{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T16:36:41Z","timestamp":1776271001677,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T00:00:00Z","timestamp":1738713600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T00:00:00Z","timestamp":1738713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03651-1","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T06:11:12Z","timestamp":1738735872000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Efficient Deep Learning Model for Analyzing Muscle Activity Patterns in Biomechanical Simulations"],"prefix":"10.1007","volume":"6","author":[{"given":"Dharmendra","family":"Dangi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dheeraj Kumar","family":"Dixit","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit","family":"Bhagat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Durgesh","family":"Rao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeetendra Kumar","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"3651_CR1","doi-asserted-by":"crossref","unstructured":"Zhang X, Li S, Ying Z, Shu L. N.J.F.i.B. Sugita, and Biotechnology, Integrating musculoskeletal simulation and machine learning: a hybrid approach for personalized ankle-foot exoskeleton assistance strategies. 2024. 12: p. 1442606.","DOI":"10.3389\/fbioe.2024.1442606"},{"key":"3651_CR2","doi-asserted-by":"crossref","unstructured":"Donmazov S, Saruhan EN, Pekkan K, Piskin S. Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials. Cardiovasc Eng Technol. 2024;15(5):522\u2013549.","DOI":"10.1007\/s13239-024-00737-y"},{"key":"3651_CR3","doi-asserted-by":"crossref","unstructured":"Xiang L, Gu Y, Gao Z, Yu P, Shim V, Wang A, Fernandez J. Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors. Comput Biol Med. 2024;170:108016.","DOI":"10.1016\/j.compbiomed.2024.108016"},{"key":"3651_CR4","doi-asserted-by":"crossref","unstructured":"Xia Z, Cornish BM, Devaprakash D, Barrett RS, Lloyd DG, Hams AH, C.J.I, Pizzolato S, Engineering R. Prediction of Achilles Tendon Force During Common Motor Tasks From Markerless Video. 2024.","DOI":"10.1109\/TNSRE.2024.3403092"},{"key":"3651_CR5","doi-asserted-by":"crossref","unstructured":"Dai F, Z.J.E.E.T.o.P H, Li, Technology. Research on 2D Animation Simulation Based on Artificial Intelligence and Biomechanical Modeling. 2024. 10.","DOI":"10.4108\/eetpht.10.5907"},{"key":"3651_CR6","doi-asserted-by":"crossref","unstructured":"Wang J, Sarkar D, Mohan A, Lee M, Ma Z, Chortos AJC, Systems B. Deep Learning for Strain Field Customization in Bioreactor with Dielectric Elastomer Actuator Array. 2024.","DOI":"10.34133\/cbsystems.0155"},{"issue":"12","key":"3651_CR7","first-page":"3253","volume":"61","author":"M Mansour","year":"2023","unstructured":"Mansour M, Serbest K, Kutlu M, Cilli MJM, Engineering B, Computing. Estimation Lower limb Joint Moments Based Inverse Dynamics Approach: Comparison Mach Learn Algorithms Rapid Estimation. 2023;61(12):3253\u201376.","journal-title":"Estimation Lower limb Joint Moments Based Inverse Dynamics Approach: Comparison Mach Learn Algorithms Rapid Estimation"},{"key":"3651_CR8","volume-title":"Learning bipedal locomotion using Central Pattern generators and deep reinforcement learning","author":"AE Vinella","year":"2024","unstructured":"Vinella AE. Learning bipedal locomotion using Central Pattern generators and deep reinforcement learning. Los Angeles: University of California; 2024."