{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T14:02:19Z","timestamp":1790258539808,"version":"4.1.0"},"reference-count":29,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Provincial College Students\u2019 Innovation and Entrepreneurship Training Program","award":["S202610304066"],"award-info":[{"award-number":["S202610304066"]}]},{"name":"Jiangsu Provincial College Students\u2019 Innovation and Entrepreneurship Training Program","award":["S202610304067"],"award-info":[{"award-number":["S202610304067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Millimeter-wave (mmWave) radar enables non-contact human identification by exploiting gait micro-Doppler signatures without capturing visual imagery. Nevertheless, practical deployment encounters several critical challenges: strong torso echoes that obscure faint limb-motion features, channel-dependent responses caused by unconstrained walking styles, and computational limitations on edge hardware. This paper proposes a lightweight gait-based human identification method for multiple-input multiple-output (MIMO) radar systems, which integrates adaptive Gamma correction, multi-transmit-channel differential encoding, and an asymmetric-pooling convolutional recurrent neural network (Asym-CRNN). Specifically, the differences across the three transmit-channel responses of the three-transmit four-receive (3T4R) radar are encoded into a three-channel spatiotemporal representation, and the compact network retains temporal resolution to facilitate efficient gait discrimination. Evaluated on a self-collected five-subject dataset with acquisition-sequence-level train-test separation, the proposed method achieves closed-set accuracies of 89.62% and 99.52% under 1 s and 6 s observation windows, respectively. For open-set scenarios, the method attains a registered-versus-unregistered area under the receiver operating characteristic curve (AUROC) of 0.9869. With a maximum softmax probability (MSP) threshold of 0.85, the method rejects 91.88% of unregistered human samples and all tested pet-interference samples. Furthermore, the model achieves an accuracy of 86.98% using only single-channel IDRad inputs. The exported model requires merely 4.32 MB of storage. Deployed on a Raspberry Pi 5 platform, the acquisition-processing pipeline runs using only two CPU cores with CPU-only execution: one core continuously receives and buffers raw radar data, whereas the other processes previously acquired observation windows. The per-window processing latency reaches 715.7 ms for the 1 s window and 4366.2 ms for the 6 s window, including network inference times of 6.7 ms and 15.2 ms, respectively. The non-causal bidirectional long short-term memory (Bi-LSTM) generates one identity decision once each observation window is fully collected. These results verify the feasibility of the proposed method for privacy-preserving indoor monitoring at small scales on resource-constrained edge devices.<\/jats:p>","DOI":"10.3390\/s26196056","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:26:11Z","timestamp":1790256371000},"page":"6056","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lightweight Gait-Based Person Identification via Transmit-Channel Differential Encoding with MIMO Millimeter-Wave Radar"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1491-5346","authenticated-orcid":false,"given":"Hongming","family":"Shen","sequence":"first","affiliation":[{"name":"School of Microelectronics and School of Integrated Circuits, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"given":"Chenxiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Microelectronics and School of Integrated Circuits, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"given":"Zhiming","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Microelectronics and School of Integrated Circuits, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"given":"Nanfeng","family":"Mi","sequence":"additional","affiliation":[{"name":"School of Microelectronics and School of Integrated Circuits, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Lei","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Microelectronics and School of Integrated Circuits, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"given":"Mei","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Microelectronics and School of Integrated Circuits, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"given":"Xia","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Microelectronics and School of Integrated Circuits, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gu, C. (2016). Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review. Sensors, 16.","DOI":"10.3390\/s16081169"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1145\/3627161","article-title":"Non-Intrusive Human Vital Sign Detection Using mmWave Sensing Technologies: A Review","volume":"20","author":"Wu","year":"2024","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2948","DOI":"10.1109\/JSEN.2022.3141213","article-title":"Micro-Doppler Based Target Recognition with Radars: A Review","volume":"22","author":"Hanif","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yoon, S., Kim, D., Hong, J.W., Kim, J., and Yoo, C.D. (2022). Dual-Scale Doppler Attention for Human Identification. Sensors, 22.","DOI":"10.3390\/s22176363"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dang, X., Tang, Y., Hao, Z., Gao, Y., Fan, K., and Wang, Y. (2024). PGGait: Gait Recognition Based on Millimeter-Wave Radar Spatio-Temporal Sensing of Multidimensional Point Clouds. Sensors, 24.","DOI":"10.3390\/s24010142"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Papanastasiou, V.S., Trommel, R.P., Harmanny, R.I.A., and Yarovoy, A. (2021, January 13\u201315). Deep Learning-Based Identification of Human Gait by Radar Micro-Doppler Measurements. Proceedings of the 2020 17th European Radar Conference (EuRAD), Utrecht, The Netherlands.","DOI":"10.1109\/EuRAD48048.2021.00024"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Addabbo, P., Bernardi, M.L., Biondi, F., Cimitile, M., Clemente, C., and Orlando, D. (2021). Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition. Sensors, 21.","DOI":"10.3390\/s21020381"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3941","DOI":"10.1109\/TGRS.2018.2816812","article-title":"Indoor Person Identification Using a Low-Power FMCW Radar","volume":"56","author":"Vandersmissen","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8227","DOI":"10.1109\/JSEN.2024.3355421","article-title":"mDS-PCGR: A Bimodal Gait Recognition Framework with the Fusion of 4-D Radar Point Cloud Sequences and Micro-Doppler Signatures","volume":"24","author":"Ma","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5101316","DOI":"10.1109\/TGRS.2023.3345829","article-title":"Fine-Grained Spatial-Temporal Gait Recognition Network Based on Millimeter-Wave Radar Point Cloud","volume":"62","author":"Xue","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yan, J., Wang, Y., Xu, C., and Liu, D. (2026, January 3\u20138). OG-PCL: Efficient Sparse Radar Point Cloud Processing for Human Activity Recognition. Proceedings of the 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP55912.2026.11462700"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1109\/LMWC.2019.2907547","article-title":"Person Identification Using Micro-Doppler Signatures of Human Motions and UWB Radar","volume":"29","author":"Yang","year":"2019","journal-title":"IEEE Microw. Wirel. Compon. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5782","DOI":"10.1364\/AO.462299","article-title":"Human Recognition with the Optoelectronic Reservoir-Computing-Based Micro-Doppler Radar Signal Processing","volume":"61","author":"Feng","year":"2022","journal-title":"Appl. Opt."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"111101","DOI":"10.1016\/j.patcog.2024.111101","article-title":"Radar Gait Recognition Using Dual-Branch Swin Transformer with Asymmetric Attention Fusion","volume":"159","author":"He","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, G., Xu, Z., and Chen, L. (2025). Gait Recognition Using Millimeter-Wave Radar Point Clouds with an Attention-Based C3D Network. Proceedings of the 2025 Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC), Guangzhou, China, 14\u201316 November 2025, IEEE.","DOI":"10.1109\/CSRSWTC67757.2025.11384354"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"24057","DOI":"10.1109\/JIOT.2025.3554076","article-title":"mmReID: Person Reidentification Based on Commodity Millimeter-Wave Radar","volume":"12","author":"Han","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"38135","DOI":"10.1109\/JSEN.2024.3454714","article-title":"A Stable Gait Recognition Algorithm Under Multiview and Multiwear Using Millimeter-Wave Radar","volume":"24","author":"Ding","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jeon, M., and Woo, S. (2026). A Lightweight Radar-Camera Fusion Deep Learning Model for Human Activity Recognition. Sensors, 26.","DOI":"10.3390\/s26030894"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"24916","DOI":"10.1109\/JSEN.2023.3310620","article-title":"Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning","volume":"23","author":"Waqar","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1109\/JSEN.2024.3483835","article-title":"Video2mmPoint: Synthesizing mmWave Point Cloud Data from Videos for Gait Recognition","volume":"25","author":"Fan","year":"2025","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ayaz, F., Alhumaily, B., Hussain, S., Imran, M.A., and Zoha, A. (2024). Real-Time Lightweight Deep Learning Models for Human Activity Recognition Using FMCW Radar. TechRxiv.","DOI":"10.36227\/techrxiv.171924983.35313177\/v1"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kim, D., Lee, S., and Lee, M. (2026). A Real-Time Multi-Class Human Activity Monitoring System Using mmWave Radar. Sensors, 26.","DOI":"10.3390\/s26103145"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"33051","DOI":"10.1109\/JSEN.2025.3587503","article-title":"Open-Set Gait Recognition from Sparse mmWave Radar Point Clouds","volume":"25","author":"Mazzieri","year":"2025","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"25134","DOI":"10.1109\/JSEN.2025.3571781","article-title":"Few-Shot Open-Set Gait Recognition Based on Radar Micro-Doppler Signatures","volume":"25","author":"Yang","year":"2025","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5229","DOI":"10.1109\/TMC.2025.3529735","article-title":"Open-Set Occluded Person Identification with mmWave Radar","volume":"24","author":"Wang","year":"2025","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"38275","DOI":"10.1109\/ACCESS.2024.3352022","article-title":"Causal Localization Network for Radar Human Localization with Micro-Doppler Signature","volume":"12","author":"Yoon","year":"2024","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xie, J., Huang, J., Xu, C., Wan, H., Zuo, X., and Dong, G. (2026). MARU-MTL: A Mamba-Enhanced Multi-Task Learning Framework for Continuous Blood Pressure Estimation Using Radar Pulse Waves. Bioengineering, 13.","DOI":"10.3390\/bioengineering13030320"},{"key":"ref_29","unstructured":"Lyu, Z., Cao, X., Song, X., Li, Y., Wang, J., Wang, S., Cui, Y., Yuan, W., Yu, X., and Zhu, G. (2026). Sense Smarter, Think Better: A Survey on Edge Perception for Next-Generation Networks. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6056\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:28:14Z","timestamp":1790256494000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":29,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196056"],"URL":"https:\/\/doi.org\/10.3390\/s26196056","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]}}}