{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:42:18Z","timestamp":1783010538660,"version":"3.54.6"},"reference-count":50,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In-car activity monitoring is a key enabler of various automotive safety functions. Existing approaches are largely based on vision systems. Radar, however, can provide a low-cost, privacy-preserving alternative. To this day, such systems based on the radar are not widely researched. In our work, we introduce a novel approach that uses the Doppler signal of an ultra-wideband (UWB) radar as an input to deep neural networks for the classification of driving activities. In contrast to previous work in the domain, we focus on generalization to unseen persons and make a new radar driving activity dataset (RaDA) available to the scientific community to encourage comparison and the benchmarking of future methods.<\/jats:p>","DOI":"10.3390\/s23020818","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T04:59:58Z","timestamp":1673413198000},"page":"818","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Driving Activity Recognition Using UWB Radar and Deep Neural Networks"],"prefix":"10.3390","volume":"23","author":[{"given":"Iuliia","family":"Brishtel","sequence":"first","affiliation":[{"name":"Department of Augmented Vision, German Research Center for Artificial Intelligence, Trippstadter Str. 122, 67663 Kaiserslautern, Germany"},{"name":"Department of Computer Science, RPTU, Erwin-Schr\u00f6dinger-Str. 57, 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephan","family":"Krauss","sequence":"additional","affiliation":[{"name":"Department of Augmented Vision, German Research Center for Artificial Intelligence, Trippstadter Str. 122, 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi","family":"Chamseddine","sequence":"additional","affiliation":[{"name":"Department of Augmented Vision, German Research Center for Artificial Intelligence, Trippstadter Str. 122, 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8122-6789","authenticated-orcid":false,"given":"Jason Raphael","family":"Rambach","sequence":"additional","affiliation":[{"name":"Department of Augmented Vision, German Research Center for Artificial Intelligence, Trippstadter Str. 122, 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Didier","family":"Stricker","sequence":"additional","affiliation":[{"name":"Department of Augmented Vision, German Research Center for Artificial Intelligence, Trippstadter Str. 122, 67663 Kaiserslautern, Germany"},{"name":"Department of Computer Science, RPTU, Erwin-Schr\u00f6dinger-Str. 57, 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Joiner, I.A. (2018). Chapter 4\u2014Driverless Vehicles: Pick Me Up at the\u2026?. Emerging Library Technologies, Chandos Publishing.","DOI":"10.1016\/B978-0-08-102253-5.00006-X"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Brishtel, I., Schmidt, T., Vozniak, I., Rambach, J.R., Mirbach, B., and Stricker, D. (2021). To Drive or to Be Driven? The Impact of Autopilot, Navigation System, and Printed Maps on Driver\u2019s Cognitive Workload and Spatial Knowledge. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10100668"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1177\/1541931213571433","article-title":"\u201cTake over!\u201d How long does it take to get the driver back into the loop?","volume":"57","author":"Gold","year":"2013","journal-title":"Proc. Hum. Factors Ergon. Soc. Annu. Meet."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yang, S., le kernec, J., Fioranelli, F., and Romain, O. (2019, January 23\u201327). Human Activities Classification in a Complex Space Using Raw Radar Data. Proceedings of the 2019 International Radar Conference (RADAR), Toulon, France.","DOI":"10.1109\/RADAR41533.2019.171367"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106143","DOI":"10.1016\/j.aap.2021.106143","article-title":"Using eye-tracking to investigate the effects of pre-takeover visual engagement on situation awareness during automated driving","volume":"157","author":"Liang","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_6","unstructured":"Katrolia, J., Mirbach, B., El-Sherif, A., Feld, H., Rambach, J., and Stricker, D. (2021, January 22\u201325). Ticam: A time-of-flight in-car cabin monitoring dataset. Proceedings of the British Machine Vision Conference (BMVC), London, UK."},{"key":"ref_7","unstructured":"Martin, M., Roitberg, A., Haurilet, M., Horne, M., Rei\u00df, S., Voit, M., and Stiefelhagen, R. (November, January 27). Drive&act: A multi-modal dataset for fine-grained driver behavior recognition in autonomous vehicles. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Brishtel, I., Krau\u00df, S., Schmidt, T., Rambach, J.R., Vozniak, I., and Stricker, D. (2022, January 9\u201312). Classification of Manual Versus Autonomous Driving based on Machine Learning of Eye Movement Patterns. Proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic.","DOI":"10.1109\/SMC53654.2022.9945234"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1177\/0018720818788164","article-title":"Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel","volume":"60","author":"Victor","year":"2018","journal-title":"Hum. Factors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.trc.2017.01.001","article-title":"Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation","volume":"76","author":"Louw","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.aap.2016.04.002","article-title":"Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving","volume":"92","author":"Zeeb","year":"2016","journal-title":"Accid. Anal. Prev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1080\/1463922X.2018.1528484","article-title":"How to keep drivers engaged while supervising driving automation? A literature survey and categorisation of six solution areas","volume":"22","author":"Cabrall","year":"2019","journal-title":"Theor. Issues Ergon. Sci."