{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T20:10:48Z","timestamp":1773691848008,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,11]],"date-time":"2017-04-11T00:00:00Z","timestamp":1491868800000},"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>Modern cars continue to offer more and more functionalities due to which they need a growing number of commands. As the driver tries to monitor the road and the graphic user interface simultaneously, his\/her overall efficiency is reduced. In order to reduce the visual attention necessary for monitoring, a gesture-based user interface is very important. In this paper, gesture recognition for a vehicle through impulse radio ultra-wideband (IR-UWB) radar is discussed. The gestures can be used to control different electronic devices inside a vehicle. The gestures are based on human hand and finger motion. We have implemented a real-time version using only one radar sensor. Studies on gesture recognition using IR-UWB radar have rarely been carried out, and some studies are merely simple methods using the magnitude of the reflected signal or those whose performance deteriorates largely due to changes in distance or direction. In this study, we propose a new hand-based gesture recognition algorithm that works robustly against changes in distance or direction while responding only to defined gestures by ignoring meaningless motions. We used three independent features, i.e., variance of the probability density function (pdf) of the magnitude histogram, time of arrival (TOA) variation and the frequency of the reflected signal, to classify the gestures. A data fitting method is included to differentiate between gesture signals and unintended hand or body motions. We have used the clustering technique for the classification of the gestures. Moreover, the distance information is used as an additional input parameter to the clustering algorithm, such that the recognition technique will not be vulnerable to distance change. The hand-based gesture recognition proposed in this paper would be a key technology of future automobile user interfaces.<\/jats:p>","DOI":"10.3390\/s17040833","type":"journal-article","created":{"date-parts":[[2017,4,11]],"date-time":"2017-04-11T11:41:42Z","timestamp":1491910902000},"page":"833","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Hand-Based Gesture Recognition for Vehicular Applications Using IR-UWB Radar"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8754-6297","authenticated-orcid":false,"given":"Faheem","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Electronics and Computer Engineering, Hanyang University, 222 Wangsimini-ro, Seongdong-gu, Seoul 133-791, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5622-3452","authenticated-orcid":false,"given":"Seong","family":"Leem","sequence":"additional","affiliation":[{"name":"Department of Electronics and Computer Engineering, Hanyang University, 222 Wangsimini-ro, Seongdong-gu, Seoul 133-791, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2393-1428","authenticated-orcid":false,"given":"Sung","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Electronics and Computer Engineering, Hanyang University, 222 Wangsimini-ro, Seongdong-gu, Seoul 133-791, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,11]]},"reference":[{"key":"ref_1","first-page":"50","article-title":"Significance of Hand Gesture Recognition Systems in Vehicular Automation-A Survey","volume":"99","author":"Francis","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_2","unstructured":"Zhou, R., Yuan, J., and Zhang, Z. (December, January 28). Robust hand gesture recognition based on finger-earth mover\u2019s distance with a commodity depth camera. Proceedings of the 19th ACM international conference on Multimedia, Scottsdale, AZ, USA."},{"key":"ref_3","unstructured":"Ying, W., and Huang, T.S. (1999). Vision-Based Gesture Recognition: A Review, Springer. International Gesture Workshop."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, Q., Georganas, N.D., and Petriu, E.M. (2007, January 1\u20133). Real-time vision-based hand gesture recognition using haar-like features. Proceedings of the 2007 IEEE Instrumentation & Measurement Technology Conference, Warsaw, Poland.","DOI":"10.1109\/IMTC.2007.379068"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/38.250916","article-title":"A survey of glove-based input","volume":"14","author":"Sturman","year":"1994","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_6","first-page":"972","article-title":"Vision based hand gesture recognition","volume":"49","author":"Pragati","year":"2009","journal-title":"World Acad. Sci. Eng. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1109\/TSMCC.2007.893280","article-title":"Gesture recognition: A survey","volume":"37","author":"Sushmita","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s00779-011-0395-z","article-title":"Gesture recognition using RFID technology","volume":"16","author":"Parvin","year":"2012","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_9","unstructured":"Michael, B., Prasad, R., Philipose, M., and Wetherall, D. (October, January 30). Recognizing daily activities with RFID-based sensors. Proceedings of the 11th International Conference on Ubiquitous Computing, Orlando, FL, USA."},{"key":"ref_10","unstructured":"Sprenger, M.E., and Paul, J.G. (2015). Radar-Based Gesture Recognition. (No. 14\/229,727), U.S. Patent."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Molchanov, P., de Mello, S., Kim, K., and Pulli, K. (2015, January 4\u20138). Multi-sensor system for driver\u2019s hand-gesture recognition. Proceedings of the 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, Slovenia.","DOI":"10.1109\/FG.2015.7163132"},{"key":"ref_12","unstructured":"Wan, Q., Li, Y., Li, C., and Pal, R. (2014, January 26\u201330). Gesture recognition for smart home applications using portable radar sensors. