{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:10:19Z","timestamp":1784301019502,"version":"3.55.0"},"reference-count":47,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T00:00:00Z","timestamp":1685059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20163"],"award-info":[{"award-number":["U20A20163"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["62201066"],"award-info":[{"award-number":["62201066"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["KZ202111232049"],"award-info":[{"award-number":["KZ202111232049"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003213","name":"Scientific Research Project of Beijing Municipal Education Commission","doi-asserted-by":"publisher","award":["U20A20163"],"award-info":[{"award-number":["U20A20163"]}],"id":[{"id":"10.13039\/501100003213","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003213","name":"Scientific Research Project of Beijing Municipal Education Commission","doi-asserted-by":"publisher","award":["62201066"],"award-info":[{"award-number":["62201066"]}],"id":[{"id":"10.13039\/501100003213","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003213","name":"Scientific Research Project of Beijing Municipal Education Commission","doi-asserted-by":"publisher","award":["KZ202111232049"],"award-info":[{"award-number":["KZ202111232049"]}],"id":[{"id":"10.13039\/501100003213","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a human activity recognition (HAR) method for frequency-modulated continuous wave (FMCW) radar sensors. The method utilizes a multi-domain feature attention fusion network (MFAFN) model that addresses the limitation of relying on a single range or velocity feature to describe human activity. Specifically, the network fuses time-Doppler (TD) and time-range (TR) maps of human activities, resulting in a more comprehensive representation of the activities being performed. In the feature fusion phase, the multi-feature attention fusion module (MAFM) combines features of different depth levels by introducing a channel attention mechanism. Additionally, a multi-classification focus loss (MFL) function is applied to classify confusable samples. The experimental results demonstrate that the proposed method achieves 97.58% recognition accuracy on the dataset provided by the University of Glasgow, UK. Compared to existing HAR methods for the same dataset, the proposed method showed an improvement of about 0.9\u20135.5%, especially in the classification of confusable activities, showing an improvement of up to 18.33%.<\/jats:p>","DOI":"10.3390\/s23115100","type":"journal-article","created":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T16:18:43Z","timestamp":1685204323000},"page":"5100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Human Activity Recognition Method Based on FMCW Radar Sensor with Multi-Domain Feature Attention Fusion Network"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0875-1549","authenticated-orcid":false,"given":"Lin","family":"Cao","sequence":"first","affiliation":[{"name":"The Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing 100101, China"},{"name":"The Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Liang","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing 100101, China"},{"name":"The Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongmin","family":"Zhao","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing 100101, China"},{"name":"The Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing TransMicrowave Technology Company, Beijing 100080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4549-744X","authenticated-orcid":false,"given":"Chong","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kangning","family":"Du","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing 100101, China"},{"name":"The Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"85334","DOI":"10.1109\/ACCESS.2021.3088452","article-title":"On the Generalization and Reliability of Single Radar-Based Human Activity Recognition","volume":"9","author":"Gorji","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"He, Y., Li, X., and Jing, X. (2019). A Mutiscale Residual Attention Network for Multitask Learning of Human Activity Using Radar Micro-Doppler Signatures. Remote Sens., 11.","DOI":"10.3390\/rs11212584"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shahmohammadi, F., Hosseini, A., King, C.E., and Sarrafzadeh, M. (2017, January 17\u201319). Smartwatch based activity recognition using active learning. Proceedings of the 2017 IEEE\/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), IEEE, Philadelphia, PA, USA.","DOI":"10.1109\/CHASE.2017.115"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Habib, S., Hussain, A., Albattah, W., Islam, M., Khan, S., Khan, R.U., and Khan, K. (2021). Abnormal Activity Recognition from Surveillance Videos Using Convolutional Neural Network. Sensors, 21.","DOI":"10.3390\/s21248291"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, X., He, Y., and Jing, X. (2019). A Survey of Deep Learning-Based Human Activity Recognition in Radar. Remote Sens., 