{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:12:02Z","timestamp":1772122322725,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100018968","name":"Universitas Jember","doi-asserted-by":"publisher","award":["7575\/UN25.3.1\/LT\/2023"],"award-info":[{"award-number":["7575\/UN25.3.1\/LT\/2023"]}],"id":[{"id":"10.13039\/501100018968","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014952","name":"Direktorat Jenderal Penguatan Riset dan Pengembangan","doi-asserted-by":"publisher","award":["108\/E\/KPT\/2023"],"award-info":[{"award-number":["108\/E\/KPT\/2023"]}],"id":[{"id":"10.13039\/501100014952","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s00521-025-11337-9","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T10:49:53Z","timestamp":1748947793000},"page":"16461-16485","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluation of LSTM for predicting grip strength using electromyography: a comparison of setups and methods"],"prefix":"10.1007","volume":"37","author":[{"given":"Khairul","family":"Anam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad","family":"Sudrajat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naufal Ainur","family":"Rizal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gramandha Wega","family":"Intyanto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wahyu","family":"Muldayani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamad Agung Prawira","family":"Negara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Sumardi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saiful","family":"Bukhori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Made Santo","family":"Gitakarma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudio","family":"Castellini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"issue":"6","key":"11337_CR1","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1109\/JSEN.2019.2956998","volume":"20","author":"G Zhang","year":"2019","unstructured":"Zhang G, Davoodnia V, Sepas-Moghaddam A, Zhang Y, Etemad A (2019) Classification of hand movements from eeg using a deep attention-based lstm network. IEEE Sens J 20(6):3113\u20133122","journal-title":"IEEE Sens J"},{"key":"11337_CR2","doi-asserted-by":"crossref","unstructured":"Merletti R, Campanini I, Rymer WZ, Disselhorst-Klug C (2021) Surface Electromyography: barriers limiting widespread use of sEMG in clinical assessment and neurorehabilitation. Frontiers Media SA","DOI":"10.3389\/978-2-88966-616-4"},{"key":"11337_CR3","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1109\/TNSRE.2022.3214866","volume":"31","author":"X Xu","year":"2023","unstructured":"Xu X, Deng H, Zhang Y, Chen J (2023) Continuous grasping force estimation with surface EMG based on Huxley-type musculoskeletal model. IEEE Trans Neural Syst Rehabil Eng 31:346\u2013355. https:\/\/doi.org\/10.1109\/TNSRE.2022.3214866","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"11337_CR4","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2022.1034672","author":"M Karrenbach","year":"2022","unstructured":"Karrenbach M, Preechayasomboon P, Sauer P, Boe D, Rombokas E (2022) Deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from emg. Frontiers in Bioengineering and Biotechnology. https:\/\/doi.org\/10.3389\/fbioe.2022.1034672","journal-title":"Frontiers in Bioengineering and Biotechnology"},{"key":"11337_CR5","doi-asserted-by":"publisher","DOI":"10.3390\/s20061642","author":"AR Asif","year":"2020","unstructured":"Asif AR, Waris A, Gilani SO, Jamil M, Ashraf H, Shafique M, Niazi IK (2020) Performance evaluation of convolutional neural network for hand gesture recognition using emg. Sensors. https:\/\/doi.org\/10.3390\/s20061642","journal-title":"Sensors"},{"issue":"8","key":"11337_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0220899","volume":"14","author":"AW Franzke","year":"2019","unstructured":"Franzke AW, Kristoffersen MB, Bongers RM, Murgia A, Pobatschnig B, Unglaube F, Sluis CK (2019) Users\u2019 and therapists\u2019 perceptions of myoelectric multi-function upper limb prostheses with conventional and pattern recognition control. PLoS ONE 14(8):1\u201313. https:\/\/doi.org\/10.1371\/journal.pone.0220899","journal-title":"PLoS