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Chicago, IL, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7125","DOI":"10.1109\/ACCESS.2016.2617282","article-title":"Hand Gesture Recognition Using Micro-Doppler Signatures with Convolutional Neural Network","volume":"4","author":"Youngwook","year":"2016","journal-title":"IEEE Access"},{"key":"ref_14","unstructured":"Chuan, Z., Hu, T., Qiao, S., Sun, Y., Huangfu, J., and Ran, L. (2013, January 9\u201311). Doppler bio-signal detection based time-domain hand gesture recognition. Proceedings of the 2013 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO), Singapore."},{"key":"ref_15","unstructured":"Pavlo, M., Gupta, S., Kim, K., and Pulli, K. (2015, January 10\u201315). Short-range FMCW monopulse radar for hand-gesture sensing. Proceedings of the 2015 IEEE Radar Conference (RadarCon), Arlington, VA, USA."},{"key":"ref_16","first-page":"10300","article-title":"Multi-Human Detection Algorithm based on an Impulse Radio Ultra-Wideband Radar System","volume":"4","author":"Woo","year":"2017","journal-title":"IEEE Access"},{"key":"ref_17","unstructured":"Ra\u00fal, C., Khaleghi, A., Balasingham, I., and Ramstad, T.A. (2009, January 24\u201327). Architecture of an ultra wideband wireless body area network for medical applications. Proceedings of the 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2009), Bratislava, Slovak."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Khan, F., and Cho, S.H. (2017). A Detailed Algorithm for Vital Sign Monitoring of a Stationary\/Non-Stationary Human through IR-UWB Radar. Sensors, 17.","DOI":"10.3390\/s17020290"},{"key":"ref_19","unstructured":"Faheem, K., Choi, J.W., and Cho, S.H. (2014, January 19\u201321). Vital sign monitoring of a non-stationary human through IR-UWB radar. Proceedings of the 2014 4th IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC), Beijing, China."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.3390\/s140202595","article-title":"Techniques for clutter suppression in the presence of body movements during the detection of respiratory activity through UWB radars","volume":"14","author":"Antonio","year":"2014","journal-title":"Sensors"},{"key":"ref_21","unstructured":"Woo, C.J., Kim, J.H., and Cho, S.H. (2012, January 21\u201323). A counting algorithm for multiple objects using an IR-UWB radar system. Proceedings of the 2012 3rd IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC), Beijing, China."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xuanjun, Q., Choi, J.W., and Cho, S.H. (2014, January 19\u201321). Direction recognition of moving targets using an IR-UWB radar system. Proceedings of the 2014 4th IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC), Beijing, China.","DOI":"10.1109\/ICNIDC.2014.7000351"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/MSP.2005.1458289","article-title":"Localization via ultra-wideband radios: A look at positioning aspects for future sensor networks","volume":"22","author":"Sinan","year":"2005","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"95","DOI":"10.4236\/jcc.2016.43015","article-title":"Algorithm for Gesture Recognition Using an IR-UWB Radar Sensor","volume":"4","author":"Nan","year":"2016","journal-title":"J. Comput. Commun."},{"key":"ref_25","unstructured":"Junbum, P., and Cho, S.H. (2016, January 12\u201314). IR-UWB Radar Sensor for Human Gesture Recognition by Using Machine Learning. Proceedings of the IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS), 2016 IEEE 18th International Conference on High Performance Computing and Communications, Sydney, Australia."},{"key":"ref_26","unstructured":"Hyeon, Y.D., and Cho, S.H. (2015). An Equidistance Multi-human Detection Algorithm Based on Noise Level Using Mono-static IR-UWB Radar System. Future Communication, Information and Computer Science: Proceedings of the 2014 International Conference on Future Communication, Information and Computer Science (FCICS 2014), May 22\u201323, 2014, Beijing, China, CRC Press."},{"key":"ref_27","unstructured":"Douglas, S.R.G., and Torrie, J.H. (1960). Principles and Procedures of Statistics with Special Reference to the Biological Sciences, Mcgraw-Hill Book Company."},{"key":"ref_28","unstructured":"Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis, CRC Press."},{"key":"ref_29","unstructured":"Anderberg, M.R. (1973). Cluster Analysis for Applications. Monographs and Textbooks on Probability and Mathematical Statistics, Academic Press."},{"key":"ref_30","unstructured":"K, J.A., and Dubes, R.C. (1988). Algorithms for Clustering Data, Prentice-Hall, Inc."},{"key":"ref_31","unstructured":"Leonard, K., and Rousseeuw, P.J. (2009). Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons."},{"key":"ref_32","unstructured":"Everitt, B. (1974). Cluster Analysis Heinemann Educational London, Heinemann Educational."},{"key":"ref_33","first-page":"2972","article-title":"A Review of K-Mean Algorithm","volume":"4","author":"Jyoti","year":"2013","journal-title":"Int. J. Eng. Trends Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/4\/833\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:32:27Z","timestamp":1760207547000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/4\/833"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,11]]},"references-count":33,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,4]]}},"alternative-id":["s17040833"],"URL":"https:\/\/doi.org\/10.3390\/s17040833","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,4,11]]}}}