{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T16:25:32Z","timestamp":1754151932389,"version":"3.41.2"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Embed. Comput. Syst."],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>A significant challenge currently facing Wi-Fi-based gait recognition technology is that changes in walking paths in a multipath environment can significantly interfere with the CSI gait signal collected via Wi-Fi, which greatly hinders the application of this technology in real life. To deal with this problem, most existing Wi-Fi gait recognition systems adopt the strategy of fixing walking paths or using multiple receivers, but these methods undoubtedly increase the complexity and cost of the system. This article proposes an innovative solution: an identification system independent of the walking path and requires only a pair of transceivers. The system is based on the identification of the IQ signal density characteristics. Specifically, the CSI signal is first decomposed and reconstructed using VMD technology, eliminating noise interference in a multipath environment. A unique point density feature is extracted from the IQ signal, which integrates both phase and amplitude information. This feature effectively distinguishes gait and is not influenced by changes in walking paths. At the same time, it can visually highlight the commonalities and differences when depicting the same person and distinguishing between different individuals' gaits, providing a strong basis for gait recognition classification. Finally, the deep learning model was introduced into the identification process, improving the system's accuracy. The experimental results show that in a dataset containing 5\u201320 testers, the Wi-GPD system achieves an identification accuracy of up to 84.75%\u201399.5%, thoroughly verifying its effectiveness and reliability.<\/jats:p>","DOI":"10.1145\/3746639","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T07:22:53Z","timestamp":1751527373000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Wi-GPD Identification System Based on Gait Point Density"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1371-6385","authenticated-orcid":false,"given":"Ying","family":"Liu","sequence":"first","affiliation":[{"name":"Faculty of Electronic Information and Engineering, Liaoning Technical University - Huludao Campus","place":["Huludao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6949-6555","authenticated-orcid":false,"given":"Zhiyang","family":"Cao","sequence":"additional","affiliation":[{"name":"Faculty of Electronic Information and Engineering, Liaoning Technical University - Huludao Campus","place":["Huludao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1829-5267","authenticated-orcid":false,"given":"Jiaqi","family":"Cai","sequence":"additional","affiliation":[{"name":"Liaoning Technical University -Huludao Campus","place":["Huludao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1610-9143","authenticated-orcid":false,"given":"Yuqing","family":"He","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Liaoning Technical University - Huludao Campus","place":["Huludao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0254-472X","authenticated-orcid":false,"given":"Mingzhe","family":"Hu","sequence":"additional","affiliation":[{"name":"Liaoning Technical University, Liaoning Technical University","place":["Huludao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"769","article-title":"WiFi-based robust human and non-human motion recognition with deep learning","year":"2024","unstructured":"G. Zhu, B. Wang, W. Gao, Y. Hu, C. Wu, and K. R. Liu. 2024. WiFi-based robust human and non-human motion recognition with deep learning. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops). IEEE, 769\u2013774.","journal-title":"Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops)"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10020228"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2023.3261325"},{"issue":"1","key":"e_1_3_1_5_2","first-page":"157","article-title":"MDPose: Human skeletal motion reconstruction using WiFi micro-doppler signatures","volume":"60","year":"2023","unstructured":"C. Tang, W. Li, S. Vishwakarma, F. Shi, S. Julier, and K. Chetty. 2023. MDPose: Human skeletal motion reconstruction using WiFi micro-doppler signatures. IEEE Transactions on Aerospace and Electronic Systems 60, 1 (2023), 157\u2013167.","journal-title":"IEEE Transactions on Aerospace and Electronic Systems"},{"issue":"5","key":"e_1_3_1_6_2","first-page":"2938","article-title":"PhaseAnti: An anti-interference WiFi-based activity recognition system using interference-independent phase component","volume":"22","year":"2021","unstructured":"Jinyang Huang, Bin Liu, Chenglin Miao, Yan Lu, Qijia Zheng, Yu Wu, Jiancun Liu, Lu Su, and Chang Wen Chen. 2021. PhaseAnti: An anti-interference WiFi-based activity recognition system using interference-independent phase component. IEEE Transactions on Mobile Computing 22, 5 (2021), 2938\u20132954.