11.","DOI":"10.3390\/rs11091068"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"24471","DOI":"10.1109\/JSEN.2021.3113908","article-title":"A Lightweight Framework for Human Activity Recognition on Wearable Devices","volume":"21","author":"Coelho","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"160635","DOI":"10.1109\/ACCESS.2021.3132559","article-title":"Maximum Entropy Markov Model for Human Activity Recognition Using Depth Camera","volume":"9","author":"Alrashdi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1109\/TAES.2017.2761229","article-title":"Sparsity-driven micro-Doppler feature extraction for dynamic hand gesture recog-nition","volume":"54","author":"Li","year":"2018","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1109\/LGRS.2018.2806940","article-title":"Personnel Recognition and Gait Classification Based on Multistatic Micro-Doppler Signatures Using Deep Convolutional Neural Networks","volume":"15","author":"Chen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3154832","article-title":"DIAT-RadHARNet: A Lightweight DCNN for Radar Based Classification of Human Suspicious Activities","volume":"71","author":"Chakraborty","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Taylor, W., Dashtipour, K., Shah, S.A., Hussain, A., Abbasi, Q.H., and Imran, M.A. (2021). Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning. Sensors, 21.","DOI":"10.3390\/s21113881"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Senigagliesi, L., Ciattaglia, G., Disha, D., and Gambi, E. (2022, January 21\u201325). Classification of Human Activities based on Automotive Radar Spectral Images Using Machine Learning Techniques: A Case Study. Proceedings of the 2022 IEEE Radar Conference (RadarConf22), New York, NY, USA.","DOI":"10.1109\/RadarConf2248738.2022.9764217"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, Z., Wang, Y., Xie, G., Lin, Y., Shen, W., and Jiang, W. (2023). Improving the Performance of RODNet for MMW Radar Target Detection in Dense Pedestrian Scene. Mathematics, 11.","DOI":"10.3390\/math11020361"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Sun, M., Xu, Z., Sun, B., and Zhang, S. (2021, January 20\u201322). FMCW Multi-Person Action Recognition System Based on Point Cloud Nearest Neighbor Sam-pling Algorithm. Proceedings of the 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), Yibin, China.","DOI":"10.1109\/PRAI53619.2021.9551097"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6605","DOI":"10.1109\/JSEN.2020.2977170","article-title":"Multi-Person Recognition Using Separated Micro-Doppler Signatures","volume":"20","author":"Huang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Saeed, U., Shah, S.Y., Shah, S.A., Ahmad, J., Alotaibi, A.A., Althobaiti, T., Ramzan, N., Alomainy, A., and Abbasi, Q.H. (2021). Discrete human activity recognition and fall detection by combining FMCW RADAR data of heterogeneous environments for independent assistive living. Electronics, 10.","DOI":"10.3390\/electronics10182237"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"24713","DOI":"10.1109\/ACCESS.2020.2971064","article-title":"A Hybrid CNN\u2013LSTM Network for the Classification of Human Activities Based on Micro-Doppler Radar","volume":"8","author":"Zhu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"7590","DOI":"10.1109\/JSEN.2020.3046991","article-title":"Sequential Human Gait Classification with Distributed Radar Sensor Fusion","volume":"21","author":"Li","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gorji, A., Gielen, T., Bauduin, M., Sahli, H., and Bourdoux, A. (2021, January 7\u201314). A Multi-radar Architecture for Human Activity Recognition in Indoor Kitchen Envi-ronments. Proceedings of the 2021 IEEE Radar Conference (RadarConf21), Virtual Event.","DOI":"10.1109\/RadarConf2147009.2021.9455238"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/JERM.2018.2827099","article-title":"A Multisensory Approach for Remote Health Monitoring of Older People","volume":"2","author":"Li","year":"2018","journal-title":"IEEE J. Electromagn. RF Microw. Med. Biol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8979","DOI":"10.1109\/JSEN.2018.2872894","article-title":"Magnetic and Radar Sensing for Multimodal Remote Health Monitoring","volume":"19","author":"Li","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1109\/JSEN.2019.2946095","article-title":"Bi-LSTM network for multimodal continuous human activity recognition and fall detec-tion","volume":"20","author":"Li","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4494","DOI":"10.1109\/JSEN.2022.3140787","article-title":"A Convolutional Neural Network for Human Motion Recognition and Classification Using a Millimeter-Wave Doppler Radar","volume":"22","author":"Arab","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_26","unstructured":"Zhang, X., Abbasi, Q.H., Fioranelli, F., Romain, O., and Le Kernec, J. (2022). Body Area Networks. Smart IoT and Big Data for Intelligent Health Management, Proceedings of the 16th EAI International Conference, BODYNETS 2021, Virtual Event, 25\u201326 October 2021, Springer International Publishing."