},{"key":"ref_13","unstructured":"Templeton, B. (2022, November 14). New Tesla Autopilot Statistics Show It\u2019s Almost as Safe Driving with It as without. Available online: https:\/\/www.forbes.com\/sites\/bradtempleton\/2020\/10\/28\/new-tesla-autopilot-statistics-show-its-almost-as-safe-driving-with-it-as-without\/."},{"key":"ref_14","unstructured":"Volvo Cars (2022, November 14). Volvo Cars to Deploy In-Car Cameras and Intervention against Intoxication, Distraction. Available online: https:\/\/www.media.volvocars.com\/global\/en-gb\/media\/pressreleases\/250015\/volvo-cars-to-deploy-in-car-cameras-and-intervention-against-intoxication-distraction."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, X., He, Y., and Jing, X. (2019). A Survey of Deep Learning-Based Human Activity Recognition in Radar. Remote Sens., 11.","DOI":"10.3390\/rs11091068"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1049\/iet-rsn.2011.0101","article-title":"Application of ultra-wide band radar for classification of human activities","volume":"6","author":"Bryan","year":"2012","journal-title":"Radar Sonar Navig. IET"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.procs.2020.03.004","article-title":"Activity Recognition in Smart Homes using UWB Radars","volume":"170","author":"Bouchard","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhu, S., Xu, J., Guo, H., Liu, Q., Wu, S., and Wang, H. (2018, January 20\u201324). Indoor Human Activity Recognition Based on Ambient Radar with Signal Processing and Machine Learning. Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA.","DOI":"10.1109\/ICC.2018.8422107"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Taylor, W., Dashtipour, K., Shah, S.A., Hussain, A., Abbasi, Q.H., and Imran, M.A. (2021). Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning. Sensors, 21.","DOI":"10.3390\/s21113881"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shao, Y., Guo, S., Sun, L., and Chen, W. (2017, January 21\u201323). Human Motion Classification Based on Range Information with Deep Convolutional Neural Network. Proceedings of the 2017 4th International Conference on Information Science and Control Engineering (ICISCE), Changsha, China.","DOI":"10.1109\/ICISCE.2017.317"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.patcog.2018.07.030","article-title":"Open-set human activity recognition based on micro-Doppler signatures","volume":"85","author":"Yang","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"138132","DOI":"10.1109\/ACCESS.2021.3117667","article-title":"Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning","volume":"9","author":"Noori","year":"2021","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1109\/TBCAS.2018.2813013","article-title":"A Direct Phase-Tracking Doppler Radar Using Wavelet Independent Component Analysis for Non-Contact Respiratory and Heart Rate Monitoring","volume":"12","author":"Mercuri","year":"2018","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MAES.2006.1624185","article-title":"UWB radar for human being detection","volume":"21","author":"Yarovoy","year":"2006","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"23518","DOI":"10.1109\/JSEN.2021.3110367","article-title":"Portable UWB RADAR Sensing System for Transforming Subtle Chest Movement Into Actionable Micro-Doppler Signatures to Extract Respiratory Rate Exploiting ResNet Algorithm","volume":"21","author":"Saeed","year":"2021","journal-title":"IEEE Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Erol, B., Amin, M., Boashash, B., Ahmad, F., and Zhang, Y. (2016, January 6\u20139). Wideband radar based fall motion detection for a generic elderly. Proceedings of the 2016 50th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2016.7869686"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jokanovic, B., Amin, M., and Ahmad, F. (2016, January 2\u20136). Radar fall motion detection using deep learning. Proceedings of the 2016 IEEE Radar Conference (RadarConf), Philadelphia, PA, USA.","DOI":"10.1109\/RADAR.2016.7485147"},{"key":"ref_28","unstructured":"Ramaiah, K. (2022, November 14). In-Cabin Radar Can Sense Children in Second- and Third-Row Vehicles. Available online: https:\/\/www.electronicproducts.com\/in-cabin-radar-can-sense-children-in-second-and-third-row-vehicles\/."},{"key":"ref_29","unstructured":"InnoSenT (2022, November 14). Incabin Radar Monitoring. Available online: https:\/\/www.innosent.de\/en\/automotive\/incabin-radar-monitoring\/."