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","unstructured":"N. Damodaran E. Haruni M. Kokhkharova et al. 2020. Device-free human activity and fall recognition using WiFi channel state information (CSI). CCF Transactions on Pervasive Computing and Interaction 2 1 (2020) 1--17.","DOI":"10.1007\/s42486-020-00027-1"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971670"},{"key":"e_1_3_1_9_2","first-page":"75","article-title":"WiFi-ID: Human identification using WiFi signal","year":"2016","unstructured":"J. Zhang, B. Wei, W. Hu, and S. S. Kanhere. 2016. WiFi-ID: Human identification using WiFi signal. In Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 75\u201382.","journal-title":"Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS)"},{"issue":"9","key":"e_1_3_1_10_2","first-page":"7610","article-title":"Gate-ID: WiFi-based human identification irrespective of walking directions in smart home","volume":"8","year":"2020","unstructured":"J. Zhang, B. Wei, F. Wu, L. Dong, W. Hu, S. S. Kanhere, and J. Cheng. 2020. Gate-ID: WiFi-based human identification irrespective of walking directions in smart home. IEEE Internet of Things Journal 8, 9 (2020), 7610\u20137624.","journal-title":"IEEE Internet of Things Journal"},{"issue":"2","key":"e_1_3_1_11_2","first-page":"1178","article-title":"WiDIGR: Direction-independent gait recognition system using commercial Wi-Fi devices","volume":"7","year":"2019","unstructured":"L. Zhang, C. Wang, M. Ma, and D. Zhang. 2019. WiDIGR: Direction-independent gait recognition system using commercial Wi-Fi devices. IEEE Internet of Things Journal 7, 2 (2019), 1178\u20131191.","journal-title":"IEEE Internet of Things Journal"},{"issue":"8","key":"e_1_3_1_12_2","first-page":"6960","article-title":"WiCrew: Gait-based crew identification for cruise ships using commodity WiFi","volume":"10","year":"2022","unstructured":"K. Liu, D. Pei, S. Zhang, X. Zeng, K. Zheng, C. Li, and M. Chen. 2022. WiCrew: Gait-based crew identification for cruise ships using commodity WiFi. IEEE Internet of Things Journal 10, 8 (2022), 6960\u20136972.","journal-title":"IEEE Internet of Things Journal"},{"issue":"1","key":"e_1_3_1_13_2","first-page":"1","article-title":"GaitSense: Towards ubiquitous gait-based human identification with Wi-Fi","volume":"18","year":"2021","unstructured":"Y. Zhang, Y. Zheng, G. Zhang, K. Qian, C. Qian, and Z. Yang. 2021. GaitSense: Towards ubiquitous gait-based human identification with Wi-Fi. ACM Transactions on Sensor Networks (TOSN) 18, 1 (2021), 1\u201324.","journal-title":"ACM Transactions on Sensor Networks (TOSN)"},{"issue":"2","key":"e_1_3_1_14_2","first-page":"465","article-title":"Attention-based gait recognition and walking direction estimation in Wi-Fi networks","volume":"21","year":"2020","unstructured":"Y. Xu, W. Yang, M. Chen, S. Chen, and L. Huang. 2020. Attention-based gait recognition and walking direction estimation in Wi-Fi networks. IEEE Transactions on Mobile Computing 21, 2 (2020), 465\u2013479.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Z. Yang K. Qian C. Wu et al. 2021. Human gait recognition with Wi-Fi. In Smart Wireless Sensing: From IoT to AIoT. 215--230.","DOI":"10.1007\/978-981-16-5658-3_10"},{"key":"e_1_3_1_16_2","first-page":"891","volume-title":"Proceedings of the 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","author":"Yang W.","year":"2022","unstructured":"W. Yang, Z. Xu, and Q. Zheng. 2022. An identity perception algorithm based on WiFi channel state information. In Proceedings of the 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 891\u2013894."},{"issue":"1","key":"e_1_3_1_17_2","first-page":"5","article-title":"Gait recognition as a service for unobtrusive user identification in smart spaces","volume":"1","year":"2020","unstructured":"C. Luo, J. Wu, J. Li, J. Wang, W. Xu, Z. Ming, and A. Y. Zomaya. 2020. Gait recognition as a service for unobtrusive user identification in smart spaces. ACM Transactions on Internet of Things 1, 1 (2020), 5.","journal-title":"ACM Transactions on Internet of Things"},{"key":"e_1_3_1_18_2","first-page":"75","volume-title":"Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS)","year":"2016","unstructured":"J. Zhang, B. Wei, W. Hu, and S. S. Kanhere. 2016. WiFi-ID: Human identification using WiFi signal. In Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 75\u201382."},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/0167-6393(95)00009-D"},{"key":"e_1_3_1_20_2","first-page":"II1089","article-title":"Speaker recognition using G. 729 speech codec parameters","volume":"2","year":"2000","unstructured":"T. F. Quatieri, R. B. Dunn, D. A. Reynolds, J. P. Campbell, and E. Singer. 2000. Speaker recognition using G. 729 speech codec parameters. