{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T09:37:32Z","timestamp":1776850652181,"version":"3.51.2"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T00:00:00Z","timestamp":1678665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T00:00:00Z","timestamp":1678665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"SERB, DST, Govt. of India","award":["ECR\/2018\/000203"],"award-info":[{"award-number":["ECR\/2018\/000203"]}]},{"name":"HEFA CSR Grant, Ministry of Education, Govt. of India","award":["SAN\/CSR\/08\/2021-22"],"award-info":[{"award-number":["SAN\/CSR\/08\/2021-22"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s11042-023-14733-2","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T09:02:46Z","timestamp":1678698166000},"page":"33401-33419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Gait reference trajectory generation at different walking speeds using LSTM and CNN"],"prefix":"10.1007","volume":"82","author":[{"given":"Vijay Bhaskar","family":"Semwal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4969-6956","authenticated-orcid":false,"given":"Rahul","family":"Jain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pushkar","family":"Maheshwari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saksham","family":"Khatwani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,13]]},"reference":[{"key":"14733_CR1","doi-asserted-by":"crossref","unstructured":"Aertbeli\u00ebn E, De Schutter J (2014) Learning a predictive model of human gait for the control of a lower-limb exoskeleton. In: 5th IEEE RAS\/EMBS international conference on biomedical robotics and biomechatronics. IEEE, pp 520\u2013525","DOI":"10.1109\/BIOROB.2014.6913830"},{"key":"14733_CR2","unstructured":"Banbury CR, Reddi VJ, Lam M, Fu W, Fazel A, Holleman J, Huang X, Hurtado R, Kanter D, Lokhmotov A et al (2020) Benchmarking tinyml systems: challenges and direction. arXiv:2003.04821"},{"issue":"01","key":"14733_CR3","first-page":"8126","volume":"33","author":"H Chao","year":"2019","unstructured":"Chao H, He Y, Zhang J, Feng J (2019) Gaitset: regarding gait as a set for cross-view gait recognition. Proc AAAI Conf Artif Intell 33(01):8126\u20138133","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"14733_CR4","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555"},{"key":"14733_CR5","doi-asserted-by":"crossref","unstructured":"Fang B, Zhou Q, Sun F, Shan J, Wang M, Xiang C, Zhang Q (2020) Gait neural network for human-exoskeleton interaction. Frontiers in Neurorobotics, pp 58","DOI":"10.3389\/fnbot.2020.00058"},{"issue":"1","key":"14733_CR6","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.gaitpost.2007.11.001","volume":"28","author":"A Findlow","year":"2008","unstructured":"Findlow A, Goulermas J, Nester C, Howard D, Kenney L (2008) Predicting lower limb joint kinematics using wearable motion sensors. Gait & Posture 28(1):120\u2013126","journal-title":"Gait & Posture"},{"key":"14733_CR7","doi-asserted-by":"publisher","first-page":"e4640","DOI":"10.7717\/peerj.4640","volume":"6","author":"CA Fukuchi","year":"2018","unstructured":"Fukuchi CA, Fukuchi RK, Duarte M (2018) A public dataset of overground and treadmill walking kinematics and kinetics in healthy individuals. PeerJ 6:e4640","journal-title":"PeerJ"},{"key":"14733_CR8","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.gaitpost.2019.07.500","volume":"73","author":"CA Fukuchi","year":"2019","unstructured":"Fukuchi CA, Fukuchi RK, Duarte M (2019) Test of two prediction methods for minimum and maximum values of gait kinematics and kinetics data over a range of speeds. Gait & Posture 73:269\u2013272","journal-title":"Gait & Posture"},{"issue":"10","key":"14733_CR9","doi-asserted-by":"publisher","first-page":"2939","DOI":"10.3390\/s20102939","volume":"20","author":"M Gholami","year":"2020","unstructured":"Gholami M, Napier C, Menon C (2020) Estimating lower extremity running gait kinematics with a single accelerometer: a deep learning approach. Sensors 20(10):2939","journal-title":"Sensors"},{"issue":"02","key":"14733_CR10","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1142\/S0218488598000094","volume":"6","author":"S