{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T01:39:10Z","timestamp":1791509950029,"version":"4.3.4"},"reference-count":33,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2441203"],"award-info":[{"award-number":["U2441203"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["216Z1803G"],"award-info":[{"award-number":["216Z1803G"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Gait &amp; Posture"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1016\/j.gaitpost.2024.12.028","type":"journal-article","created":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T11:18:21Z","timestamp":1735384701000},"page":"191-203","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"special_numbering":"C","title":["Two-dimensional identification of lower limb gait features based on the variational modal decomposition of sEMG signal and convolutional neural network"],"prefix":"10.1016","volume":"117","author":[{"given":"Qiming","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxing","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingfu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijie","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihua","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.gaitpost.2024.12.028_bib1","first-page":"1","article-title":"Spatiotemporal evolution of global population ageing from 1960 to 2017","volume":"19","author":"Li","year":"2019","journal-title":"BMC Public Health"},{"issue":"1","key":"10.1016\/j.gaitpost.2024.12.028_bib2","first-page":"3","article-title":"China's population aging trend and countermeasures","volume":"19","author":"Pang","year":"2021","journal-title":"Chin. J. Geriatr. Care"},{"key":"10.1016\/j.gaitpost.2024.12.028_bib3","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1097\/01.blo.0000218757.97063.21","article-title":"Age-associated decrease of type IIA\/B human skeletal muscle fibers","volume":"450","author":"Lee","year":"2006","journal-title":"Clin. Orthop. Relat. Res. \u00ae"},{"issue":"1","key":"10.1016\/j.gaitpost.2024.12.028_bib4","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1001\/jama.2010.1923","article-title":"Gait speed and survival in older adults","volume":"305","author":"Studenski","year":"2011","journal-title":"Jama"},{"issue":"6","key":"10.1016\/j.gaitpost.2024.12.028_bib5","first-page":"761","article-title":"Advances in clinical rehabilitation applications of lower extremity exoskeleton robots","volume":"36","author":"Wang","year":"2021","journal-title":"Chin. J. Rehabil. Med."},{"issue":"22","key":"10.1016\/j.gaitpost.2024.12.028_bib6","first-page":"2852","article-title":"Activities of daily living and depressive symptoms in the chinese elderly","volume":"23","author":"Chen","year":"2020","journal-title":"Chin. Gen. Pract."},{"issue":"4","key":"10.1016\/j.gaitpost.2024.12.028_bib7","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s10514-021-09988-3","article-title":"A novel adaptive iterative learning control approach and human-in-the-loop control pattern for lower limb rehabilitation robot in disturbances environment","volume":"45","author":"Sun","year":"2021","journal-title":"Auton. Robots"},{"key":"10.1016\/j.gaitpost.2024.12.028_bib8","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s10514-016-9566-0","article-title":"An oscillator-based smooth real-time estimate of gait phase for wearable robotics","volume":"41","author":"Yan","year":"2017","journal-title":"Auton. Robots"},{"key":"10.1016\/j.gaitpost.2024.12.028_bib9","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/s10514-016-9551-7","article-title":"Assist on-ankle: a reconfigurable ankle exoskeleton with series-elastic actuation","volume":"41","author":"Erdogan","year":"2017","journal-title":"Auton. Robots"},{"key":"10.1016\/j.gaitpost.2024.12.028_bib10","unstructured":"Zheng X. Analysis of Multi-features and Application of Gait Recognition Based on Electromyographic Signals[D]."},{"key":"10.1016\/j.gaitpost.2024.12.028_bib11","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/PHT.2013.6461326","article-title":"Classification of gait phases from lower limb EMG: Application to exoskeleton orthosis","author":"Joshi","year":"2013","journal-title":"2013 IEEE Point--Care Healthc. Technol. (PHT)"},{"issue":"8","key":"10.1016\/j.gaitpost.2024.12.028_bib12","doi-asserted-by":"crossref","first-page":"894","DOI":"10.3390\/electronics8080894","article-title":"A deep learning approach to EMG-based classification of gait phases during level ground walking","volume":"8","author":"Morbidoni","year":"2019","journal-title":"Electronics"},{"issue":"2","key":"10.1016\/j.gaitpost.2024.12.028_bib13","doi-asserted-by":"crossref","first-page":"849","DOI":"10.3390\/s23020849","article-title":"Novel deep learning network for gait recognition using multimodal inertial sensors","volume":"23","author":"Shi","year":"2023","journal-title":"Sensors"},{"issue":"7","key":"10.1016\/j.gaitpost.2024.12.028_bib14","first-page":"3467","article-title":"GaitSet: cross-view gait recognition through utilizing gait as a deep set","volume":"44","author":"Chao","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.gaitpost.2024.12.028_bib15","doi-asserted-by":"crossref","unstructured":"Fan C., Peng Y., Cao C., et al. Gaitpart: temporal part-based model for gait recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2020: 14225-14233.","DOI":"10.1109\/CVPR42600.2020.01423"},{"key":"10.1016\/j.gaitpost.2024.12.028_bib16","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2019.107069","article-title":"A model-based gait recognition method with body pose and human prior knowledge","volume":"98","author":"Liao","year":"2020","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.gaitpost.2024.12.028_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103693","article-title":"Gait phase recognition of lower limb exoskeleton system based on the integrated network