{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T02:54:47Z","timestamp":1780109687176,"version":"3.54.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T00:00:00Z","timestamp":1583107200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T00:00:00Z","timestamp":1583107200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s00521-020-04742-9","type":"journal-article","created":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T17:12:56Z","timestamp":1583169176000},"page":"16073-16089","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Deep neural learning techniques with long short-term memory for gesture recognition"],"prefix":"10.1007","volume":"32","author":[{"given":"Deepak Kumar","family":"Jain","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aniket","family":"Mahanti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pourya","family":"Shamsolmoali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramachandran","family":"Manikandan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,3,2]]},"reference":[{"issue":"04","key":"4742_CR1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.4236\/jsea.2016.94009","volume":"9","author":"Y Chong","year":"2016","unstructured":"Chong Y, Huang J, Pan S (2016) Hand gesture recognition using appearance features based on 3d point cloud. J Softw Eng Appl 9(04):103","journal-title":"J Softw Eng Appl"},{"issue":"13","key":"4742_CR2","doi-asserted-by":"publisher","first-page":"5429","DOI":"10.1109\/JSEN.2018.2834968","volume":"18","author":"K Czuszy\u0144ski","year":"2018","unstructured":"Czuszy\u0144ski K, Rumi\u0144ski J, Kwa\u015bniewska A (2018) Gesture recognition with the linear optical sensor and recurrent neural networks. IEEE Sens J 18(13):5429\u20135438","journal-title":"IEEE Sens J"},{"issue":"6","key":"4742_CR3","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.3390\/s17061287","volume":"17","author":"J Davila","year":"2017","unstructured":"Davila J, Cretu AM, Zaremba M (2017) Wearable sensor data classification for human activity recognition based on an iterative learning framework. Sensors 17(6):1287","journal-title":"Sensors"},{"key":"4742_CR4","doi-asserted-by":"publisher","first-page":"573","DOI":"10.3389\/fneur.2017.00573","volume":"8","author":"H Ding","year":"2017","unstructured":"Ding H, He Q, Zhou Y, Dan G, Cui S (2017) An individual finger gesture recognition system based on motion-intent analysis using mechanomyogram signal. Front Neurol 8:573","journal-title":"Front Neurol"},{"key":"4742_CR5","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.patrec.2013.10.010","volume":"50","author":"F Dominio","year":"2014","unstructured":"Dominio F, Donadeo M, Zanuttigh P (2014) Combining multiple depth-based descriptors for hand gesture recognition. Pattern Recognit Lett 50:101\u2013111","journal-title":"Pattern Recognit Lett"},{"key":"4742_CR6","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/205707","author":"S Fong","year":"2015","unstructured":"Fong S, Liang J, Fister I, Mohammed S (2015) Gesture recognition from data streams of human motion sensor using accelerated PSO swarm search feature selection algorithm. J Sens. https:\/\/doi.org\/10.1155\/2015\/205707","journal-title":"J Sens"},{"issue":"3","key":"4742_CR7","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1109\/TSMCB.2012.2217324","volume":"43","author":"D Frolova","year":"2013","unstructured":"Frolova D, Stern H, Berman S (2013) Most probable longest common subsequence for recognition of gesture character input. IEEE Trans Cybern 43(3):871\u2013880","journal-title":"IEEE Trans Cybern"},{"issue":"6","key":"4742_CR8","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.medengphy.2014.02.012","volume":"36","author":"L Gao","year":"2014","unstructured":"Gao L, Bourke A, Nelson J (2014) Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. Med Eng Phys 36(6):779\u2013785","journal-title":"Med Eng Phys"},{"key":"4742_CR9","doi-asserted-by":"crossref","unstructured":"Goyal M, Shahi B, Prema K, Reddy NS (2017) Performance analysis of human gesture recognition techniques. In: 2017 2nd IEEE international conference on recent trends in electronics, information and communication technology (RTEICT), IEEE, pp 111\u2013115","DOI":"10.1109\/RTEICT.2017.8256568"},{"key":"4742_CR10","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.protcy.2012.10.013","volume":"6","author":"A Gupta","year":"2012","unstructured":"Gupta A, Sehrawat VK, Khosla M (2012) Fpga based real time human hand gesture recognition system. Proc Technol 6:98\u2013107","journal-title":"Proc Technol"},{"issue":"3","key":"4742_CR11","doi-asserted-by":"publisher","first-page":"266","DOI":"10.7763\/IJMLC.2012.V2.128","volume":"2","author":"MM Hasan","year":"2012","unstructured":"Hasan MM, Mishra PK (2012) Robust gesture recognition using Gaussian distribution for features fitting. Int J Mach Learn Comput 2(3):266","journal-title":"Int