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, Z., Fioranelli, F., Yang, S., Zhang, L., Romain, O., He, Q., Cui, G., and Le Kernec, J. (2020, January 4\u20136). Multi-domains based human activity classification in radar. Proceedings of the IET International Radar Conference (IET IRC 2020), Online Event.","DOI":"10.1049\/icp.2021.0557"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2889","DOI":"10.1109\/TAES.2021.3068436","article-title":"Radar-Based Human Activity Recognition Using Hybrid Neural Network Model with Multidomain Fusion","volume":"57","author":"Ding","year":"2021","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_29","first-page":"1","article-title":"GCN-Enhanced Multi-domain Fusion Network for Through-wall Human Activity Recognition","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9767","DOI":"10.1109\/TGRS.2019.2929096","article-title":"Radar-Based Human Gait Recognition Using Dual-Channel Deep Convolutional Neural Network","volume":"57","author":"Bai","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jia, M., Li, S., Le Kernec, J., Yang, S., Fioranelli, F., and Romain, O. (2020, January 20\u201321). Human activity classification with radar signal processing and machine learning. Proceedings of the 2020 International conference on UK-China Emerging Technologies (UCET), Glasgow, UK.","DOI":"10.1109\/UCET51115.2020.9205461"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhao, Y., and Hu, W. (2021, January 17\u201319). CentralNet Method for Human motion Recognition Based on Multi-feature Fusion of Millimeter Wave Radar. Proceedings of the 2021 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Virtual Event.","DOI":"10.1109\/ICSPCC52875.2021.9564487"},{"key":"ref_33","first-page":"1","article-title":"A Multi-Domain Fusion Human Motion Recognition Method Based on Lightweight Network","volume":"19","author":"Chen","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1109\/LGRS.2020.2993039","article-title":"Narrowband Radar Automatic Target Recognition Based on a Hierarchical Fusing Network with Multidomain Features","volume":"18","author":"Gao","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","first-page":"1","article-title":"Activity recognition of FMCW radar human signatures using tower convolutional neural networks","volume":"2021","author":"Maragatham","year":"2021","journal-title":"Wirel. Netw."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, W., Liu, Q., Sun, Y., Tang, Z., and Zhu, Z. (2020, January 4\u20136). Human activity classification with radar based on Multi-CNN information fusion. Proceedings of the IET International Radar Conference (IET IRC 2020), Virtual Event.","DOI":"10.1049\/icp.2021.0676"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jokanovic, B., Amin, M., and Erol, B. (2017, January 8\u201312). Multiple Joint-Variable Domains Recognition of Human Motion. Proceedings of the 2017 IEEE Radar Conference, Seattle, WA, USA.","DOI":"10.1109\/RADAR.2017.7944340"},{"key":"ref_38","first-page":"1","article-title":"Radar-Based Human Activity Recognition Combining Range\u2013Time\u2013Doppler Maps and Range-Distributed-Convolutional Neural Networks","volume":"Volume 60","author":"Kim","year":"2022","journal-title":"Proceedings of the IEEE Transactions on Geoscience and Remote Sensing"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"144610","DOI":"10.1109\/ACCESS.2020.3010063","article-title":"Enhanced Multi-Channel Feature Synthesis for Hand Gesture Recognition Based on CNN with a Channel and Spatial Attention Mechanism","volume":"8","author":"Du","year":"2020","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Campbell, C., and Ahmad, F. (2020, January 4\u20136). Attention-augmented convolutional autoencoder for radar-based human activity recognition. Proceedings of the 2020 IEEE International Radar Conference (RADAR), Virtual Event.","DOI":"10.1109\/RADAR42522.2020.9114787"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1049\/iet-rsn.2013.0165","article-title":"Classification of human motions using empirical mode decomposition of human micro-Doppler signatures","volume":"8","author":"Fairchild","year":"2014","journal-title":"IET Radar Sonar Navig."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_43","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_46","first-page":"012043","article-title":"Age classification using convolutional neural networks with the multi-class focal loss","volume":"Volume 428","author":"Liu","year":"2018","journal-title":"Materials Science and Engineering"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shah, S.A., and Fioranelli, F. (2019, January 23\u201327). Human activity recognition: Preliminary results for dataset portability using FMCW radar. Proceedings of the 2019 International Radar Conference (RADAR), Toulon, France.","DOI":"10.1109\/RADAR41533.2019.171307"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5100\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:43:00Z","timestamp":1760125380000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,26]]},"references-count":47,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115100"],"URL":"https:\/\/doi.org\/10.3390\/s23115100","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,26]]}}}