{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T05:10:45Z","timestamp":1777957845559,"version":"3.51.4"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 52525504"],"award-info":[{"award-number":["Grant No. 52525504"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No.U25A20144"],"award-info":[{"award-number":["Grant No.U25A20144"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"DOI":"10.1007\/s11517-026-03585-1","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T04:11:52Z","timestamp":1777954312000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Musculoskeletal modeling based on deep-nsNMF for multi-DoF motion decoding"],"prefix":"10.1007","author":[{"given":"Jianmin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianchang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7217-6935","authenticated-orcid":false,"given":"Lizhi","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"key":"3585_CR1","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.bspc.2007.07.009","volume":"2","author":"M Asghari Oskoei","year":"2007","unstructured":"Asghari Oskoei M, Hu H (2007) Myoelectric control systems\u2014A survey. Biomed Signal Process Control 2:275","journal-title":"Biomed Signal Process Control"},{"key":"3585_CR2","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1016\/j.jelekin.2006.08.006","volume":"16","author":"P Parker","year":"2006","unstructured":"Parker P, Englehart K, Hudgins B (2006) Myoelectric signal processing for control of powered limb prostheses. J Electromyogr Kinesiol 16:541","journal-title":"J Electromyogr Kinesiol"},{"key":"3585_CR3","doi-asserted-by":"publisher","first-page":"389","DOI":"10.3109\/17453677308989075","volume":"44","author":"P Herberts","year":"1973","unstructured":"Herberts P, Almstr\u00f6m C, Kadefors R, Lawrence P (1973) Hand prosthesis control via myoelectric patterns. Acta Orthop Scand 44:389","journal-title":"Acta Orthop Scand"},{"issue":"11","key":"3585_CR4","doi-asserted-by":"publisher","first-page":"2444","DOI":"10.3390\/electronics12112444","volume":"12","author":"S Li","year":"2023","unstructured":"Li S, Zhang Y, Tang Y, Li W, Sun W, Yu H (2023) Real-time sEMG pattern recognition of multiple-mode movements for artificial limbs based on CNN-RNN algorithm. Electronics 12(11):2444","journal-title":"Electronics"},{"key":"3585_CR5","doi-asserted-by":"crossref","unstructured":"Higashi S, Goto D, Okada S, Shiozawa N, Makikawa M (2019) Development of wearable EMG measurement system on forearm for wrist gestures discrimination. In: 2019 IEEE 1st global conference on life sciences and technologies (LifeTech), Osaka, Japan, pp 250\u2013251","DOI":"10.1109\/LifeTech.2019.8884009"},{"issue":"3","key":"3585_CR6","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1109\/JBHI.2013.2256920","volume":"17","author":"Z Li","year":"2013","unstructured":"Li Z, Wang B, Yang C, Xie Q, Su CY (2013) Boosting-based EMG patterns classification scheme for robustness enhancement. IEEE J Biomed Health Inform 17(3):545\u2013552","journal-title":"IEEE J Biomed Health Inform"},{"key":"3585_CR7","doi-asserted-by":"publisher","first-page":"104142","DOI":"10.1109\/ACCESS.2023.3317956","volume":"11","author":"RV Godoy","year":"2023","unstructured":"Godoy RV, Guan B, Sanches F, Dwivedi A, Liarokapis M (2023) Electromyography based gesture decoding employing few-shot learning, transfer learning, and training from scratch. IEEE Access 11:104142\u2013104154","journal-title":"IEEE Access"},{"key":"3585_CR8","doi-asserted-by":"crossref","unstructured":"Fukano K, Iiazawa K, Soeda T, Shirai A, Capi G (2021) Deep learning for gesture recognition based on surface EMG data. In: 2021 International conference on advanced mechatronic systems (ICAMechS), Tokyo, Japan, pp 41\u201345","DOI":"10.1109\/ICAMechS54019.2021.9661533"},{"key":"3585_CR9","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1109\/TBME.2008.919734","volume":"55","author":"MA Oskoei","year":"2008","unstructured":"Oskoei MA, Hu H (2008) Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans Biomed Eng 55:1956\u20131965","journal-title":"IEEE Trans Biomed Eng"},{"key":"3585_CR10","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.jelekin.2018.10.004","volume":"43","author":"S Roldan-Vasco","year":"2018","unstructured":"Roldan-Vasco S, Restrepo-Agudelo S, Valencia-Martinez Y, Orozco-Duque A (2018) Automatic detection of oral and pharyngeal phases in swallowing using classification algorithms and multichannel EMG. J Electromyogr Kinesiol 43:193\u2013200","journal-title":"J Electromyogr Kinesiol"},{"key":"3585_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102781","volume":"68","author":"X