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 00CH37100). IEEE, Vol. 2, II1089\u2013II1092.","journal-title":"Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 00CH37100)"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3472810"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-021-02050-w"},{"issue":"10","key":"e_1_3_1_23_2","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.1109\/TUFFC.2022.3198503","article-title":"Ultrasonic guided wave inversion based on deep learning restoration for fingerprint recognition","volume":"69","year":"2022","unstructured":"C. Zhao, J. Li, M. Lin, X. Chen, and Y. Liu. 2022. Ultrasonic guided wave inversion based on deep learning restoration for fingerprint recognition. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69, 10 (2022), 2965\u20132974.","journal-title":"IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control"},{"key":"e_1_3_1_24_2","first-page":"10","article-title":"A minutia-based partial fingerprint recognition system","volume":"38","author":"Jea T. Y.","year":"2005","unstructured":"T. Y. Jea and V. Govindaraju. 2005. A minutia-based partial fingerprint recognition system. Pattern Recognition 38, 10 (2005), 1672\u20131684.","journal-title":"Pattern Recognition"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2007.903540"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02925-y"},{"key":"e_1_3_1_27_2","first-page":"1","volume-title":"Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG)","year":"2022","unstructured":"D. Muramatsu, K. Moriwaki, Y. Maruya, N. Takemura, and Y. Yagi. 2022. Incorporation of extra pseudo labels for CNN-based gait recognition. In Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG). IEEE, 1\u20135."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17030478"},{"issue":"11","key":"e_1_3_1_29_2","first-page":"8978","article-title":"Gait quality aware network: Toward the interpretability of silhouette-based gait recognition","volume":"34","year":"2022","unstructured":"S. Hou, X. Liu, C. Cao, and Y. Huang. 2022. Gait quality aware network: Toward the interpretability of silhouette-based gait recognition. IEEE Transactions on Neural Networks and Learning Systems 34, 11 (2022), 8978\u20138988.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"2","key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"468","DOI":"10.3390\/s18020468","article-title":"Flexible piezoelectric sensor-based gait recognition","volume":"18","author":"Cha Y.","year":"2018","unstructured":"Y. Cha, H. Kim, and D. Kim. 2018. Flexible piezoelectric sensor-based gait recognition. Sensors 18, 2 (2018), 468.","journal-title":"Sensors"},{"key":"e_1_3_1_31_2","doi-asserted-by":"crossref","unstructured":"L. Tran and D. Choi. 2020. Data augmentation for inertial sensor-based gait deep neural network. IEEE Access 8 (2020) 12364--12378.","DOI":"10.1109\/ACCESS.2020.2966142"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","unstructured":"P. Delgado-Santos R. Tolosana R. Guest et al. 2022. GaitPrivacyON: Privacy-preserving mobile gait biometrics using unsupervised learning. Pattern Recognition Letters 161 (2022) 30--37.","DOI":"10.1016\/j.patrec.2022.07.015"},{"issue":"9","key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"6448","DOI":"10.1109\/TCSVT.2022.3161515","article-title":"Unsupervised domain adaptation for disguised-gait-based person identification on micro-doppler signatures","volume":"32","year":"2022","unstructured":"Y. Yang, X. Yang, T. Sakamoto, F. Fioranelli, B. Li, and Y. Lang. 2022. Unsupervised domain adaptation for disguised-gait-based person identification on micro-doppler signatures. IEEE Transactions on Circuits and Systems for Video Technology 32, 9 (2022), 6448\u20136460.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_3_1_34_2","unstructured":"F. Wang J. Han S. Zhang et al. 2018. CSI-Net: Unified human body characterization and pose recognition. arXiv preprint arXiv:1810.03064. Retrieved from https:\/\/arxiv.org\/abs\/1810.03064"},{"key":"e_1_3_1_35_2","first-page":"872","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","year":"2019","unstructured":"T. Li, L. Fan, M. Zhao, Y. Liu, and D. Katabi. 2019. Making the invisible visible: Action recognition through walls and occlusions. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 872\u2013881."},{"key":"e_1_3_1_36_2","first-page":"7356","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","year":"2018","unstructured":"M. Zhao, T. Li, M. Abu Alsheikh, Y. Tian, H. Zhao, A. Torralba, and D. Katabi. 2018. Through-wall human pose estimation using radio signals. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7356\u20137365."},{"key":"e_1_3_1_37_2","first-page":"10113","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","year":"2019","unstructured":"M. Zhao, Y. Liu, A. Raghu, T. Li, H. Zhao, A. Torralba, and D. Katabi. 2019. Through-wall human mesh recovery using radio signals. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 10113\u201310122."