{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:42:50Z","timestamp":1760150570297,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","award":["CELL-LESS6G (2022.08786.PTDC)","UIDB\/50008\/2020"],"award-info":[{"award-number":["CELL-LESS6G (2022.08786.PTDC)","UIDB\/50008\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper explores the opportunities and challenges for classifying human posture in indoor scenarios by analyzing the Frequency-Modulated (FM) radio broadcasting signal received at multiple locations. More specifically, we present a passive RF testbed operating in FM radio bands, which allows experimentation with innovative human posture classification techniques. After introducing the details of the proposed testbed, we describe a simple methodology to detect and classify human posture. The methodology includes a detailed study of feature engineering and the assumption of three traditional classification techniques. The implementation of the proposed methodology in software-defined radio devices allows an evaluation of the testbed\u2019s capability to classify human posture in real time. The evaluation results presented in this paper confirm that the accuracy of the classification can be approximately 90%, showing the effectiveness of the proposed testbed and its potential to support the development of future innovative classification techniques by only sensing FM bands in a passive mode.<\/jats:p>","DOI":"10.3390\/s23239563","type":"journal-article","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T10:39:48Z","timestamp":1701427188000},"page":"9563","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Passive RF Testbed for Human Posture Classification in FM Radio Bands"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6072-7614","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Pereira","sequence":"first","affiliation":[{"name":"Departamento de Engenharia Electrot\u00e9cnica e de Computadores, Faculdade de Ci\u00eancias e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, 1049-001 Lisbon, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3233-2408","authenticated-orcid":false,"given":"Eugene","family":"Casmin","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia Electrot\u00e9cnica e de Computadores, Faculdade de Ci\u00eancias e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, 1049-001 Lisbon, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9181-8438","authenticated-orcid":false,"given":"Rodolfo","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia Electrot\u00e9cnica e de Computadores, Faculdade de Ci\u00eancias e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, 1049-001 Lisbon, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"30845","DOI":"10.1109\/ACCESS.2021.3059488","article-title":"Joint Design of Communication and Sensing for beyond 5G and 6G Systems","volume":"9","author":"Wild","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6821","DOI":"10.1109\/TGRS.2019.2908758","article-title":"Continuous Human Motion Recognition with a Dynamic Range-Doppler Trajectory Method Based on FMCW Radar","volume":"57","author":"Ding","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MSP.2018.2890128","article-title":"Radar-Based Human-Motion Recognition with Deep Learning: Promising Applications for Indoor Monitoring","volume":"36","author":"Gurbuz","year":"2019","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"33275","DOI":"10.1109\/ACCESS.2018.2844882","article-title":"Context-Aware Indoor VLC\/RF Heterogeneous Network Selection: Reinforcement Learning with Knowledge Transfer","volume":"6","author":"Du","year":"2018","journal-title":"IEEE Access"},{"key":"ref_5","first-page":"3763","article-title":"American Sign Language Recognition Using RF Sensing","volume":"21","author":"Gurbuz","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TGRS.2009.2012849","article-title":"Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine","volume":"47","author":"Kim","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","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 Sens. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hyun, E., and Jin, Y. (2020). Doppler-Spectrum Feature-Based Human\u2013Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor. Sensors, 20.","DOI":"10.3390\/s20072001"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cruz, R., Furtado, A., and Oliveira, R. (2022, January 19\u201322). Assessment of Feature Selection for Context Awareness RF Sensing Systems. Proceedings of the 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), Helsinki, Finland.","DOI":"10.1109\/VTC2022-Spring54318.2022.9860673"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/JIOT.2016.2624800","article-title":"Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces","volume":"4","author":"Kianoush","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, J., Wang, L., Fang, J., Guo, L., Lu, B., and Shu, L. (2018). Multi-Target Intense Human Motion Analysis and Detection Using Channel State Information. Sensors, 18.","DOI":"10.3390\/s18103379"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Iqbal, S., Iqbal, U., Hassan, S.A., and Saleem, S. (2018, January 3\u20136). Indoor Motion Classification Using Passive RF Sensing Incorporating Deep Learning. Proceedings of the 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), Porto, Portugal.","DOI":"10.1109\/VTCSpring.2018.8417859"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.1109\/TNNLS.2020.3042908","article-title":"Harvesting Ambient RF for Presence Detection through Deep Learning","volume":"33","author":"Liu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sigg, S., Shi, S., Buesching, F., Ji, Y., and Wolf, L. (2013, January 2\u20134). Leveraging RF-Channel Fluctuation for Activity Recognition: Active and Passive Systems, Continuous and RSSI-Based Signal Features. Proceedings of the International Conference on Advances in Mobile Computing & Multimedia, Vienna, Austria.","DOI":"10.1145\/2536853.2536873"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1109\/TMC.2016.2557792","article-title":"WiFall: Device-Free Fall Detection by Wireless Networks","volume":"16","author":"Wang","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_16","unstructured":"Radio, G. (2023, August 18). GNU Radio-The Free & Open Source Radio Ecosystem. Available online: https:\/\/www.gnuradio.org\/."},{"key":"ref_17","unstructured":"Smith, C. (2023, August 18). GitHub-Gnuradio\/Pybombs. Available online: https:\/\/github.com\/gnuradio\/pybombs."},{"key":"ref_18","unstructured":"Wikipedia (2023, August 18). Analysis of Variance. Available online: https:\/\/en.wikipedia.org\/wiki\/Analysis_of_variance."},{"key":"ref_19","unstructured":"Scikit-Learn (2023, August 22). Scikit-Learn Machine Learning in Python. Available online: https:\/\/scikit-learn.org\/stable\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9563\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:36:24Z","timestamp":1760132184000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9563"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,1]]},"references-count":19,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23239563"],"URL":"https:\/\/doi.org\/10.3390\/s23239563","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,12,1]]}}}