ONE"},{"issue":"1","key":"11337_CR7","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1146\/annurev-control-071020-104336","volume":"4","author":"V Mendez","year":"2021","unstructured":"Mendez V, Iberite F, Shokur S, Micera S (2021) Current solutions and future trends for robotic prosthetic hands. Annual Rev Control Robot Auto Syst 4(1):595\u2013627. https:\/\/doi.org\/10.1146\/annurev-control-071020-104336","journal-title":"Annual Rev Control Robot Auto Syst"},{"key":"11337_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3189632","volume":"71","author":"G Hajian","year":"2022","unstructured":"Hajian G, Morin E, Etemad A (2022) Multimodal estimation of endpoint force during quasi-dynamic and dynamic muscle contractions using deep learning. IEEE Trans Instrum Meas 71:1\u201311. https:\/\/doi.org\/10.1109\/TIM.2022.3189632","journal-title":"IEEE Trans Instrum Meas"},{"issue":"15","key":"11337_CR9","doi-asserted-by":"publisher","first-page":"4213","DOI":"10.3390\/s20154213","volume":"20","author":"C Chen","year":"2020","unstructured":"Chen C, Huang K, Li D, Zhao Z, Hong J (2020) Multi-segmentation parallel CNN model for estimating assembly torque using surface electromyography signals. Sensors 20(15):4213","journal-title":"Sensors"},{"issue":"9","key":"11337_CR10","doi-asserted-by":"publisher","first-page":"10054","DOI":"10.1109\/JSEN.2023.3259150","volume":"23","author":"O Kerdjidj","year":"2023","unstructured":"Kerdjidj O, Amara K, Harizi F, Boumridja H (2023) Implementing hand gesture recognition using emg on the zynq circuit. IEEE Sens J 23(9):10054\u201310061. https:\/\/doi.org\/10.1109\/JSEN.2023.3259150","journal-title":"IEEE Sens J"},{"key":"11337_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106327","volume":"123","author":"JP V\u00e1sconez","year":"2023","unstructured":"V\u00e1sconez JP, Barona L\u00f3pez LI, Caraguay Leonardo Valdivieso, Benalc\u00e1zar ME (2023) A comparison of emg-based hand gesture recognition systems based on supervised and reinforcement learning. Eng Appl Artif Intell 123:106327. https:\/\/doi.org\/10.1016\/j.engappai.2023.106327","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"11337_CR12","doi-asserted-by":"publisher","first-page":"11000","DOI":"10.1038\/s41598-023-36490-w","volume":"13","author":"M Montazerin","year":"2023","unstructured":"Montazerin M, Rahimian E, Naderkhani F, Atashzar SF, Yanushkevich S, Mohammadi A (2023) Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density emg signals. Sci Rep 13(1):11000","journal-title":"Sci Rep"},{"key":"11337_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105141","volume":"86","author":"L Wang","year":"2023","unstructured":"Wang L, Fu J, Chen H, Zheng B (2023) Hand gesture recognition using smooth wavelet packet transformation and hybrid cnn based on surface emg and accelerometer signal. Biomed Signal Process Control 86:105141","journal-title":"Biomed Signal Process Control"},{"issue":"10","key":"11337_CR14","doi-asserted-by":"publisher","first-page":"4940","DOI":"10.3390\/s23104940","volume":"23","author":"R Zhang","year":"2023","unstructured":"Zhang R, Hong Y, Zhang H, Dang L, Li Y (2023) High-performance surface electromyography armband design for gesture recognition. Sensors 23(10):4940","journal-title":"Sensors"},{"key":"11337_CR15","doi-asserted-by":"crossref","unstructured":"Chen C, Yu Y, Sheng X, Meng J, Zhu X (2023) Real-time hand gesture recognition by decoding motor unit discharges across multiple motor tasks from surface electromyography. IEEE Transactions on Biomedical Engineering","DOI":"10.1109\/TBME.2023.3234642"},{"issue":"3","key":"11337_CR16","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.jphysparis.2009.08.008","volume":"103","author":"C Castellini","year":"2009","unstructured":"Castellini C, Gruppioni E, Davalli A, Sandini G (2009) Fine detection of grasp force and posture by amputees via surface electromyography. J Physiol Paris 103(3):255\u2013262. https:\/\/doi.org\/10.1016\/j.jphysparis.2009.08.008","journal-title":"J Physiol Paris"},{"key":"11337_CR17","doi-asserted-by":"crossref","unstructured":"Anam K, Al-Jumaily A (2017) Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees. Neural Networks 85:51\u201368. Publisher: Elsevier","DOI":"10.1016\/j.neunet.2016.09.004"},{"issue":"3","key":"11337_CR18","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1007\/s40747-021-00338-5","volume":"8","author":"S Hua","year":"2022","unstructured":"Hua S, Wang C, Wu X (2022) A novel sEMG-based force estimation method using deep-learning algorithm. Complex Intell Syst 8(3):1949\u20131961. https:\/\/doi.org\/10.1007\/s40747-021-00338-5","journal-title":"Complex Intell Syst"},{"issue":"5","key":"11337_CR19","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/j.medengphy.2010.04.004","volume":"32","author":"C Choi","year":"2010","unstructured":"Choi C, Kwon S, Park W, Lee H-D, Kim J (2010) Real-time pinch force estimation by surface electromyography using an artificial neural network. Med Eng Phys 32(5):429\u2013436. https:\/\/doi.org\/10.1016\/j.medengphy.2010.04.004","journal-title":"Med Eng Phys"},{"issue":"2","key":"11337_CR20","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1007\/s00500-015-1800-8","volume":"21","author":"H Cao","year":"2017","unstructured":"Cao H, Sun S, Zhang K (2017) Modified EMG-based handgrip force prediction using extreme learning machine. Soft Comput 21(2):491\u2013500. https:\/\/doi.org\/10.1007\/s00500-015-1800-8","journal-title":"Soft Comput"},{"issue":"10","key":"11337_CR21","doi-asserted-by":"publisher","first-page":"3226","DOI":"10.3390\/s18103226","volume":"18","author":"L Xu","year":"2018","unstructured":"Xu L, Chen X, Cao S, Zhang X, Chen X (2018) Feasibility study of advanced neural networks applied to sEMG-based force estimation. Sensors 18(10):3226. https:\/\/doi.org\/10.3390\/s18103226","journal-title":"Sensors"},{"key":"11337_CR22","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.bspc.2018.06.011","volume":"47","author":"N Wang","year":"2019","unstructured":"Wang N, Lao K, Zhang X, Lin J, Zhang X (2019) The recognition of grasping force using LDA. Biomed Signal Process Control 47:393\u2013400. https:\/\/doi.org\/10.1016\/j.bspc.2018.06.011","journal-title":"Biomed Signal Process Control"},{"key":"11337_CR23","doi-asserted-by":"crossref","unstructured":"Martinez IJR, Mannini A, Clemente F, Sabatini AM, Cipriani C (2020) Grasp force estimation from the transient EMG using high-density surface recordings. Journal of Neural Engineering 17(1):016052. Publisher: IOP Publishing","DOI":"10.1088\/1741-2552\/ab673f"},{"key":"11337_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103036","volume":"70","author":"B Dutra","year":"2021","unstructured":"Dutra B, Silveira A, Pereira A (2021) Grasping force estimation using state-space model and Kalman Filter. Biomed Signal Process Control 70:103036. https:\/\/doi.org\/10.1016\/j.bspc.2021.103036","journal-title":"Biomed Signal Process Control"},{"key":"11337_CR25","doi-asserted-by":"publisher","first-page":"1020086","DOI":"10.3389\/fnins.2022.1020086","volume":"16","author":"B Xu","year":"2022","unstructured":"Xu B, Zhang K, Yang X, Liu D, Hu C, Li H, Song A (2022) Natural grasping movement recognition and force estimation using electromyography. Front Neurosci 16:1020086. https:\/\/doi.org\/10.3389\/fnins.2022.1020086","journal-title":"Front Neurosci"},{"key":"11337_CR26","doi-asserted-by":"crossref","unstructured":"Eftimiu NJ, K\u00f6ll\u0151d C, Ulbert I, M\u00e1rton G (2022) A surface electromyography dataset for hand gesture recognition. In: 2022 IEEE 20th Jubilee International Symposium on Intelligent Systems and Informatics (SISY), pp. 000115\u2013000120. IEEE","DOI":"10.1109\/SISY56759.2022.10036305"},{"issue":"8","key":"11337_CR27","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":"11337_CR28","doi-asserted-by":"publisher","first-page":"19038","DOI":"10.1038\/s41598-019-55320-6","volume":"9","author":"A Sagheer","year":"2019","unstructured":"Sagheer A, Kotb M (2019) Unsupervised pre-training of a deep lstm-based stacked autoencoder for multivariate time series forecasting problems. Sci Rep 9(1):19038","journal-title":"Sci Rep"},{"key":"11337_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106121","volume":"206","author":"OP Idowu","year":"2021","unstructured":"Idowu OP, Ilesanmi AE, Li X, Samuel OW, Fang P, Li G (2021) An integrated deep learning model for motor