model","volume":"76","author":"Zhang","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"issue":"12","key":"10.1016\/j.gaitpost.2024.12.028_bib18","doi-asserted-by":"crossref","first-page":"253","DOI":"10.3390\/a12120253","article-title":"Walking gait phase detection based on acceleration signals using LSTM-DNN algorithm","volume":"12","author":"Zhen","year":"2019","journal-title":"Algorithms"},{"issue":"10","key":"10.1016\/j.gaitpost.2024.12.028_bib19","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.3390\/s16101579","article-title":"Gait phase recognition for lower-limb exoskeleton with only joint angular sensors","volume":"16","author":"Liu","year":"2016","journal-title":"Sensors"},{"issue":"3","key":"10.1016\/j.gaitpost.2024.12.028_bib20","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s11370-021-00367-6","article-title":"Research on human gait prediction and recognition algorithm of lower limb-assisted exoskeleton robot","volume":"14","author":"Qin","year":"2021","journal-title":"Intell. Serv. Robot."},{"issue":"2","key":"10.1016\/j.gaitpost.2024.12.028_bib21","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1109\/TMRB.2022.3166543","article-title":"Metric learning for robust gait phase recognition for a lower limb exoskeleton robot based on sEMG","volume":"4","author":"Liu","year":"2022","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"issue":"7","key":"10.1016\/j.gaitpost.2024.12.028_bib22","doi-asserted-by":"crossref","first-page":"7005","DOI":"10.1109\/JSEN.2022.3146446","article-title":"Lower limb motion intention recognition based on sEMG fusion features","volume":"22","author":"Zhang","year":"2022","journal-title":"IEEE Sens. J."},{"issue":"1","key":"10.1016\/j.gaitpost.2024.12.028_bib23","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TNSRE.2019.2950096","article-title":"A low-cost end-to-end sEMG-based gait sub-phase recognition system","volume":"28","author":"Luo","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.gaitpost.2024.12.028_bib24","doi-asserted-by":"crossref","unstructured":"Ziegler J., Gattringer H., Mueller A. Classification of gait phases based on bilateral EMG data using support vector machines. In: Proceedings of the 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob). 2018: 978-983.","DOI":"10.1109\/BIOROB.2018.8487750"},{"key":"10.1016\/j.gaitpost.2024.12.028_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102587","article-title":"A comparison of neural networks algorithms for EEG and sEMG features based gait phases recognition","volume":"68","author":"Wei","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"issue":"3","key":"10.1016\/j.gaitpost.2024.12.028_bib26","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational mode decomposition","volume":"62","author":"Dragomiretskiy","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"10.1016\/j.gaitpost.2024.12.028_bib27","unstructured":"Wang Z., Oates T. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In: Proceedings of the Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015."},{"issue":"5","key":"10.1016\/j.gaitpost.2024.12.028_bib28","first-page":"122","article-title":"The design and performance of optimal BP neural network classifier","volume":"37","author":"Jiang","year":"2001","journal-title":"Comput. Eng. Appl."},{"key":"10.1016\/j.gaitpost.2024.12.028_bib29","doi-asserted-by":"crossref","unstructured":"Li Y., Gao F., Zheng X., et al. Gait recognition using GA-SVM method based on electromyography signal. In: Proceedings of the Intelligent Robotics and Applications: 10th International Conference. 2017: 313-322.","DOI":"10.1007\/978-3-319-65289-4_30"},{"key":"10.1016\/j.gaitpost.2024.12.028_bib30","doi-asserted-by":"crossref","unstructured":"Shen C., Pei Z., Chen W., et al. Lower limb activity recognition using sEMG signals via weighted random forest. In: Proceedings of the IEEE 17th Conference on Industrial Electronics and Applications (ICIEA). 2022: 1151-1156.","DOI":"10.1109\/ICIEA54703.2022.10005913"},{"key":"10.1016\/j.gaitpost.2024.12.028_bib31","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2021.666458","article-title":"Evaluation of three machine learning algorithms for the automatic classification of EMG patterns in gait disorders","volume":"12","author":"Fricke","year":"2021","journal-title":"Front. Neurol."},{"issue":"9","key":"10.1016\/j.gaitpost.2024.12.028_bib32","first-page":"1","article-title":"Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands","volume":"10","author":"Atzori","year":"2016","journal-title":"Front. Neurorobot."},{"key":"10.1016\/j.gaitpost.2024.12.028_bib33","doi-asserted-by":"crossref","unstructured":"Song S., Yang L., Wu M., et al. Dynamic hand gesture recognition via electromyographic signal based on convolutional neural network. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2021: 876-881.","DOI":"10.1109\/SMC52423.2021.9658997"}],"container-title":["Gait &amp; Posture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0966636224007665?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0966636224007665?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T12:46:21Z","timestamp":1762346781000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0966636224007665"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":33,"alternative-id":["S0966636224007665"],"URL":"https:\/\/doi.org\/10.1016\/j.gaitpost.2024.12.028","relation":{},"ISSN":["0966-6362"],"issn-type":[{"value":"0966-6362","type":"print"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Two-dimensional identification of lower limb gait features based on the variational modal decomposition of sEMG signal and convolutional neural network","name":"articletitle","label":"Article Title"},{"value":"Gait & Posture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.gaitpost.2024.12.028","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}