J Mach Learn Comput"},{"key":"4742_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.07.103","author":"J He","year":"2019","unstructured":"He J, Zhang C, He X, Dong R (2019) Visual recognition of traffic police gestures with convolutional pose machine and handcrafted features. Neurocomputing. https:\/\/doi.org\/10.1016\/j.neucom.2019.07.103","journal-title":"Neurocomputing"},{"key":"4742_CR13","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.imavis.2016.06.001","volume":"58","author":"A Joshi","year":"2017","unstructured":"Joshi A, Monnier C, Betke M, Sclaroff S (2017) Comparing random forest approaches to segmenting and classifying gestures. Image Vis Comput 58:86\u201395","journal-title":"Image Vis Comput"},{"key":"4742_CR14","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.jvcir.2015.01.015","volume":"28","author":"N\u00c7 K\u0131l\u0131boz","year":"2015","unstructured":"K\u0131l\u0131boz N\u00c7, G\u00fcd\u00fckbay U (2015) A hand gesture recognition technique for human\u2013computer interaction. J Vis Commun Image Represent 28:97\u2013104","journal-title":"J Vis Commun Image Represent"},{"key":"4742_CR15","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.displa.2018.08.001","volume":"55","author":"JH Kim","year":"2018","unstructured":"Kim JH, Hong GS, Kim BG, Dogra DP (2018) deepgesture: deep learning-based gesture recognition scheme using motion sensors. Displays 55:38\u201345","journal-title":"Displays"},{"issue":"14","key":"4742_CR16","doi-asserted-by":"publisher","first-page":"6067","DOI":"10.1016\/j.eswa.2014.04.037","volume":"41","author":"Y Kwon","year":"2014","unstructured":"Kwon Y, Kang K, Bae C (2014) Unsupervised learning for human activity recognition using smartphone sensors. Expert Syst Appl 41(14):6067\u20136074","journal-title":"Expert Syst Appl"},{"key":"4742_CR17","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.patcog.2017.12.023","volume":"77","author":"C Li","year":"2018","unstructured":"Li C, Xie C, Zhang B, Chen C, Han J (2018a) Deep fisher discriminant learning for mobile hand gesture recognition. Pattern Recognit 77:276\u2013288","journal-title":"Pattern Recognit"},{"key":"4742_CR18","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.ins.2018.02.024","volume":"441","author":"Y Li","year":"2018","unstructured":"Li Y, Wang X, Liu W, Feng B (2018b) Deep attention network for joint hand gesture localization and recognition using static RGB-D images. Inf Sci 441:66\u201378","journal-title":"Inf Sci"},{"issue":"1","key":"4742_CR19","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.patcog.2013.05.028","volume":"47","author":"YT Li","year":"2014","unstructured":"Li YT, Wachs JP (2014) Hegm: a hierarchical elastic graph matching for hand gesture recognition. Pattern Recognit 47(1):80\u201388","journal-title":"Pattern Recognit"},{"issue":"6","key":"4742_CR20","doi-asserted-by":"publisher","first-page":"1898","DOI":"10.1109\/JSEN.2014.2306094","volume":"14","author":"K Liu","year":"2014","unstructured":"Liu K, Chen C, Jafari R, Kehtarnavaz N (2014) Fusion of inertial and depth sensor data for robust hand gesture recognition. IEEE Sens J 14(6):1898\u20131903","journal-title":"IEEE Sens J"},{"issue":"24","key":"4742_CR21","doi-asserted-by":"publisher","first-page":"10,085","DOI":"10.1109\/JSEN.2018.2873003","volume":"18","author":"YT Liu","year":"2018","unstructured":"Liu YT, Zhang YA, Zeng M (2018) Novel algorithm for hand gesture recognition utilizing a wrist-worn inertial sensor. IEEE Sens J 18(24):10,085\u201310,095","journal-title":"IEEE Sens J"},{"key":"4742_CR22","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.eswa.2016.02.021","volume":"56","author":"RCB Madeo","year":"2016","unstructured":"Madeo RCB, Peres SM, de Moraes Lima CA (2016) Gesture phase segmentation using support vector machines. Expert Syst Appl 56:100\u2013115","journal-title":"Expert Syst Appl"},{"issue":"2","key":"4742_CR23","doi-asserted-by":"publisher","first-page":"39","DOI":"10.5772\/50204","volume":"9","author":"N Nguyen-Duc-Thanh","year":"2012","unstructured":"Nguyen-Duc-Thanh N, Lee S, Kim D (2012) Two-stage hidden markov model in gesture recognition for human robot interaction. Int J Adv Robot Syst 9(2):39","journal-title":"Int J Adv Robot Syst"},{"key":"4742_CR24","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.patcog.2017.10.033","volume":"76","author":"JC Nunez","year":"2018","unstructured":"Nunez JC, Cabido R, Pantrigo JJ, Montemayor AS, Velez JF (2018) Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition. Pattern Recognit 76:80\u201394","journal-title":"Pattern Recognit"},{"issue":"1","key":"4742_CR25","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/s16010115","volume":"16","author":"F Ord\u00f3\u00f1ez","year":"2016","unstructured":"Ord\u00f3\u00f1ez F, Roggen D (2016) Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1):115","journal-title":"Sensors"},{"issue":"12","key":"4742_CR26","first-page":"1","volume":"4","author":"SU