Wang","year":"2021","unstructured":"Wang X, Dong D, Chi X, Wang S, Miao Y, An M, Gavrilov AI (2021) sEMG-based consecutive estimation of human lower limb movement by using multi-branch neural network. Biomed Signal Process Control 68:102781","journal-title":"Biomed Signal Process Control"},{"key":"3585_CR12","doi-asserted-by":"crossref","unstructured":"Aung YM, Al-Jumaily A (2012) sEMG Based ANN for Shoulder Angle Prediction. Proc Eng (International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012)) 41:1009\u20131015","DOI":"10.1016\/j.proeng.2012.07.276"},{"key":"3585_CR13","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.bspc.2017.10.002","volume":"40","author":"J Chen","year":"2018","unstructured":"Chen J, Zhang X, Cheng Y, Xi N (2018) Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks. Biomed Signal Process Control 40:335\u2013342","journal-title":"Biomed Signal Process Control"},{"key":"3585_CR14","doi-asserted-by":"crossref","unstructured":"Park K-H, Lee S-W (2016) Movement intention decoding based on deep learning for multiuser myoelectric interfaces. In: Proc 4th int winter conf brain\u2013comput interface (BCI), pp 1\u20132","DOI":"10.1109\/IWW-BCI.2016.7457459"},{"issue":"3","key":"3585_CR15","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab0e2e","volume":"16","author":"A Ameri","year":"2019","unstructured":"Ameri A, Akhaee MA, Scheme E, Englehart K (2019) Regression convolutional neural network for improved simultaneous EMG control. J Neural Eng 16(3):036015","journal-title":"J Neural Eng"},{"key":"3585_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105447","volume":"87","author":"NAS Putro","year":"2024","unstructured":"Putro NAS, Avian C, Prakosa SW, Mahali MI, Leu J-S (2024) Estimating finger joint angles by surface EMG signal using feature extraction and transformer-based deep learning model. Biomed Signal Process Control 87:105447","journal-title":"Biomed Signal Process Control"},{"key":"3585_CR17","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1109\/TNSRE.2022.3226860","volume":"31","author":"J Zhang","year":"2023","unstructured":"Zhang J, Zhao Y, Shone F, Li Z, Frangi AF, Xie SQ, Zhang Z-Q (2023) Physics-informed deep learning for musculoskeletal modeling: predicting muscle forces and joint kinematics from surface EMG. IEEE Trans Neural Syst Rehabil Eng 31:484\u2013493","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"2","key":"3585_CR18","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1109\/TMECH.2022.3211671","volume":"28","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Qian K, Bo S, Zhang Z, Li Z, Li G-Q, Dehghani-Sanij AA, Xie SQ (2023) Adaptive cooperative control strategy for a wrist exoskeleton using model-based joint impedance estimation. IEEE\/ASME Trans Mechatron 28(2):748\u2013757","journal-title":"IEEE\/ASME Trans Mechatron"},{"issue":"16","key":"3585_CR19","doi-asserted-by":"publisher","first-page":"3901","DOI":"10.1016\/j.jbiomech.2016.10.035","volume":"49","author":"DL Crouch","year":"2016","unstructured":"Crouch DL, Huang H (2016) Lumped-parameter electromyogram-driven musculoskeletal hand model: A potential platform for real-time prosthesis control. J Biomech 49(16):3901\u20133907","journal-title":"J Biomech"},{"key":"3585_CR20","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aae26b","volume":"15","author":"M Sartori","year":"2018","unstructured":"Sartori M, Durandau G, Dosen S, Farina D (2018) Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling. J Neural Eng 15:066026","journal-title":"J Neural Eng"},{"key":"3585_CR21","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1109\/TNSRE.2020.3038051","volume":"28","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Zhang Z, Li Z, Yang Z, Xie S (2020) An EMG-driven musculoskeletal model for estimating continuous wrist motion. IEEE Trans Neural Syst Rehabil Eng 28:3113\u20133120","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"3585_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104480","volume":"81","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Li Z, Zhang Z, Qian K, Xie S (2023) An EMG-driven musculoskeletal model for estimation of wrist kinematics using mirrored bilateral movement. Biomed Signal Process Control 81:104480","journal-title":"Biomed Signal Process Control"},{"key":"3585_CR23","doi-asserted-by":"crossref","unstructured":"Han L, Cheng L, Li H, Zou Y, Qin S, Zhou M (2024) Hierarchical optimization for personalized hand and wrist musculoskeletal modeling and motion estimation. IEEE Trans Biomed Eng","DOI":"10.1109\/TBME.2024.3456235"},{"key":"3585_CR24","doi-asserted-by":"publisher","first-page":"77","DOI":"10.3389\/fbioe.2016.00077","volume":"4","author":"AJ Meyer","year":"2016","unstructured":"Meyer AJ, Eskinazi I, Jackson JN, Rao AV, Patten C, Fregly BJ (2016) Muscle synergies facilitate computational prediction of subject-specific walking motions. Front Bioeng Biotechnol 4:77","journal-title":"Front Bioeng Biotechnol"},{"issue":"8","key":"3585_CR25","doi-asserted-by":"publisher","first-page":"3772","DOI":"10.3390\/app12083772","volume":"12","author":"Z He","year":"2022","unstructured":"He Z, Qin Z, Koike Y (2022) Continuous estimation of finger and wrist joint angles using a muscle synergy based musculoskeletal model. Appl Sci 12(8):3772","journal-title":"Appl Sci"},{"issue":"1","key":"3585_CR26","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac4851","volume":"19","author":"J Zhao","year":"2022","unstructured":"Zhao J, Yu Y, Wang X, Ma S, Sheng X, Zhu X (2022) A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements. J Neural Eng 19(1):016027","journal-title":"J Neural Eng"},{"issue":"1","key":"3585_CR27","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1109\/TMRB.2024.3503920","volume":"7","author":"L Pan","year":"2025","unstructured":"Pan L, Li Q, Li J (2025) Musculoskeletal modeling based on muscle synergy for prediction of hand and wrist movements. IEEE Trans Med Robot Bionics 7(1):337\u2013346","journal-title":"IEEE Trans Med Robot Bionics"},{"key":"3585_CR28","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1007\/s10439-005-3320-7","volume":"33","author":"KRS Holzbaur","year":"2005","unstructured":"Holzbaur KRS, Murray WM, Delp SL (2005) A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control. Ann Biomed Eng 33:829\u2013840","journal-title":"Ann Biomed Eng"},{"issue":"11","key":"3585_CR29","doi-asserted-by":"publisher","first-page":"1940","DOI":"10.1109\/TBME.2007.901024","volume":"54","author":"SL Delp","year":"2007","unstructured":"Delp SL, Anderson FC, Arnold AS, Loan P, Habib A, John CT, Guendelman E, Thelen DG (2007) OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng 54(11):1940\u20131950","journal-title":"IEEE Trans Biomed Eng"},{"key":"3585_CR30","first-page":"1457","volume":"5","author":"PO Hoyer","year":"2004","unstructured":"Hoyer PO (2004) Non-negative matrix factorization with sparseness constraints. J Mach Learn Res 5:1457\u20131469","journal-title":"J Mach Learn Res"},{"issue":"3","key":"3585_CR31","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1109\/TPAMI.2006.60","volume":"28","author":"A Pascual-Montano","year":"2006","unstructured":"Pascual-Montano A, Carazo JM, Kochi K, Lehner M, Pascual-Marqui RD (2006) Nonsmooth nonnegative matrix factorization (nsNMF). IEEE Trans Pattern Anal Mach Intell 28(3):403\u2013415","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3585_CR32","doi-asserted-by":"publisher","first-page":"58096","DOI":"10.1109\/ACCESS.2018.2873385","volume":"6","author":"J Yu","year":"2018","unstructured":"Yu J, Zhou G, Cichocki A, Yuan Q (2018) Learning the hierarchical parts of objects by deep non-smooth nonnegative matrix factorization. IEEE Access 6:58096\u201358105","journal-title":"IEEE Access"},{"key":"3585_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2020.3045841","volume":"70","author":"T Bao","year":"2020","unstructured":"Bao T, Zaidi SAR, Xie S, Yang P, Zhang Z-Q (2020) A CNN-LSTM hybrid model for wrist kinematics estimation using surface electromyography. IEEE Trans Instrum Meas 70:1\u20139","journal-title":"IEEE Trans Instrum Meas"},{"key":"3585_CR34","doi-asserted-by":"publisher","first-page":"1763","DOI":"10.1109\/TNSRE.2025.3565305","volume":"33","author":"M Li","year":"2025","unstructured":"Li M, Wei Z, Zhang Z-Q, Ma S, Xie SQ (2025) Continuous joint kinematics prediction using GAT-LSTM framework based on muscle synergy and sparse sEMG. IEEE Trans Neural Syst Rehabil Eng 33:1763\u20131773","journal-title":"IEEE Trans Neural Syst Rehabil Eng"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-026-03585-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-026-03585-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-026-03585-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T04:11:55Z","timestamp":1777954315000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-026-03585-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,5]]},"references-count":34,"alternative-id":["3585"],"URL":"https:\/\/doi.org\/10.1007\/s11517-026-03585-1","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,5]]},"assertion":[{"value":"16 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare there are no conflicts of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}]}}