乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T13:41:37Z","timestamp":1693057297716},"reference-count":32,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:00:00Z","timestamp":1642636800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2022,5,15]]},"abstract":"Abstract<\/jats:title>Low\u2010cost traditional prosthetic legs, available worldwide, can make walking and stair climbing possible but still difficult. This article presents the hardware implementation of the surface electromyography (sEMG) powered prosthesis actuation (PPA) system using a learned neural network algorithm based on recurrent neural network (RNN), which is used to train sEMG benchmark databases, and predict joint angle. This implementation was created based on sEMG signal measurements. The data were collected from three benchmark datasets describing different subjects during performance, and analyzing various gait patterns were used to construct the neural network and reduce significant model errors in a real\u2010time setting. Processing circuits, interfacing the output with the controller board, signal amplification, motor driving circuits, and single\u2010board computer programming are included in the implementation.<\/jats:p>","DOI":"10.1002\/cpe.6848","type":"journal-article","created":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T07:25:47Z","timestamp":1642749947000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Implementation of surface electromyography controlled prosthetics limb based on recurrent neural network"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-1756-6804","authenticated-orcid":false,"given":"Tarek M.","family":"Bittibssi","sequence":"first","affiliation":[{"name":"Department of Electronics and Electrical Communication Engineering Ain Shams University Cairo Egypt"}]},{"given":"Abdelhalim","family":"Zekry","sequence":"additional","affiliation":[{"name":"Department of Electronics and Electrical Communication Engineering Ain Shams University Cairo Egypt"}]},{"given":"Mohamed A.","family":"Genedy","sequence":"additional","affiliation":[{"name":"Military Medical Academy Cairo Egypt"}]},{"given":"Shady A.","family":"Maged","sequence":"additional","affiliation":[{"name":"Department of Mechatronic Engineering Ain Shams University Cairo Egypt"}]}],"member":"311","published-online":{"date-parts":[[2022,1,20]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2005.856295"},{"key":"e_1_2_7_3_1","doi-asserted-by":"crossref","unstructured":"PingZ LoweryMM DewaldJPA KuikenTA.Towards improved myoelectric prosthesis control: high density surface EMG recording after targeted muscle reinnervation. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology. Vol. 7; 2005:4064\u20104067. doi:10.1109\/iembs.2005.1615355","DOI":"10.1109\/IEMBS.2005.1615355"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2004.836492"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/03091900210142459"},{"key":"e_1_2_7_6_1","doi-asserted-by":"crossref","unstructured":"HerleS ManS LazeaG MarcuC RaicaP RobotinR.Hierarchical myoelectric control of a human upper limb prosthesis. Proceedings of the 19th International Workshop on Robotics in Alpe\u2010Adria\u2010Danube Region RAAD 2010. 2010:55\u201060. doi:10.1109\/RAAD.2010.5524609","DOI":"10.1109\/RAAD.2010.5524609"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1123\/jab.13.2.135"},{"key":"e_1_2_7_8_1","volume-title":"Biomedical Signal Analysis","author":"Rangayyan RM","year":"2020"},{"key":"e_1_2_7_9_1","doi-asserted-by":"crossref","unstructured":"TebanT AlbuA.Recurrent neural network models for myoelectric\u2010based control of a prosthetic hand. Proceedings of the 22nd International Conference on System Theory Control and Computing (ICSTCC); 2018:603\u2010608.","DOI":"10.1109\/ICSTCC.2018.8540720"},{"key":"e_1_2_7_10_1","doi-asserted-by":"crossref","unstructured":"JafarzadehM TadesseY.Deep learning approach to control of prosthetic hands with electromyography signals. Proceedings of the 2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR); January 2020. doi:10.1109\/ISMCR47492.2019.8955725","DOI":"10.1109\/ISMCR47492.2019.8955725"},{"key":"e_1_2_7_11_1","doi-asserted-by":"crossref","unstructured":"JabbariM KhushabaRN MemberS NazarpourK MemberS.EMG\u2010based hand gesture classification with long short\u2010term memory deep recurrent neural networks. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); 2020:3302\u20103305.","DOI":"10.1109\/EMBC44109.2020.9175279"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abc3d3"},{"key":"e_1_2_7_13_1","unstructured":"GehlharR MarSY.Recurrent neural network control of a hybrid dynamical transfemoral prosthesis with EdgeDRNN accelerator. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA); 2019."},{"key":"e_1_2_7_14_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/8853314","article-title":"sEMG\u2010based neural network prediction model selection of gesture fatigue and dataset optimization","volume":"2020","author":"Ma F","year":"2020","journal-title":"Comput Intell Neurosci"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2021.3098689"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21041264"},{"key":"e_1_2_7_17_1","doi-asserted-by":"crossref","unstructured":"KetykoI KovacsF VargaKZ. Domain adaptation for sEMG\u2010based gesture recognition with recurrent neural networks. Proceedings of the International Joint Conference on Neural Networks. July 2019. doi:10.1109\/IJCNN.2019.8852018","DOI":"10.1109\/IJCNN.2019.8852018"},{"key":"e_1_2_7_18_1","doi-asserted-by":"crossref","unstructured":"AlamR RhivuSR&HaqueMAImproved gesture recognition using deep neural networks on sEMG. Proceedings of the 2018 International Conference on Engineering Applied Sciences and Technology (ICEAST); 2018:1\u20104. doi:10.1109\/iceast.2018.8434493","DOI":"10.1109\/ICEAST.2018.8434493"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep36571"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0206049"},{"key":"e_1_2_7_21_1","doi-asserted-by":"crossref","unstructured":"David Orjuela\u2010Ca\u00f1\u00f3nA Ru\u00edz\u2010OlayaAF&ForeroLDeep neural network for EMG signal classification of wrist position: Preliminary results. Proceedings of the 2017 IEEE Latin American Conference on Computational Intelligence LA\u2010CCI 2017. November 2017:1\u20105. doi:10.1109\/LA\u2010CCI.2017.8285706","DOI":"10.1109\/LA-CCI.2017.8285706"},{"key":"e_1_2_7_22_1","doi-asserted-by":"crossref","unstructured":"HuangD ChenB. Surface EMG decoding for hand gestures based on spectrogram and CNN\u2010LSTM. Proceedings of the 2nd China Symposium on Cognitive Computing and Hybrid Intelligence CCHI 2019. Vol 1; 2019:123\u2010126. doi:10.1109\/CCHI.2019.8901936","DOI":"10.1109\/CCHI.2019.8901936"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.07.021"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102024"},{"key":"e_1_2_7_25_1","doi-asserted-by":"crossref","unstructured":"MillarC SiddiqueN KerrE.LSTM classification of sEMG signals for individual finger movements using low cost wearable sensor. Proceedings of the 2020 International Symposium on Community\u2010centric Systems CcS 2020. 2020. doi:10.1109\/CcS49175.2020.9231515","DOI":"10.1109\/CcS49175.2020.9231515"},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.BSPC.2021.103048"},{"key":"e_1_2_7_27_1","doi-asserted-by":"crossref","unstructured":"FougnerA SchemeE ChanADC EnglehartK StavdahlO.A multi\u2010modal approach for hand motion classification using surface EMG and accelerometers. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS. 2011(Grant 192546):4247\u20104250. doi:10.1109\/IEMBS.2011.6091054","DOI":"10.1109\/IEMBS.2011.6091054"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2003.813539"},{"key":"e_1_2_7_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/su10061865"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2018.00014"},{"key":"e_1_2_7_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbiomech.2021.110320"},{"key":"e_1_2_7_32_1","unstructured":"UCI Machine Learning Repository: EMG dataset in Lower Limb Data Set. Accessed November 19 2020.http:\/\/archive.ics.uci.edu\/ml\/datasets\/emg+dataset+in+lower+limb#"},{"key":"e_1_2_7_33_1","unstructured":"gait analysis | Quintic Sports. Accessed October 13 2021.https:\/\/www.quinticsports.com\/gait\u2010analysis\/"}],"container-title":["Concurrency and Computation: Practice and Experience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.6848","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/cpe.6848","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.6848","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T22:10:41Z","timestamp":1693001441000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cpe.6848"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,20]]},"references-count":32,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,5,15]]}},"alternative-id":["10.1002\/cpe.6848"],"URL":"https:\/\/doi.org\/10.1002\/cpe.6848","archive":["Portico"],"relation":{},"ISSN":["1532-0626","1532-0634"],"issn-type":[{"value":"1532-0626","type":"print"},{"value":"1532-0634","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,20]]},"assertion":[{"value":"2021-06-04","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-26","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-01-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}