Hochreiter","year":"1998","unstructured":"Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Internat J Uncertain Fuzziness Knowledge-Based Systems 6(02):107\u2013116","journal-title":"Internat J Uncertain Fuzziness Knowledge-Based Systems"},{"issue":"8","key":"14733_CR11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"issue":"1","key":"14733_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12984-016-0214-x","volume":"14","author":"LJ Holanda","year":"2017","unstructured":"Holanda LJ, Silva PM, Amorim TC, Lacerda MO, Sim\u00e3o CR, Morya E (2017) Robotic assisted gait as a tool for rehabilitation of individuals with spinal cord injury: a systematic review. J Neuroeng Rehabili 14(1):1\u20137","journal-title":"J Neuroeng Rehabili"},{"issue":"1","key":"14733_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-38748-8","volume":"9","author":"F Horst","year":"2019","unstructured":"Horst F, Lapuschkin S, Samek W, M\u00fcller K-R, Sch\u00f6llhorn WI (2019) Explaining the unique nature of individual gait patterns with deep learning. Sci Rep 9(1):1\u201313","journal-title":"Sci Rep"},{"issue":"8","key":"14733_CR14","doi-asserted-by":"publisher","first-page":"2567","DOI":"10.1017\/S026357472100179X","volume":"40","author":"R Jain","year":"2022","unstructured":"Jain R (2022) Stride segmentation of inertial sensor data using statistical methods for different walking activities. Robotica 40(8):2567\u20132580","journal-title":"Robotica"},{"issue":"6","key":"14733_CR15","doi-asserted-by":"publisher","first-page":"e12743","DOI":"10.1111\/exsy.12743","volume":"39","author":"R Jain","year":"2022","unstructured":"Jain R, Semwal VB, Kaushik P (2022) Deep ensemble learning approach for lower extremity activities recognition using wearable sensors. Expert Syst 39(6):e12743. [Online]. Available: https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1111\/exsy.12743","journal-title":"Expert Syst"},{"issue":"7553","key":"14733_CR16","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y., Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"14733_CR17","doi-asserted-by":"crossref","unstructured":"Liang F-Y, Zhong C-H, Zhao X, Castro DL, Chen B, Gao F, Liao W-H (2018) Online adaptive and lstm-based trajectory generation of lower limb exoskeletons for stroke rehabilitation. In: 2018 IEEE international conference on robotics and biomimetics (ROBIO), pp 27\u201332","DOI":"10.1109\/ROBIO.2018.8664778"},{"issue":"1","key":"14733_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12984-015-0109-2","volume":"13","author":"DR Louie","year":"2016","unstructured":"Louie DR, Eng JJ (2016) Powered robotic exoskeletons in post-stroke rehabilitation of gait: a scoping review. J Neuroeng Rehabili 13(1):1\u201310","journal-title":"J Neuroeng Rehabili"},{"issue":"1","key":"14733_CR19","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.gaitpost.2013.08.028","volume":"39","author":"TP Luu","year":"2014","unstructured":"Luu TP, Low K, Qu X, Lim H, Hoon K (2014) An individual-specific gait pattern prediction model based on generalized regression neural networks. Gait & Posture 39(1):443\u2013448","journal-title":"Gait & Posture"},{"key":"14733_CR20","doi-asserted-by":"crossref","unstructured":"McGrath RL, Pires-Fernandes M, Knarr B, Higginson JS, Sergi F (2017) Toward goal-oriented robotic gait training: The effect of gait speed and stride length on lower extremity joint torques. In: 2017 international conference on rehabilitation robotics (ICORR), IEEE, pp 270\u2013275","DOI":"10.1109\/ICORR.2017.8009258"},{"issue":"1","key":"14733_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-45397-4","volume":"9","author":"F Moissenet","year":"2019","unstructured":"Moissenet F, Leboeuf F, Armand S (2019) Lower limb sagittal gait kinematics can be predicted based on walking speed, gender, age and bmi. Sci Rep 9 (1):1\u201312","journal-title":"Sci Rep"},{"key":"14733_CR22","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.2147\/NDT.S114102","volume":"13","author":"G Morone","year":"2017","unstructured":"Morone G, Paolucci S, Cherubini A, De Angelis D, Venturiero V, Coiro P, Iosa M (2017) Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics. Neuropsychiatr