},{"issue":"6","key":"e_1_3_1_38_2","first-page":"2186","article-title":"GaitWay: Monitoring and recognizing gait speed through the walls","volume":"20","year":"2020","unstructured":"C. Wu, F. Zhang, Y. Hu, and K. R. Liu. 2020. GaitWay: Monitoring and recognizing gait speed through the walls. IEEE Transactions on Mobile Computing 20, 6 (2020), 2186\u20132199.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_3_1_39_2","first-page":"1","volume-title":"Proceedings of the IEEE Smart World Congress (SWC)","year":"2023","unstructured":"J. Yang, Y. Liu, Y. Wu, P. Yang, and Z. Liu. 2023. Gait-enhance: Robust gait recognition of complex walking patterns based on WiFi CSI. In Proceedings of the IEEE Smart World Congress (SWC). IEEE, 1\u20139."},{"issue":"6","key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"5321","DOI":"10.1109\/JIOT.2022.3222204","article-title":"WiWalk: Gait-based dual-user identification using WiFi device","volume":"10","author":"Ou R.","year":"2022","unstructured":"R. Ou, Y. Chen, and Y. Deng. 2022. WiWalk: Gait-based dual-user identification using WiFi device. IEEE Internet of Things Journal 10, 6 (2022), 5321\u20135334.","journal-title":"IEEE Internet of Things Journal"},{"issue":"23","key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"22725","DOI":"10.1109\/JSEN.2022.3214239","article-title":"ECG denoising method based on an improved VMD algorithm","volume":"22","year":"2022","unstructured":"C. Li, Y. Wu, H. Lin, J. Li, F. Zhang, and Y. Yang. 2022. ECG denoising method based on an improved VMD algorithm. IEEE Sensors Journal 22, 23 (2022), 22725\u201322733.","journal-title":"IEEE Sensors Journal"},{"key":"e_1_3_1_42_2","doi-asserted-by":"crossref","unstructured":"X. Wang A. Yu K. Niu et al. 2024. Understanding the diffraction model in static multipath-rich environments for WiFi sensing system design. IEEE Transactions on Mobile Computing 23 11 (2024) 10393--10410.","DOI":"10.1109\/TMC.2024.3377708"},{"issue":"3","key":"e_1_3_1_43_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3264958","article-title":"FullBreathe: Full human respiration detection exploiting complementarity of CSI phase and amplitude of WiFi signals","volume":"2","year":"2018","unstructured":"Y. Zeng, D. Wu, R. Gao, T. Gu, and D. Zhang. 2018. FullBreathe: Full human respiration detection exploiting complementarity of CSI phase and amplitude of WiFi signals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 1\u201319.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3581791.3596849"},{"issue":"9","key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"3414","DOI":"10.1109\/TMC.2021.3052314","article-title":"Wi-PIGR: Path independent gait recognition with commodity Wi-Fi","volume":"21","author":"Zhang L.","year":"2021","unstructured":"L. Zhang, C. Wang, and D. Zhang. 2021. Wi-PIGR: Path independent gait recognition with commodity Wi-Fi. IEEE Transactions on Mobile Computing 21, 9 (2021), 3414\u20133427.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_3_1_46_2","doi-asserted-by":"crossref","unstructured":"J. Jiang S. Jiang Y. Liu et al. 2023. Wi-Gait: Pushing the limits of robust passive personnel identification using Wi-Fi signals. Computer Networks 229 (2023) 109751.","DOI":"10.1016\/j.comnet.2023.109751"},{"issue":"1","key":"e_1_3_1_47_2","first-page":"1","article-title":"Mgait: Model-based gait analysis using wearable bend and inertial sensors","volume":"3","year":"2021","unstructured":"S. An, Y. Tuncel, T. Basaklar, G. K. Krishnakumar, G. Bhat, and U. Y. Ogras. 2021. Mgait: Model-based gait analysis using wearable bend and inertial sensors. ACM Transactions on Internet of Things 3, 1 (2021), 1\u201324.","journal-title":"ACM Transactions on Internet of Things"},{"issue":"10","key":"e_1_3_1_48_2","doi-asserted-by":"crossref","first-page":"e0183989","DOI":"10.1371\/journal.pone.0183989","article-title":"Wearable sensors objectively measure gait parameters in Parkinson's disease","volume":"12","year":"2017","unstructured":"Johannes C. M. Schlachetzki, Jens Barth, Franz Marxreiter, Julia Gossler, Zacharias Kohl, Samuel Reinfelder, Heiko Gassner, Kamiar Aminian, Bjoern M. Eskofier, et al. 2017. Wearable sensors objectively measure gait parameters in Parkinson's disease. PloS One 12, 10 (2017), e0183989.","journal-title":"PloS One"}],"container-title":["ACM Transactions on Embedded Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746639","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T10:10:53Z","timestamp":1753092653000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,21]]},"references-count":47,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7,31]]}},"alternative-id":["10.1145\/3746639"],"URL":"https:\/\/doi.org\/10.1145\/3746639","relation":{},"ISSN":["1539-9087","1558-3465"],"issn-type":[{"type":"print","value":"1539-9087"},{"type":"electronic","value":"1558-3465"}],"subject":[],"published":{"date-parts":[[2025,7,21]]},"assertion":[{"value":"2024-12-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}