Rahman","year":"2014","unstructured":"Rahman SU, Afroze Z, Tareq M (2014) Hand gesture recognition techniques for human computer interaction using open cv. Int J Sci Res Publ 4(12):1\u20136","journal-title":"Int J Sci Res Publ"},{"key":"4742_CR27","doi-asserted-by":"crossref","unstructured":"Razzaq M, Cleland I, Nugent C, Lee S (2018) Multimodal sensor data fusion for activity recognition using filtered classifier. In: Multidisciplinary digital publishing institute proceedings, vol\u00a02, p 1262","DOI":"10.3390\/proceedings2191262"},{"issue":"1","key":"4742_CR28","doi-asserted-by":"publisher","first-page":"1550147716683,6","DOI":"10.1177\/1550147716683687","volume":"13","author":"CA Ronao","year":"2017","unstructured":"Ronao CA, Cho SB (2017) Recognizing human activities from smartphone sensors using hierarchical continuous hidden markov models. Int J Distrib Sens Netw 13(1):1550147716683,687","journal-title":"Int J Distrib Sens Netw"},{"issue":"2","key":"4742_CR29","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s10055-016-0301-0","volume":"21","author":"KM Sagayam","year":"2017","unstructured":"Sagayam KM, Hemanth DJ (2017) Hand posture and gesture recognition techniques for virtual reality applications: a survey. Virtual Real 21(2):91\u2013107","journal-title":"Virtual Real"},{"issue":"11","key":"4742_CR30","doi-asserted-by":"publisher","first-page":"28,646","DOI":"10.3390\/s151128646","volume":"15","author":"D Santos","year":"2015","unstructured":"Santos D, Fernandes B, Bezerra B (2015) HAGR-D: a novel approach for gesture recognition with depth maps. Sensors 15(11):28,646\u201328,664","journal-title":"Sensors"},{"issue":"1","key":"4742_CR31","first-page":"59","volume":"25","author":"AD Soares","year":"2017","unstructured":"Soares AD, Apolin\u00e1rio AL Jr (2017) Real-time 3d gesture recognition using dynamic time warping and simplification methods. J WSCG 25(1):59\u201366","journal-title":"J WSCG"},{"key":"4742_CR32","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/8580959","author":"J Sun","year":"2018","unstructured":"Sun J, Fu Y, Li S, He J, Xu C, Tan L (2018) Sequential human activity recognition based on deep convolutional network and extreme learning machine using wearable sensors. J Sens. https:\/\/doi.org\/10.1155\/2018\/8580959","journal-title":"J Sens"},{"issue":"3","key":"4742_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2018.2864963","volume":"2","author":"TM Tai","year":"2018","unstructured":"Tai TM, Jhang YJ, Liao ZW, Teng KC, Hwang WJ (2018) Sensor-based continuous hand gesture recognition by long short-term memory. IEEE Sens Lett 2(3):1\u20134","journal-title":"IEEE Sens Lett"},{"issue":"3","key":"4742_CR34","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1109\/TASE.2013.2256349","volume":"10","author":"D Trabelsi","year":"2013","unstructured":"Trabelsi D, Mohammed S, Chamroukhi F, Oukhellou L, Amirat Y (2013) An unsupervised approach for automatic activity recognition based on hidden markov model regression. IEEE Trans Autom Sci Eng 10(3):829\u2013835","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"4742_CR35","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/6296013","author":"Z Wang","year":"2018","unstructured":"Wang Z, Chen B, Wu J (2018) Effective inertial hand gesture recognition using particle filtering based trajectory matching. J Electr Comput Eng. https:\/\/doi.org\/10.1155\/2018\/6296013","journal-title":"J Electr Comput Eng"},{"key":"4742_CR36","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/4160652","author":"C Xu","year":"2018","unstructured":"Xu C, He J, Zhang X, Cai H, Duan S, Tseng PH, Li C (2018) Recurrent transformation of prior knowledge based model for human motion recognition. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2018\/4160652","journal-title":"Comput Intell Neurosci"},{"issue":"1","key":"4742_CR37","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s13673-017-0097-2","volume":"7","author":"J Zhu","year":"2017","unstructured":"Zhu J, San-Segundo R, Pardo JM (2017) Feature extraction for robust physical activity recognition. Hum Centric Comput Inf Sci 7(1):16","journal-title":"Hum Centric Comput Inf Sci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04742-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-020-04742-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04742-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T01:06:45Z","timestamp":1614647205000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-020-04742-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,2]]},"references-count":37,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["4742"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-04742-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,2]]},"assertion":[{"value":"24 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}