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Leem, S., Khan, F., and Cho, S.H. (2017). Vital Sign Monitoring and Mobile Phone Usage Detection Using IR-UWB Radar for Intended Use in Car Crash Prevention. Sensors, 17.","DOI":"10.3390\/s17061240"},{"key":"ref_31","unstructured":"MOBIS, H. (2022, November 14). The New Radar-based Occupant Alert System To Keep Your Children Safe. Available online: https:\/\/www.hyundaimotorgroup.com\/story\/CONT0000000000002988."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4031","DOI":"10.1109\/TMTT.2019.2934413","article-title":"Inattentive Driving Behavior Detection Based on Portable FMCW Radar","volume":"67","author":"Ding","year":"2019","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_33","unstructured":"Sakamoto, T. (2020). Personal Identification Using Ultrawideband Radar Measurement of Walking and Sitting Motions and a Convolutional Neural Network. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1109\/LGRS.2012.2190707","article-title":"Through-Wall Detection of Human Being\u2019s Movement by UWB Radar","volume":"9","author":"Li","year":"2012","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Congzhang, D., Jia, Y., Cui, G., Chen, C., Zhong, X., and Guo, Y. (2021). Continuous Human Activity Recognition through Parallelism LSTM with Multi-Frequency Spectrograms. Remote Sens., 13.","DOI":"10.3390\/rs13214264"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1038\/s41597-021-00876-0","article-title":"UWB-gestures, a public dataset of dynamic hand gestures acquired using impulse radar sensors","volume":"8","author":"Ahmed","year":"2021","journal-title":"Sci. Data"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1049\/el.2019.2378","article-title":"Radar sensing for healthcare","volume":"55","author":"Fioranelli","year":"2019","journal-title":"Electron. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1038\/s41597-022-01573-2","article-title":"OPERAnet, a multimodal activity recognition dataset acquired from radio frequency and vision-based sensors","volume":"9","author":"Bocus","year":"2022","journal-title":"Sci. Data"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"12295","DOI":"10.1007\/s00521-019-04408-1","article-title":"Indoor human activity recognition using high-dimensional sensors and deep neural networks","volume":"32","author":"Vandersmissen","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1109\/TMTT.2004.834186","article-title":"Recent system applications of short-pulse ultra-wideband (UWB) technology","volume":"52","author":"Fontana","year":"2004","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_41","unstructured":"Zhang, C., Kuhn, M., Merkl, B., Fathy, A., and Mahfouz, M. (2006, January 17\u201319). Accurate UWB indoor localization system utilizing time difference of arrival approach. Proceedings of the 2006 IEEE Radio and Wireless Symposium, San Diego, CA, USA."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Du, H., He, Y., and Jin, T. (2018, January 26\u201328). Transfer Learning for Human Activities Classification Using Micro-Doppler Spectrograms. Proceedings of the 2018 IEEE International Conference on Computational Electromagnetics (ICCEM), Chengdu, China.","DOI":"10.1109\/COMPEM.2018.8496654"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, X., He, Y., Yang, Y., Hong, Y., and Jing, X. (2019, January 20\u201322). LSTM based Human Activity Classification on Radar Range Profile. Proceedings of the 2019 IEEE International Conference on Computational Electromagnetics (ICCEM), Shanghai, China.","DOI":"10.1109\/COMPEM.2019.8779144"},{"key":"ref_44","unstructured":"University, C.M. (2022, November 14). CMU Graphics Lab Motion Capture Database. Available online: http:\/\/mocap.cs.cmu.edu\/."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"13607","DOI":"10.1109\/JSEN.2020.3006386","article-title":"Continuous human activity classification from FMCW radar with Bi-LSTM networks","volume":"20","author":"Shrestha","year":"2020","journal-title":"IEEE Sensors J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1177\/0018720812446965","article-title":"How Dangerous Is Looking Away from the Road? Algorithms Predict Crash Risk from Glance Patterns in Naturalistic Driving","volume":"54","author":"Liang","year":"2012","journal-title":"Hum. Factors"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.procs.2021.12.222","article-title":"A Systematic Evaluation of the XeThru X4 Ultra-Wideband Radar Behavior","volume":"198","author":"Thullier","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_48","unstructured":"Porter, B.E. (2011). Chapter 20\u2014Driver Distraction: Definition, Mechanisms, Effects, and Mitigation. Handbook of Traffic Psychology, Academic Press."},{"key":"ref_49","first-page":"8026","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_50","first-page":"369","article-title":"Super-convergence: Very fast training of neural networks using large learning rates","volume":"Volume 11006","author":"Pham","year":"2019","journal-title":"Proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/818\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:06:15Z","timestamp":1760119575000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/818"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,10]]},"references-count":50,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020818"],"URL":"https:\/\/doi.org\/10.3390\/s23020818","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,10]]}}}