},{"key":"3651_CR9","doi-asserted-by":"crossref","unstructured":"Codol O, Michaels JA, Kashefi M, Pruszynski JA, Gribble PL. MotorNet, a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks. Elife. 2024;12:RP88591.","DOI":"10.7554\/eLife.88591.4"},{"issue":"1","key":"3651_CR10","first-page":"011005","volume":"146","author":"S Tahmid","year":"2024","unstructured":"Tahmid S, Font-Llagunes JM. J.J.o.b.e. Yang. Up Extremity Muscle Activation Pattern Prediction through Synergy Extrapolation Electromyography-Driven Model. 2024;146(1):011005.","journal-title":"Up Extremity Muscle Activation Pattern Prediction through Synergy Extrapolation Electromyography-Driven Model"},{"key":"3651_CR11","doi-asserted-by":"crossref","unstructured":"Sun J, Wang Y, Hou J, Li G, Sun B. P.J.I.T.o.N.S. Lu, and R. Engineering, Deep Learning for Electromyographic Lower-Limb Motion Signal Classification Using Residual Learning. 2024.","DOI":"10.1109\/TNSRE.2024.3403723"},{"key":"3651_CR12","unstructured":"Chakravarty S, Kumar A, Hales M, Johnson JD, Xie YJWPC. Machine Learning and Computer Visualization for Monocular Biomechanical Analysis. 2024: pp. 1\u201314."},{"issue":"9","key":"3651_CR13","first-page":"2929","volume":"24","author":"F Roggio","year":"2024","unstructured":"Roggio F, Di Grande S, Cavalieri S, Falla D, Musumeci GJS. Biomech Posture Anal Healthy Adults Mach Learning: Applicability Reliab. 2024;24(9):2929.","journal-title":"Biomech Posture Anal Healthy Adults Mach Learning: Applicability Reliab"},{"key":"3651_CR14","doi-asserted-by":"crossref","unstructured":"Xu D, Zhou H, Quan W, Gusztav F, Wang M, Baker JS, Gu YJCM, Biomedicine Pi. Accurately and effectively predict the ACL force: Utilizing biomechanical landing pattern before and after-fatigue. 2023. 241: p. 107761.","DOI":"10.1016\/j.cmpb.2023.107761"},{"key":"3651_CR15","unstructured":"July. 2020; Available from: https:\/\/www.facebook.com\/developmentofsports\/photos\/a.153315666239660\/169868171251076\/?type=3"},{"key":"3651_CR16","first-page":"123953","volume":"250","author":"R Moura","year":"2024","unstructured":"Moura R, Oliveira DA, Ferreira JP, Parente MP, Kimmich N, Jorge. Finite element-based Mach Learn Framew Predict Mech Behav Pelvic Floor Muscles Dur Childbirth. 2024;250:123953.","journal-title":"Finite element-based Mach Learn Framew Predict Mech Behav Pelvic Floor Muscles Dur Childbirth"},{"key":"3651_CR17","doi-asserted-by":"crossref","unstructured":"Ma S, Zhang J, Shi C, Di P, Robertson ID, Zhang Z.-Q., Physics-informed Deep Learn Muscle Force Prediction Unlabeled sEMG Signals, in IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2024;32;1246\u20131256.","DOI":"10.1109\/TNSRE.2024.3375320"},{"key":"3651_CR18","doi-asserted-by":"crossref","unstructured":"Ma S, Mendez Guerra I, Caillet AH, Zhao J, Clarke AK, Maksymenko K, Deslauriers-Gauthier S, Sheng X, Zhu X, Farina D. NeuroMotion: open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement, PLoS Comput Biol. 2024;20(7):e1012257.","DOI":"10.1371\/journal.pcbi.1012257"},{"key":"3651_CR19","doi-asserted-by":"crossref","unstructured":"Ma S, Clarke AK, Maksymenko K, Deslauriers-Gauthier S, Sheng X, Zhu X. D.J.I.T.o.N.N. Farina, and L. Systems, Conditional generative models for simulation of EMG during naturalistic movements. 