{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:49:36Z","timestamp":1760402976558,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,18]],"date-time":"2020-04-18T00:00:00Z","timestamp":1587168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and ICT","award":["IITP-2020-2018-0-01423"],"award-info":[{"award-number":["IITP-2020-2018-0-01423"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017R1D1A1B03033422"],"award-info":[{"award-number":["NRF-2017R1D1A1B03033422"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.<\/jats:p>","DOI":"10.3390\/s20082321","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T04:49:38Z","timestamp":1587444578000},"page":"2321","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar"],"prefix":"10.3390","volume":"20","author":[{"given":"Myoungseok","family":"Yu","sequence":"first","affiliation":[{"name":"Dept. Information and Communication Engineering, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Korea"}]},{"given":"Narae","family":"Kim","sequence":"additional","affiliation":[{"name":"Dept. Information and Communication Engineering, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2299-9911","authenticated-orcid":false,"given":"Yunho","family":"Jung","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Korea Aerospace University, Goyang, Gyeonggi-do 10540, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9344-7052","authenticated-orcid":false,"given":"Seongjoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Dept. Information and Communication Engineering, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sonkusare, J.S., Chopade, N.B., Sor, R., and Tade, S.L. (2015, January 26\u201327). A review on hand gesture recognition system. Proceedings of the 2015 International Conference on Computing Communication Control and Automation, Pune, India.","DOI":"10.1109\/ICCUBEA.2015.158"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1109\/TCSVT.2015.2469551","article-title":"Survey on 3D Hand Gesture Recognition","volume":"26","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_3","unstructured":"Sun, J., Ji, T., Zhang, S., Yang, J., and Ji, G. (2019, January 7). Survey on 3D hand gesture recognition. Proceedings of the IEEE International Conference 12th International Symposium on Antennas, Propagation and EM Theory, Hangzhou, China."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1109\/TMM.2018.2808769","article-title":"EgoGesture: A new dataset and benchmark for egocentric hand gesture recognition","volume":"20","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/TMM.2014.2374357","article-title":"Superpixel-based hand gesture recognition with kinect depth camera","volume":"17","author":"Wang","year":"2014","journal-title":"IEEE Trans. Multimed."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/TAES.2019.2914536","article-title":"A pose measurement method of a space non-cooperative target based on maximum outer contour recognition","volume":"56","author":"Peng","year":"2019","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"22344","DOI":"10.1109\/ACCESS.2017.2759798","article-title":"An efficient pose measurement method of a space non-cooperative target based on stereo vision","volume":"5","author":"Peng","year":"2017","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lian, K., Chiu, C., Hong, Y., and Sung, W. (2017, January 5\u20138). Wearable armband for real time hand gesture recognition. Proceedings of the IEEE Internatonal Conference IEEE Internatonal Conference on Systems, Man, and Cybernetics, Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8123083"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Siddiqui, N., and Chan, R.H.M. (2017, January 11\u201315). A wearable hand gesture recognition device based on acoustic measurements at wrist. Proceedings of the IEEE International Conference 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Seogwipo, Korea.","DOI":"10.1109\/EMBC.2017.8037842"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sakamoto, T., Gao, X., Yavari, E., Rahman, A., Boric-Lubecke, O., and Lubecke, V.M. (2017, January 4\u20136). Radar-based hand gesture recognition using I-Q echo plot and convolutional neural network. Proceedings of the IEEE International Conference IEEE Conference on Antenna Measurements and Applications, Tsukuba, Japan.","DOI":"10.1109\/CAMA.2017.8273461"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dekker, B., Jacobs, S., Kossen, A.S., Kruithof, M.C., Huizing, A.G., and Geurts, M. (2017, January 11\u201313). Gesture recognition with a low power FMCW radar and a deep convolutional neural network. Proceedings of the IEEE International Conference European Radar Conference, Nuremberg, Germany.","DOI":"10.23919\/EURAD.2017.8249172"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.1109\/JSEN.2017.2679220","article-title":"A hand gesture recognition sensor using reflected impulses","volume":"17","author":"Kim","year":"2017","journal-title":"IEEE Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, G., Ritchie, M., Fioranelli, F., and Griffiths, H. (2016, January 10\u201313). Dynamic hand gesture classification based on radar micro-doppler signatures. Proceedings of the IEEE International Conference CIE International Conference on Radar, Guangzhou, China.","DOI":"10.1109\/RADAR.2016.8059518"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7593","DOI":"10.1109\/JSEN.2018.2859815","article-title":"Feature-based hand gesture recognition using an fmcw radar and its temporal feature analysis","volume":"18","author":"Ryu","year":"2018","journal-title":"IEEE Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3278","DOI":"10.1109\/JSEN.2018.2808688","article-title":"Latern: Dynamic continuous hand gesture recognition using FMCW radar sensor","volume":"18","author":"Zhang","year":"2018","journal-title":"IEEE Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3041","DOI":"10.1109\/JSEN.2019.2892073","article-title":"Hand-gesture recognition using two-antenna doppler radar with deep convolutional neural networks","volume":"19","author":"Skaria","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kim, Y., and Toomajian, B. (2017, January 19\u201324). Application of Doppler Radar for the recognition of hand gestures using optimized deep convolutional neural networks. Proceedings of the IEEE International Conference 11th European Conference on Antennas and Propagation, Paris, France.","DOI":"10.23919\/EuCAP.2017.7928465"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7125","DOI":"10.1109\/ACCESS.2016.2617282","article-title":"Hand gesture recognition using micro-doppler signatureswith convolutional neural network","volume":"4","author":"Kim","year":"2016","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"33610","DOI":"10.1109\/ACCESS.2019.2903586","article-title":"Short-range radar based real-time hand gesture recognition using LSTM encoder","volume":"7","author":"Choi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"She, L., Wang, G., Zhang, S., and Zhao, J. (2009, January 17\u201319). An adaptive threshold algorithm combining shifting window difference and forward-backward difference in real-time r-wave detection. Proceedings of the IEEE International Conference 2009 Internatonal Congress on Image and Signal Processing, Tianjin, China.","DOI":"10.1109\/CISP.2009.5304666"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/8\/2321\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:09:00Z","timestamp":1760364540000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/8\/2321"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,18]]},"references-count":20,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["s20082321"],"URL":"https:\/\/doi.org\/10.3390\/s20082321","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,4,18]]}}}