{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:03:17Z","timestamp":1760144597424,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,28]],"date-time":"2024-04-28T00:00:00Z","timestamp":1714262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971430"],"award-info":[{"award-number":["61971430"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Human pose estimation (HPE) is an integral component of numerous applications ranging from healthcare monitoring to human-computer interaction, traditionally relying on vision-based systems. These systems, however, face challenges such as privacy concerns and dependency on lighting conditions. As an alternative, short-range radar technology offers a non-invasive, lighting-insensitive solution that preserves user privacy. This paper presents a novel radar-based framework for HPE, SCRP-Radar (space-aware coordinate representation for human pose estimation using single-input single-output (SISO) ultra-wideband (UWB) radar). The methodology begins with clutter suppression and denoising techniques to enhance the quality of radar echo signals, followed by the construction of a micro-Doppler (MD) matrix from these refined signals. This matrix is segmented into bins to extract distinctive features that are critical for pose estimation. The SCRP-Radar leverages the Hrnet and LiteHrnet networks, incorporating space-aware coordinate representation to reconstruct 2D human poses with high precision. Our method redefines HPE as dual classification tasks for vertical and horizontal coordinates, which is a significant departure from existing methods such as RF-Pose, RF-Pose 3D, UWB-Pose, and RadarFormer. Extensive experimental evaluations demonstrate that SCRP-Radar significantly surpasses these methods in accuracy and robustness, consistently exhibiting lower average error rates, achieving less than 40 mm across 17 skeletal key-points. This innovative approach not only enhances the precision of radar-based HPE but also sets a new benchmark for future research and application, particularly in sectors that benefit from accurate and privacy-preserving monitoring technologies.<\/jats:p>","DOI":"10.3390\/rs16091572","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T04:26:16Z","timestamp":1714364776000},"page":"1572","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SCRP-Radar: Space-Aware Coordinate Representation for Human Pose Estimation Based on SISO UWB Radar"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8197-789X","authenticated-orcid":false,"given":"Xiaolong","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4142-6265","authenticated-orcid":false,"given":"Yongpeng","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongping","family":"Song","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kemeng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5217915","DOI":"10.1109\/TGRS.2023.3322554","article-title":"Sparse Logistic Regression-Based One-Bit SAR Imaging","volume":"61","author":"Ge","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1075538","DOI":"10.3389\/fnins.2022.1075538","article-title":"Millimeter-wave radar object classification using knowledge-assisted neural network","volume":"16","author":"Wang","year":"2022","journal-title":"Front. Neurosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104282","DOI":"10.1016\/j.imavis.2021.104282","article-title":"A review of deep learning techniques for 2D and 3D human pose estimation","volume":"114","author":"Gamra","year":"2021","journal-title":"Image Vis. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1109\/TMM.2017.2762010","article-title":"Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation","volume":"20","author":"Ning","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jiang, W., Xue, H., Miao, C., Wang, S., Lin, S., Tian, C., Murali, S., Hu, H., Sun, Z., and Su, L. (2020, January 21\u201325). Towards 3d human pose construction using wifi. Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, New York, NY, USA.","DOI":"10.1145\/3372224.3380900"},{"key":"ref_6","unstructured":"Wang, F., Zhou, S., Panev, S., Han, J., and Huang, D. (November, January 27). Person-in-WiFi: Fine-grained person perception using WiFi. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1218","DOI":"10.1109\/TR.2020.3030952","article-title":"RFID-pose: Vision-aided three-dimensional human pose estimation with radio-frequency identification","volume":"70","author":"Yang","year":"2020","journal-title":"IEEE Trans. Reliab."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, M., Li, T., Abu Alsheikh, M., Tian, Y., Zhao, H., Torralba, A., and Katabi, D. (2018, January 18\u201323). Through-wall human pose estimation using radio signals. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00768"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhao, M., Tian, Y., Zhao, H., Alsheikh, M.A., Li, T., Hristov, R., Kabelac, Z., Katabi, D., and Torralba, A. (2018, January 20\u201325). RF-based 3D skeletons. Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, Budapest, Hungary.","DOI":"10.1145\/3230543.3230579"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2816795.2818072","article-title":"Capturing the human figure through a wall","volume":"6","author":"Adib","year":"2015","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TBIOM.2023.3265206","article-title":"MD-Pose: Human Pose Estimation for Single-Channel UWB Radar","volume":"5","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhou, X., Jin, T., and Du, H. (2020, January 4\u20136). A lightweight network model for human activity classifiction based on pre-trained mobilenetv2. Proceedings of the IET International Radar Conference (IET IRC 2020), Chongqing, China.","DOI":"10.1049\/icp.2021.0595"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Qi, F., Lv, H., Liang, F., Li, Z., Yu, X., and Wang, J. (2017). MHHT-based method for analysis of micro-Doppler signatures for human finer-grained activity using through-wall SFCW radar. Remote Sens., 9.","DOI":"10.3390\/rs9030260"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, S., Liu, P., Zhang, S., Wang, Y., Wang, Z., Yang, W., and Xia, S.T. (2022, January 23\u201324). Simcc: A simple coordinate classification perspective for human pose estimation. Proceedings of the 2022 European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20068-7_6"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Toshev, A., and Szegedy, C. (2014, January 23\u201328). Deeppose: Human pose estimation via deep neural networks. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.214"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xiao, B., Wu, H., and Wei, Y. (2018, January 8\u201314). Simple baselines for human pose estimation and tracking. Proceedings of the 2018 European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01231-1_29"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_18","unstructured":"Wang, F., Panev, S., Dai, Z., Han, J., and Huang, D. (2019). Can WiFi estimate person pose?. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1109\/LCOMM.2019.2961890","article-title":"From signal to image: Capturing fine-grained human poses with commodity Wi-Fi","volume":"24","author":"Guo","year":"2019","journal-title":"IEEE Commun. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.1109\/LCOMM.2021.3073271","article-title":"From point to space: 3D moving human pose estimation using commodity WiFi","volume":"25","author":"Wang","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, K., Wang, Q., Xue, F., and Chen, W. (2020, January 11\u201314). 3D-skeleton estimation based on commodity millimeter wave radar. Proceedings of the 2020 IEEE 6th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/ICCC51575.2020.9345237"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8418","DOI":"10.1109\/TNNLS.2022.3151101","article-title":"mmpose-nlp: A natural language processing approach to precise skeletal pose estimation using mmwave radars","volume":"34","author":"Sengupta","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10032","DOI":"10.1109\/JSEN.2020.2991741","article-title":"mm-Pose: Real-time human skeletal posture estimation using mmWave radars and CNNs","volume":"20","author":"Sengupta","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100228","DOI":"10.1016\/j.smhl.2021.100228","article-title":"mPose: Environment-and subject-agnostic 3D skeleton posture reconstruction leveraging a single mmWave device","volume":"23","author":"Shi","year":"2022","journal-title":"Smart Health"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sengupta, A., Jin, F., and Cao, S. (2020, January 21\u201325). NLP based skeletal pose estimation using mmWave radar point-cloud: A simulation approach. Proceedings of the 2020 IEEE Radar Conference (RadarConf20), Florence, Italy.","DOI":"10.1109\/RadarConf2043947.2020.9266600"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"23174","DOI":"10.1109\/JSEN.2021.3107361","article-title":"Radar-based 3D human skeleton estimation by kinematic constrained learning","volume":"21","author":"Ding","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/JSEN.2021.3127937","article-title":"real-time short-range human posture estimation using mmWave radars and neural networks","volume":"22","author":"Cui","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, T., Fan, L., Yuan, Y., and Katabi, D. (2022, January 4\u20138). Unsupervised learning for human sensing using radio signals. Proceedings of the 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00116"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Song, Y., Jin, T., Dai, Y., Song, Y., and Zhou, X. (2022). Through-wall human pose reconstruction via UWB MIMO radar and 3D CNN. Remote Sens., 13.","DOI":"10.3390\/rs13020241"},{"key":"ref_30","first-page":"3505205","article-title":"Human posture reconstruction for through-the-wall radar imaging using convolutional neural networks","volume":"19","author":"Zheng","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"15082","DOI":"10.1109\/ACCESS.2023.3244017","article-title":"A Study on 3D Human Pose Estimation Using Through-Wall IR-UWB Radar and Transformer","volume":"11","author":"Kim","year":"2023","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, F., Zhu, X., Dai, H., Ye, M., and Zhu, C. (2020, January 13\u201319). Distribution-aware coordinate representation for human pose estimation. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00712"},{"key":"ref_33","unstructured":"Yang, S., Quan, Z., Nie, M., and Yang, W. (2020). Transpose: Towards explainable human pose estimation by transformer. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, S., Wang, Z., Yang, S., Yang, W., Xia, S.T., and Zhou, E. (2021, January 10\u201317). Tokenpose: Learning keypoint tokens for human pose estimation. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01112"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, J., Bian, S., Zeng, A., Wang, C., Pang, B., Liu, W., and Lu, C. (2021, January 10\u201317). Human pose regression with residual log-likelihood estimation. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01084"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8648","DOI":"10.1109\/JSEN.2022.3156762","article-title":"Activity classification based on feature fusion of FMCW radar human motion micro-Doppler signatures","volume":"22","author":"Abdu","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hassan, S., Wang, X., Ishtiaq, S., Ullah, N., Mohammad, A., and Noorwali, A. (2023). Human Activity Classification Based on Dual Micro-Motion Signatures Using Interferometric Radar. Remote Sens., 15.","DOI":"10.3390\/rs15071752"},{"key":"ref_38","first-page":"5103112","article-title":"Semisupervised human activity recognition with radar micro-Doppler signatures","volume":"60","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","first-page":"1","article-title":"Spatiotemporal Weighted Micro-Doppler Spectrum Design for Soft Synchronization FMCW Radar","volume":"72","author":"Li","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhang, D., and Liang, X. (2023). RadarFormer: End-to-End Human Perception with Through-Wall Radar and Transformers. IEEE Trans. Neural Netw. Learn. Syst., 1\u201315.","DOI":"10.1109\/TNNLS.2023.3314031"},{"key":"ref_41","unstructured":"Yuan, Y., Fu, R., and Huang, L. (2021, January 6\u201314). Hrformer: High-resolution transformer for dense prediction. Proceedings of the Thirty-Fifth Conference on Neural Information Processing Systems, Online Conference."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wei, S.E., Ramakrishna, V., and Kanade, T. (2016, January 27\u201330). Convolutional pose machines. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.511"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., and Zhu, M. (2018, January 28\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xu, L., Guan, Y., and Jin, S. (2021, January 20\u201325). Vipnas: Efficient video pose estimation via neural architecture search. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01581"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1572\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:35:30Z","timestamp":1760106930000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1572"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,28]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16091572"],"URL":"https:\/\/doi.org\/10.3390\/rs16091572","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,4,28]]}}}