{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:53:22Z","timestamp":1780502002133,"version":"3.54.1"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy, South Korea","doi-asserted-by":"publisher","award":["10063300"],"award-info":[{"award-number":["10063300"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy, South Korea","doi-asserted-by":"publisher","award":["20003762"],"award-info":[{"award-number":["20003762"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002582","name":"Gwangju Institute of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002582","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3013029","type":"journal-article","created":{"date-parts":[[2020,7,30]],"date-time":"2020-07-30T22:54:21Z","timestamp":1596149661000},"page":"139881-139891","source":"Crossref","is-referenced-by-count":55,"title":["Pathological Gait Classification Using Kinect v2 and Gated Recurrent Neural Networks"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8757-2014","authenticated-orcid":false,"given":"Kooksung","family":"Jun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5615-4258","authenticated-orcid":false,"given":"Yongwoo","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8363-3054","authenticated-orcid":false,"given":"Sanghyub","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8787-5608","authenticated-orcid":false,"given":"Deok-Won","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6050-6594","authenticated-orcid":false,"given":"Mun Sang","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2785279"},{"key":"ref33","article-title":"Delving deeper into convolutional networks for learning video representations","author":"ballas","year":"2015","journal-title":"arXiv 1511 06432"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.01.005"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/MIPRO.2014.6859769"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/MMSP.2013.6659329"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"ref35","first-page":"1110","article-title":"Hierarchical recurrent neural network for skeleton based action recognition","author":"du","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/BIOSMART.2019.8734198"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00152"},{"key":"ref29","article-title":"Gait recognition based on Kinect sensor","volume":"9139","author":"ahmed","year":"2014","journal-title":"Proc SPIE"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.gaitpost.2011.03.027"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(06)70678-0"},{"key":"ref20","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"2014","journal-title":"arXiv 1412 3555"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1186\/s40648-019-0150-1"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2980128"},{"key":"ref24","article-title":"Gated recurrent unit (GRU) for emotion classification from noisy speech","author":"rana","year":"2016","journal-title":"arXiv 1612 07778"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2017.8296249"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2939947"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.30630\/joiv.3.4.289"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp.2007.131045"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CCE.2018.8465714"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-019-00790-7"},{"key":"ref40","first-page":"843","article-title":"Unsupervised learning of video representations using lstms","author":"srivastava","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.5244\/C.28.79"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2015.05.011"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1186\/s12938-015-0092-7","article-title":"Motion tracking and gait feature estimation for recognising Parkinson’s disease using MS kinect","volume":"14","author":"?upa","year":"2015","journal-title":"Biomed Eng Online"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2019.8857607"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2967845"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.11591\/ijece.v10i2.pp1495-1506"},{"key":"ref18","first-page":"410","article-title":"Using a skeleton gait energy image for pathological gait classification","author":"loureiro","year":"2020","journal-title":"Proc 15th IEEE Int Conf Auto Face Gest Recognit (FG)(FG)"},{"key":"ref19","first-page":"1","article-title":"Pathological gait detection based on multiple regression models using unobtrusive sensing technology","author":"chakraborty","year":"2020","journal-title":"J Signal Process Syst"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.gaitpost.2018.11.029"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/diseases7010018"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2018.12.007"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2015.7284881"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.5220\/0005722901310137"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-016-1330-0"},{"key":"ref49","first-page":"26","article-title":"Lecture 6.5-RMSProp: Divide the gradient by a running average of its recent magnitude","volume":"4","author":"tieleman","year":"2012","journal-title":"COURSERA Neural Networks for Mach Learn"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/s16111792"},{"key":"ref46","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488598000094"},{"key":"ref48","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"duchi","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref47","article-title":"ADADELTA: An adaptive learning rate method","author":"zeiler","year":"2012","journal-title":"arXiv 1212 5701"},{"key":"ref42","first-page":"3697","article-title":"Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks","author":"zhu","year":"2016","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299101"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"ref43","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn (ICML)"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09152991.pdf?arnumber=9152991","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:56:42Z","timestamp":1642003002000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9152991\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3013029","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}