{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T09:35:05Z","timestamp":1768556105297,"version":"3.49.0"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T00:00:00Z","timestamp":1640736000000},"content-version":"vor","delay-in-days":362,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002171"],"award-info":[{"award-number":["62002171"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20200464"],"award-info":[{"award-number":["BK20200464"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>As a machine\u2010learning\u2010driven decision\u2010making problem, the surface electromyography (sEMG)\u2010based hand movement recognition is one of the key issues in robust control of noninvasive neural interfaces such as myoelectric prosthesis and rehabilitation robot. Despite the recent success in sEMG\u2010based hand movement recognition using end\u2010to\u2010end deep feature learning technologies based on deep learning models, the performance of today\u2019s sEMG\u2010based hand movement recognition system is still limited by the noisy, random, and nonstationary nature of sEMG signals and researchers have come up with a number of methods that improve sEMG\u2010based hand movement via feature engineering. Aiming at achieving higher sEMG\u2010based hand movement recognition accuracies while enabling a trade\u2010off between performance and computational complexity, this study proposed a progressive fusion network (PFNet) framework, which improves sEMG\u2010based hand movement recognition via integration of domain knowledge\u2010guided feature engineering and deep feature learning. In particular, it learns high\u2010level feature representations from raw sEMG signals and engineered time\u2010frequency domain features via a feature learning network and a domain knowledge network, respectively, and then employs a 3\u2010stage progressive fusion strategy to progressively fuse the two networks together and obtain the final decisions. Extensive experiments were conducted on five sEMG datasets to evaluate our proposed PFNet, and the experimental results showed that the proposed PFNet could achieve the average hand movement recognition accuracies of 87.8%, 85.4%, 68.3%, 71.7%, and 90.3% on the five datasets, respectively, which outperformed those achieved by the state of the arts.<\/jats:p>","DOI":"10.1155\/2021\/4454648","type":"journal-article","created":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T02:20:39Z","timestamp":1640830839000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Towards Integration of Domain Knowledge\u2010Guided Feature Engineering and Deep Feature Learning in Surface Electromyography\u2010Based Hand Movement Recognition"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9466-9922","authenticated-orcid":false,"given":"Wentao","family":"Wei","sequence":"first","affiliation":[]},{"given":"Xuhui","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Hua","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8431-7037","authenticated-orcid":false,"given":"Yan","family":"Song","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,12,29]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2990881"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2020.01.007"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19122811"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2020.01.015"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnsre.2017.2687520"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnsre.2020.3024947"},{"key":"e_1_2_8_7_2","doi-asserted-by":"crossref","unstructured":"AmmaC. KringsT. B\u00f6erJ. andSchultzT. Advancing muscle-computer interfaces with high-density electromyography Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems July 2015 Seoul Korea 929\u2013938 https:\/\/doi.org\/10.1145\/2702123.2702501 2-s2.0-84951109185.","DOI":"10.1145\/2702123.2702501"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc2030021"},{"key":"e_1_2_8_9_2","article-title":"Hand gesture recognition based on surface electromyography using convolutional neural network with transfer learning method","volume":"1","author":"Chen X.","year":"2020","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0206049"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2016.00009"},{"key":"e_1_2_8_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep36571"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2017.12.005"},{"key":"e_1_2_8_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2019.2899222"},{"key":"e_1_2_8_15_2","doi-asserted-by":"crossref","unstructured":"MillarC. SiddiqueN. andKerrE. Lstm classification of functional grasps using semg data from low-cost wearable sensor Proceedings of the International Conference on Control Automation and Robotics (ICCAR) 2021 Singapore 213\u2013222 https:\/\/doi.org\/10.1109\/iccar52225.2021.9463477.","DOI":"10.1109\/ICCAR52225.2021.9463477"},{"key":"e_1_2_8_16_2","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6051"},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnsre.2019.2896269"},{"key":"e_1_2_8_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2941977"},{"key":"e_1_2_8_19_2","doi-asserted-by":"crossref","unstructured":"AtzoriM. GijsbertsA. andHeynenS. Building the Ninapro database: a resource for the biorobotics community Proceedings of the IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics July 2012 Rome Italy 1258\u20131265 https:\/\/doi.org\/10.1109\/biorob.2012.6290287 2-s2.0-84867415696.","DOI":"10.1109\/BioRob.2012.6290287"},{"key":"e_1_2_8_20_2","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2014.53"},{"key":"e_1_2_8_21_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0186132"},{"key":"e_1_2_8_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103723"},{"key":"e_1_2_8_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/10.204774"},{"key":"e_1_2_8_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2003.813539"},{"key":"e_1_2_8_25_2","doi-asserted-by":"crossref","unstructured":"ZhouX. ZhouC. andStewartB. G. Comparisons of discrete wavelet transform wavelet packet transform and stationary wavelet transform in denoising PD measurement data Peroceedings of the Conference Record of the IEEE International Symposium on Electrical Insulation June 2006 Toronto Canada 237\u2013240.","DOI":"10.1109\/ELINSL.2006.1665301"},{"key":"e_1_2_8_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2009.02.004"},{"key":"e_1_2_8_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3438872.3439060"},{"key":"e_1_2_8_28_2","doi-asserted-by":"crossref","unstructured":"JiangW.andYinZ. Human activity recognition using wearable sensors by deep convolutional neural networks Proceedings of the ACM International Conference on Multimedia 2015 New York NY USA 1307\u20131310 https:\/\/doi.org\/10.1145\/2733373.2806333 2-s2.0-84962910692.","DOI":"10.1145\/2733373.2806333"},{"key":"e_1_2_8_29_2","doi-asserted-by":"crossref","unstructured":"PoriaS. CambriaE. andGelbukhA. Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis Proceedings of the Conference on Empirical Methods in Natural Language Processing March 2015 Lisbon Portugal 2539\u20132544 https:\/\/doi.org\/10.18653\/v1\/d15-1303.","DOI":"10.18653\/v1\/D15-1303"},{"key":"e_1_2_8_30_2","unstructured":"IoffeS.andSzegedyC. Batch normalization: accelerating deep network training by reducing internal covariate shift Proceedings of the International Conference on Machine Learning 2015 Lille France 448\u2013456."},{"key":"e_1_2_8_31_2","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky A.","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_8_32_2","first-page":"1929","article-title":"A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava N.","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_8_33_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17030458"},{"key":"e_1_2_8_34_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep residual learning for image recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2016 Las Vegas NV USA 770\u2013778 https:\/\/doi.org\/10.1109\/cvpr.2016.90 2-s2.0-84986274465.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_8_35_2","article-title":"A tensor-based approach using multilinear SVD for hand gesture recognition from semg signals","volume":"1","author":"Padhy S.","year":"2020","journal-title":"IEEE Sensors Journal"},{"key":"e_1_2_8_36_2","article-title":"MXNet: a flexible and efficient machine learning library for heterogeneous distributed systems","volume":"42","author":"Chen T.","year":"2015","journal-title":"Neural Information Processing Systems, Workshop on Machine Learning Systems"},{"key":"e_1_2_8_37_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2017.00379"},{"key":"e_1_2_8_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2019.2943309"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/4454648.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/4454648.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/4454648","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T10:24:46Z","timestamp":1726395886000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/4454648"}},"subtitle":[],"editor":[{"given":"Heng","family":"Liu","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/4454648"],"URL":"https:\/\/doi.org\/10.1155\/2021\/4454648","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-09-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"4454648"}}