intention recognition of multi-class eeg signals in upper limb amputees. Comput Methods Programs Biomed 206:106121","journal-title":"Comput Methods Programs Biomed"},{"key":"11337_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103099","volume":"71","author":"C Avian","year":"2022","unstructured":"Avian C, Prakosa SW, Faisal M, Leu J-S (2022) Estimating finger joint angles on surface emg using manifold learning and long short-term memory with attention mechanism. Biomed Signal Process Control 71:103099","journal-title":"Biomed Signal Process Control"},{"key":"11337_CR31","doi-asserted-by":"crossref","unstructured":"Akiba T, Sano S, Yanase T, Ohta T, Koyama M (2019) Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2623\u20132631","DOI":"10.1145\/3292500.3330701"},{"issue":"18","key":"11337_CR32","doi-asserted-by":"publisher","first-page":"4004","DOI":"10.3390\/math11184004","volume":"11","author":"ND Alotaibi","year":"2023","unstructured":"Alotaibi ND, Jahanshahi H, Yao Q, Mou J, Bekiros S (2023) An ensemble of long short-term memory networks with an attention mechanism for upper limb electromyography signal classification. Mathematics 11(18):4004","journal-title":"Mathematics"},{"key":"11337_CR33","doi-asserted-by":"crossref","unstructured":"Guo W, Jiang N, Farina D, Su J, Wang Z, Lin C, Xiong H (2023) Multi-attention feature fusion network for accurate estimation of finger kinematics from surface electromyographic signals. IEEE Transactions on Human-Machine Systems","DOI":"10.1109\/THMS.2023.3252817"},{"issue":"10","key":"11337_CR34","doi-asserted-by":"publisher","first-page":"2333","DOI":"10.1109\/TNSRE.2020.3022587","volume":"28","author":"IJR Martinez","year":"2020","unstructured":"Martinez IJR, Mannini A, Clemente F, Cipriani C (2020) Online grasp force estimation from the transient EMG. IEEE Trans Neural Syst Rehabil Eng 28(10):2333\u20132341. https:\/\/doi.org\/10.1109\/TNSRE.2020.3022587","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"11337_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104088","volume":"79","author":"H Mao","year":"2023","unstructured":"Mao H, Zheng Y, Ma C, Wu K, Li G, Fang P (2023) Simultaneous estimation of grip force and wrist angles by surface electromyography and acceleration signals. Biomed Signal Process Control 79:104088","journal-title":"Biomed Signal Process Control"},{"issue":"5","key":"11337_CR36","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1109\/TAI.2021.3066565","volume":"2","author":"H Su","year":"2021","unstructured":"Su H, Qi W, Li Z, Chen Z, Ferrigno G, De Momi E (2021) Deep neural network approach in EMG-based force estimation for human-robot interaction. IEEE Trans Artif Intell 2(5):404\u2013412","journal-title":"IEEE Trans Artif Intell"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11337-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11337-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11337-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T17:17:57Z","timestamp":1757179077000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11337-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,3]]},"references-count":36,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["11337"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11337-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,3]]},"assertion":[{"value":"18 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2025","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 that they have no conflict of interest regarding the research presented in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research study has received ethical approval from the Ethical Committee of Medical Research, Faculty of Dentistry, University of Jember, under protocol number No. 960\/UN25.8\/KEPK\/DL\/2020. The study was conducted in accordance with the ethical principles, and all participants provided informed consent prior to their involvement in the study. The handling of sensitive data and the protection of participants\u2019 privacy were of utmost concern throughout the research process. Any potential risks to participants were minimized and managed in strict adherence to ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}