{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:25:38Z","timestamp":1760232338285,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T00:00:00Z","timestamp":1667174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","award":["2022D01C54"],"award-info":[{"award-number":["2022D01C54"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, radio frequency identification (RFID) sensing has attracted much attention due to its contact-free nature, low cost, light weight and other advantages. RFID-based person detection has also become a hot research topic, but there are still some problems in the existing research. First, most of the current studies cannot identify numerous people at a time well. Second, in order to detect more accurately, it is necessary to evaluate the whole-body activity of a person, which will consume a lot of time to process the data and cannot be applied in time. To solve these problems, in this paper we propose RF-Detection, a person detection system using RFID. First of all, RF-Detection takes step length as the standard for person detection, divides step length into specific sections according to the relationship between step length and height, and achieves high accuracy for new user detection through a large amount of training for a specific step length. Secondly, RF-Detection can better identify the number of people in the same space by segmenting continuous people. Finally, the data collection was reduced by expanding the data set, and the deep learning method was used to further improve the accuracy. The results show that the overall recognition accuracy of RF-Detection is 98.93%.<\/jats:p>","DOI":"10.3390\/s22218353","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T06:49:02Z","timestamp":1667371742000},"page":"8353","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Capturing Features and Performing Human Detection from Human Gaits Using RFID"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9396-3313","authenticated-orcid":false,"given":"Yajun","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Software, XinJiang University, \u00dcr\u00fcmqi 830091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software, XinJiang University, \u00dcr\u00fcmqi 830091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixiong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Software, XinJiang University, \u00dcr\u00fcmqi 830091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software, XinJiang University, \u00dcr\u00fcmqi 830091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Software, XinJiang University, \u00dcr\u00fcmqi 830091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mart\u00edn-Gorostiza, E., Garc\u00eda-Garrido, M.N., and Pizarro, D. (2019). An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors. Sensors, 19.","DOI":"10.20944\/preprints201905.0343.v1"},{"key":"ref_2","first-page":"8500313","article-title":"Experimental Evaluation of the Forkbeard Ultrasonic Indoor Positioning System","volume":"71","author":"Vincent","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hou, Y., Sum, G., and Fan, B. (2014, January 19\u201321). The indoor wireless location technology research based on WiFi. Proceedings of the 2014 10th International Conference on Natural Computation (ICNC), Xiamen, China.","DOI":"10.1109\/ICNC.2014.6975984"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, Y., Peng, C., and Mou, D. (2018). The Height-Adaptive Parameterized Step Length Measurement Method and Experiment Based on Motion Parameters. Sensors, 18.","DOI":"10.3390\/s18041039"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.3390\/s22041656","article-title":"Human Being Detection from UWB NLOS Signals: Accuracy and Generality of Advanced Machine Learning Models","volume":"22","author":"Gianluca","year":"2022","journal-title":"Sensors"},{"key":"ref_6","first-page":"2003816","article-title":"A Short-Range FMCW Radar-Based Approach for Multi-Target Human-Vehicle Detection","volume":"60","author":"Emanuele","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","unstructured":"Bharati, K., and Anil, S. (2018, January 10\u201312). Human Fall Detection Using RFID Technology. Proceedings of the International Conference on Computing, Communication and Networking Technologies, Bengaluru, India."},{"key":"ref_8","unstructured":"(2022, June 12). Leap Motion. Available online: https:\/\/www.vicon.com."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jain, S., and Nandy, A. (2020, January 4\u20136). Human Gait Abnormality Detection Using Low Cost Sensor Technology. Proceedings of the Computer Vision and Image Processing, Prayagraj, India.","DOI":"10.1007\/978-981-16-1092-9_28"},{"key":"ref_10","unstructured":"(2022, June 12). X-Box Kinect. Available online: https:\/\/www.xbox.com."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s11517-009-0565-6","article-title":"Capacitive facial movement detection for human\u2013computer interaction to click by frowning and lifting eyebrows","volume":"48","author":"Ville","year":"2010","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, J., Yuen, P.C., and Li, C.H. (2001, January 18\u201320). Detection, Recognition, and Expression Analysis of Human Faces. Proceedings of the Active Media Technology, Hong Kong, China.","DOI":"10.1007\/3-540-45336-9"},{"key":"ref_13","unstructured":"Kirti, C., Gabriel, R., and Zhang, L. (2010, January 15\u201319). Object localization using RFID. Proceedings of the IEEE 5th International Symposium on Wireless Pervasive Computing, Seattle, WA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Li, D., Zhao, R., Wang, D., Deng, Y., and Chen, B. (2018, January 19\u201323). RFree-ID: An Unobtrusive Human Identification System Irrespective of Walking Cofactors Using COTS RFID. Proceedings of the IEEE International Conference on Pervasive Computing and Communications, Athens, Greece.","DOI":"10.1109\/PERCOM.2018.8444599"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, Q., Jia, H., and Duan, X. (2019, January 18\u201320). An Effective Personnel Detection System Based on Radio Frequency Identification. Proceedings of the IEEE International Conference on Advanced Infocomm Technology, Jinan, China.","DOI":"10.1109\/ICAIT.2019.8935896"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"46585","DOI":"10.1109\/ACCESS.2020.2979080","article-title":"RFID Based Non-Contact Human Activity Detection Exploiting Cross Polarization","volume":"8","author":"He","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xie, B., Xiong, J., Chen, X., and Chai, E. (2019, January 10\u201313). Tagtag: Material sensing with commodity RFID. Proceedings of the 17th Conference on Embedded Networked Sensor Systems, New York, NY, USA.","DOI":"10.1145\/3356250.3360027"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, J., Xiong, J., Chen, X., and Jiang, H. (2017, January 16\u201320). TagScan: Simultaneous Target Imaging and Material Identification with Commodity RFID Devices. Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking, Snowbird, UT, USA.","DOI":"10.1145\/3117811.3117830"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, R., Huang, X., Liu, W., and Zhou, Y. (2020, January 6\u20139). RFID-Based Vehicle Localization Scheme in GPS-Less Environments. Proceedings of the IEEE Conference on Computer Communications, Toronto, ON, Canada.","DOI":"10.1109\/INFOCOMWKSHPS50562.2020.9162740"},{"key":"ref_20","unstructured":"Jue, W., Deepak, V., and Dina, K. (2014, January 17\u201322). RF-IDraw: Virtual touch screen in the air using RF signals. Proceedings of the ACM SIGCOMM, Chicago, IL, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3427315","article-title":"Towards Domain-independent Complex and Fine-grained Gesture Recognition with RFID","volume":"4","author":"Dian","year":"2020","journal-title":"ACM Hum. Comput. Interact."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhao, R., Wang, D., Zhang, Q., Chen, H., and Huang, A. (2018, January 11\u201313). CRH: A Contactless Respiration and Heartbeat Monitoring System with COTS RFID Tags. Proceedings of the IEEE International Conference on Sensing, Communication and Networking, Hong Kong, China.","DOI":"10.1109\/SAHCN.2018.8397132"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"96:1","DOI":"10.1145\/3351254","article-title":"Beyond Respiration: Contactless Sleep Sound-Activity Recognition Using RF Signals","volume":"3","author":"Liu","year":"2019","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1812","DOI":"10.1109\/TMC.2020.3029833","article-title":"ShopSense:Customer Localization in Multi-Person Scenario With Passive RFID Tags","volume":"21","author":"Wang","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"79:1","DOI":"10.1145\/3351237","article-title":"LungTrack: Towards Contactless and Zero Dead-Zone Respiration Monitoring with Commodity RFIDs","volume":"3","author":"Chen","year":"2019","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_26","unstructured":"Chen, X., Liu, J., Xiao, F., Chen, S., and Chen, L. (July, January 24). Thermotag: Item-level temperature sensing with a passive RFID tag. Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, Virtual Event."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Duan, C., Shi, W., Dang, F., and Ding, X. (2020, January 6\u20139). Enabling RFID-Based Tracking for Multi-Objects with Visual Aids: A Calibration-Free Solution. Proceedings of the IEEE Conference on Computer Communications, Toronto, ON, Canada.","DOI":"10.1109\/INFOCOM41043.2020.9155355"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3448101","article-title":"RF-Identity: Non-Intrusive Person Identification Based on Commodity RFID Devices","volume":"5","author":"Feng","year":"2021","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3328919","article-title":"Au-Id: Automatic User Identification and Authentication through the Motions Captured from Sequential Human Activities Using RFID","volume":"3","author":"Huang","year":"2019","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_30","first-page":"1","article-title":"TagFree Activity Identification with RFIDs","volume":"2","author":"Fan","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Feng, L., Li, Z., and Liu, C. (2019, January 21\u201323). Are you sitting right? -Sitting Posture Recognition Using RF Signals. Proceedings of the IEEE PACRIM, Victoria, BC, Canada.","DOI":"10.1109\/PACRIM47961.2019.8985070"},{"key":"ref_32","first-page":"10","article-title":"Estimation of height based on the regression analysis of step lenght","volume":"1","author":"Li","year":"1999","journal-title":"China Acad. J. Electron. Publ. House"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Luo, J., and Shin, K.G. (2019, January 17\u201321). Detecting Misplaced RFID Tags on Static Shelved Items. Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, Seoul, Korea.","DOI":"10.1145\/3307334.3326085"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1063\/1.4822961","article-title":"Savitzky-golay smoothing filters","volume":"4","author":"Press","year":"1990","journal-title":"Comput. Phys."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1016\/j.patcog.2005.01.012","article-title":"Score normalization in multimodal biometric systems","volume":"38","author":"Anil","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5669","DOI":"10.1109\/JIOT.2020.3033173","article-title":"DeepSeg: Deep-Learning-Based Activity Segmentation Framework for Activity Recognition Using WiFi","volume":"8","author":"Xiao","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1109\/JIOT.2019.2947448","article-title":"SmartHandwriting: Handwritten Chinese Character Recognition with Smartwatch","volume":"7","author":"Zhang","year":"2020","journal-title":"IEEE Internet Things"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jin, X., Du, X., and Sun, H. (2021, January 23\u201325). VGG-S: Improved Small Sample Image Recognition Model Based on VGG16. Proceedings of the International Conference on Artificial Intelligence and Advanced Manufacture, Manchester, UK.","DOI":"10.1109\/AIAM54119.2021.00054"},{"key":"ref_39","unstructured":"Jens, B., Cacilia, O., Patrick, K., and Dominik, S. (2013, January 3\u20137). Subsequence dynamic time warping as a method for robust step segmentation using gyroscope signals of daily life activities. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1109\/JIOT.2019.2948605","article-title":"Enhancing Camera-Based Multimodal Indoor Localization With Device-Free Movement Measurement Using WiFi","volume":"7","author":"Zhao","year":"2020","journal-title":"IEEE Internet Things J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8353\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:06:34Z","timestamp":1760144794000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8353"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,31]]},"references-count":40,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218353"],"URL":"https:\/\/doi.org\/10.3390\/s22218353","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,10,31]]}}}