Dis Treat 13:1303","journal-title":"Neuropsychiatr Dis Treat"},{"issue":"2","key":"14733_CR23","doi-asserted-by":"publisher","first-page":"3362","DOI":"10.3390\/s140203362","volume":"14","author":"A Muro-De-La-Herran","year":"2014","unstructured":"Muro-De-La-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A (2014) Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 14(2):3362\u20133394","journal-title":"Sensors"},{"key":"14733_CR24","doi-asserted-by":"crossref","unstructured":"Ren S, Wang W, Hou Z. -G., Chen B, Liang X, Wang J, Peng L (2019) Personalized gait trajectory generation based on anthropometric features using random forest. Journal of Ambient Intelligence and Humanized Computing, pp 1\u201312","DOI":"10.1007\/s12652-019-01390-3"},{"issue":"24","key":"14733_CR25","doi-asserted-by":"publisher","first-page":"7127","DOI":"10.3390\/s20247127","volume":"20","author":"B Su","year":"2020","unstructured":"Su B, Gutierrez-Farewik EM (2020) Gait trajectory and gait phase prediction based on an lstm network. Sensors 20(24):7127","journal-title":"Sensors"},{"issue":"1","key":"14733_CR26","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/TNSRE.2008.2008278","volume":"17","author":"H Vallery","year":"2008","unstructured":"Vallery H, Van Asseldonk EH, Buss M, Van Der Kooij H (2008) Reference trajectory generation for rehabilitation robots: complementary limb motion estimation. IEEE Trans Neural Syst Rehabil Eng 17(1):23\u201330","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"4","key":"14733_CR27","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1109\/TASE.2018.2841358","volume":"15","author":"X Wu","year":"2018","unstructured":"Wu X, Liu D. -X., Liu M, Chen C, Guo H (2018) Individualized gait pattern generation for sharing lower limb exoskeleton robot. IEEE Trans Autom Sci Eng 15(4):1459\u20131470","journal-title":"IEEE Trans Autom Sci Eng"},{"issue":"1","key":"14733_CR28","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.jbiomech.2013.09.032","volume":"47","author":"Y Yun","year":"2014","unstructured":"Yun Y, Kim H-C, Shin SY, Lee J, Deshpande AD, Kim C (2014) Statistical method for prediction of gait kinematics with gaussian process regression. J Biomechan 47(1):186\u2013192","journal-title":"J Biomechan"},{"key":"14733_CR29","doi-asserted-by":"publisher","first-page":"362","DOI":"10.3389\/fbioe.2020.00362","volume":"8","author":"A Zaroug","year":"2020","unstructured":"Zaroug A, Lai DT, Mudie K, Begg R (2020) Lower limb kinematics trajectory prediction using long short-term memory neural networks. Front Bioeng Biotechnol 8:362","journal-title":"Front Bioeng Biotechnol"},{"key":"14733_CR30","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1109\/TNSRE.2020.3045425","volume":"29","author":"Z Zhou","year":"2020","unstructured":"Zhou Z, Liang B, Huang G, Liu B, Nong J, Xie L (2020) Individualized gait generation for rehabilitation robots based on recurrent neural networks. IEEE Trans Neural Syst Rehabil Eng 29:273\u2013281","journal-title":"IEEE Trans Neural Syst Rehabil Eng"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14733-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-14733-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14733-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T09:04:42Z","timestamp":1693472682000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-14733-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,13]]},"references-count":30,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["14733"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-14733-2","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,13]]},"assertion":[{"value":"8 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"All the ethical issues have been taken care of while writing the manuscript, and we have complied with all the standards to the best of our knowledge.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author(s) proclaim no conflict of interest regarding this research paper with any person or organization. This manuscript is based on original research findings done by the authors themselves.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}