comput. Civ. Eng. 33(5), 04019029 (2019)","journal-title":"J. Comput. Civ. Eng."},{"doi-asserted-by":"crossref","unstructured":"Parlak, O.: Portable and wearable real-time stress monitoring: a critical review. Sens. Actuators Rep. 3, 100036 (2021)","key":"8_CR9","DOI":"10.1016\/j.snr.2021.100036"},{"issue":"6","key":"8_CR10","doi-asserted-by":"publisher","first-page":"04015003","DOI":"10.1061\/(ASCE)CO.1943-7862.0000972","volume":"141","author":"YM Goh","year":"2015","unstructured":"Goh, Y.M., Sa\u2019adon, N.F.B.: Cognitive factors influencing safety behavior at height: a multimethod exploratory study. J. Constr. Eng. Manag. 141(6), 04015003 (2015)","journal-title":"J. Constr. Eng. Manag."},{"doi-asserted-by":"crossref","unstructured":"Liu, C.-C., Ying, J.J.-C.: DeepSafety: a deep learning framework for unsafe behaviors detection of steel activity in construction projects. In: 2020 International Computer Symposium (ICS), pp. 135\u2013140. IEEE, December 2020","key":"8_CR11","DOI":"10.1109\/ICS51289.2020.00036"},{"doi-asserted-by":"crossref","unstructured":"Htike, K.K., Khalifa, O.O., Ramli, H.A.M., Abushariah, M.A.M.: Human activity recognition for video surveillance using sequences of postures. In: The Third International Conference on e-Technologies and Networks for Development (ICeND 2014), pp. 79\u201382. IEEE, April 2014","key":"8_CR12","DOI":"10.1109\/ICeND.2014.6991357"},{"doi-asserted-by":"crossref","unstructured":"Kabir, R., Ahmed, N., Roy, N., Islam, M.R.: A novel dynamic hand gesture and movement trajectory recognition model for non-touch HRI interface. In: 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), pp. 505\u2013508. IEEE, October 2019","key":"8_CR13","DOI":"10.1109\/ECICE47484.2019.8942691"},{"doi-asserted-by":"crossref","unstructured":"Nobis, F., Geisslinger, M., Weber, M., Betz, J., Lienkamp, M.: A deep learning-based radar and camera sensor fusion architecture for object detection. In: 2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF), pp. 1\u20137. IEEE, October 2019","key":"8_CR14","DOI":"10.1109\/SDF.2019.8916629"},{"issue":"4","key":"8_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2897824.2925953","volume":"35","author":"J Lien","year":"2016","unstructured":"Lien, J., Gillian, N., Karagozler, M.E., Amihood, P., Schwesig, C., Olson, E., Raja, H., Poupyrev, I.: Soli: ubiquitous gesture sensing with millimeter wave radar. ACM Trans. Graph. 35(4), 1\u201319 (2016)","journal-title":"ACM Trans. Graph."},{"issue":"1","key":"8_CR16","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/TAES.2006.1603402","volume":"42","author":"VC Chen","year":"2006","unstructured":"Chen, V.C., Li, F., Ho, S.-S., Wechsler, H.: Micro-doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans. Aerosp. Electron. Syst. 42(1), 2\u201321 (2006)","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"doi-asserted-by":"crossref","unstructured":"Heuel, S., Rohling, H.: Pedestrian classification in automotive radar systems. In: 2012 13th International Radar Symposium, pp. 39\u201344. IEEE, May 2012","key":"8_CR17","DOI":"10.1109\/IRS.2012.6233285"},{"key":"8_CR18","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.eswa.2019.06.048","volume":"136","author":"JVB Severino","year":"2019","unstructured":"Severino, J.V.B., Zimmer, A., Brandmeier, T., Freire, R.Z.: Pedestrian recognition using micro Doppler effects of radar signals based on machine learning and multi-objective optimization. Expert Syst. Appl. 136, 304\u2013315 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"8_CR19","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1109\/LGRS.2014.2336231","volume":"12","author":"K Youngwook","year":"2015","unstructured":"Youngwook, K., Sungjae, H., Jihoon, K.: Human detection using doppler radar based on physical characteristics of targets. IEEE Geosci. Remote Sens. Lett. 12(2), 289\u2013293 (2015)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"4","key":"8_CR20","doi-asserted-by":"publisher","first-page":"3197","DOI":"10.1109\/TAES.2020.2969579","volume":"56","author":"B Erol","year":"2020","unstructured":"Erol, B., Gurbuz, S.Z., Amin, M.G.: Motion classification using kinematically sifted ACGAN-synthesized radar micro-doppler signatures. IEEE Trans. Aerosp. Electron. Syst. 56(4), 3197\u20133213 (2020)","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"doi-asserted-by":"crossref","unstructured":"Abdulatif, S., Wei, Q., Aziz, F., Kleiner, B., Schneider, U.: Micro-doppler based human-robot classification using ensemble and deep learning approaches (2017)","key":"8_CR21","DOI":"10.1109\/RADAR.2018.8378705"},{"doi-asserted-by":"crossref","unstructured":"Shah, S.A., Fioranelli, F.: Human activity recognition: preliminary results for dataset portability using FMCW radar. In: 2019 International Radar Conference (RADAR), pp. 1\u20134 (2019)","key":"8_CR22","DOI":"10.1109\/RADAR41533.2019.171307"},{"key":"8_CR23","volume-title":"Introduction to Radar Systems, International Student Edition","author":"MI Skolnik","year":"1962","unstructured":"Skolnik, M.I.: Introduction to Radar Systems, International Student Edition. McGraw-Hill, New Delhi (1962)"},{"issue":"7","key":"8_CR24","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1109\/PROC.1969.7230","volume":"57","author":"JJ Kroszczynski","year":"1969","unstructured":"Kroszczynski, J.J.: Pulse compression by means of linear-period modulation. Proc. IEEE 57(7), 1260\u20131266 (1969)","journal-title":"Proc. IEEE"},{"issue":"1","key":"8_CR25","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/LGRS.2005.856700","volume":"3","author":"JJM DeWit","year":"2006","unstructured":"DeWit, J.J.M., Meta, A., Hoogeboom, P.: Modified range-doppler processing for FM-CW synthetic aperture radar. IEEE Geosci. Remote Sens. Lett. 3(1), 83\u201387 (2006)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"8_CR26","volume-title":"Two-Dimensional Phase Unwrapping: Theory","author":"DC Ghiglia","year":"1998","unstructured":"Ghiglia, D.C., Pritt, M.D.: Two-Dimensional Phase Unwrapping: Theory. Algorithms and Software. Wiley, New York (1998)"},{"unstructured":"Texas Instruments: IWR1642BOOST, IWR1642 single-chip 76-GHz to 81-GHz mmWave sensor integrating DSP and MCU evaluation module. https:\/\/www.ti.com\/tool\/IWR1642BOOST","key":"8_CR27"},{"unstructured":"Texas Instruments: DCA1000EVM, real-time data-capture adapter for radar sensing evaluation module. https:\/\/www.ti.com\/tool\/DCA1000EVM","key":"8_CR28"},{"unstructured":"Texas Instruments: mmWave Studio. https:\/\/www.ti.com\/tool\/MMWAVE-STUDIO","key":"8_CR29"}],"container-title":["Lecture Notes in Networks and Systems","Ambient Intelligence \u2013 Software and Applications \u2013 12th International Symposium on Ambient Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06894-2_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T19:20:14Z","timestamp":1661973614000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06894-2_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031068935","9783031068942"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06894-2_8","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISAmI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Ambient Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isaml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isami-conference.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}