2024.","DOI":"10.1109\/TNNLS.2024.3438368"},{"issue":"3","key":"3651_CR20","first-page":"528","volume":"5","author":"SP Sitole","year":"2023","unstructured":"Sitole SP, F.C.J.I.T.o.M R, Sup, Bionics. Continuous Prediction Hum Joint Mech Using emg Signals: Rev model-based model-free Approaches. 2023;5(3):528\u201346.","journal-title":"Continuous Prediction Hum Joint Mech Using emg Signals: Rev model-based model-free Approaches"},{"key":"3651_CR21","doi-asserted-by":"crossref","unstructured":"Han Y, Chen Y, Ong C, Chen J, Hicks J. and Teran J, a neural network model for efficient Musculoskeletal-Driven skin deformation, ACM Transactions on Graphics (TOG). 2024;43(4):1\u201312.","DOI":"10.1145\/3658135"},{"key":"3651_CR22","first-page":"111967","volume":"166","author":"F Ghezelbash","year":"2024","unstructured":"Ghezelbash F, Eskandari AH, Robert-Lachaine X, Cao S, Pesteie M, Qiao Z, Shirazi-Adl A. J.o.B. Larivi\u00e8re. Mach Learn Appl Spine Biomech. 2024;166:111967.","journal-title":"Mach Learn Appl Spine Biomech"},{"key":"3651_CR23","doi-asserted-by":"crossref","unstructured":"Shi Y, Ma S, Zhao Y, Shi C, Zhang ZJIJoB, Informatics H. A Physics-Informed Low-Shot Adversarial Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics. 2023.","DOI":"10.1109\/JBHI.2023.3347672"},{"key":"3651_CR24","doi-asserted-by":"crossref","unstructured":"Gei\u00df H-J, Al-Hafez F, Seyfarth A, Peters J. and Tateo D. Exciting Action: Investigating Efficient Explor Learn Musculoskelet Humanoid Locomotion. arXiv preprint arXiv:2407. 2024:11658.","DOI":"10.1109\/Humanoids58906.2024.10769835"},{"key":"3651_CR25","doi-asserted-by":"crossref","unstructured":"Liang W, Wang F, Fan A, Zhao W, Yao W, Yang P. Extended application of inertial measurement units in biomechanics: from activity recognition to force estimation. Sensors. 2023;23(9):4229.","DOI":"10.3390\/s23094229"},{"key":"3651_CR26","unstructured":"Etemi M. Design and validation of a real-time, deep learning-based muscle activity detector for clinical and robotic applications. Politecnico di Torino; 2024."},{"issue":"1","key":"3651_CR27","first-page":"1","volume":"56","author":"C Zheng","year":"2023","unstructured":"Zheng C, Wu W, Chen C, Yang T, Zhu S, Shen J, Kehtarnavaz N, Shah MJACS. Deep learning-based Hum pose Estimation: Surv. 2023;56(1):1\u201337.","journal-title":"Deep learning-based Hum pose Estimation: Surv"},{"key":"3651_CR28","doi-asserted-by":"crossref","unstructured":"Schweihoff JF, Loshakov M, Pavlova I, K\u00fcck L, Ewell LA, K. M. J.C.b. Schwarz, DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection. 2021. 4(1): p. 130.","DOI":"10.1038\/s42003-021-01654-9"},{"key":"3651_CR29","doi-asserted-by":"crossref","unstructured":"Chen H, Feng R, Wu S, Xu H, Zhou F, Liu ZJMS. 2D Human pose estimation: A survey. 2023. 29(5): pp. 3115\u20133138.","DOI":"10.1007\/s00530-022-01019-0"},{"issue":"1","key":"3651_CR30","first-page":"6","volume":"32","author":"A Kadkhodamohammadi","year":"2021","unstructured":"Kadkhodamohammadi A. N.J.M.V. Padoy 2021 Applications. Generalizable Approach multi-view 3d Hum pose Regres 32 1 6.","journal-title":"Generalizable Approach multi-view 3d Hum pose Regres"},{"key":"3651_CR31","doi-asserted-by":"crossref","unstructured":"Shield S, Muramatsu N, Da Silva Z, J.J.o.E A. Chasing the cheetah: how field biomechanics has evolved to keep up with the fastest land animal. 2023. 226(Suppl_1): p. jeb245122.","DOI":"10.1242\/jeb.245122"},{"issue":"Suppl1","key":"3651_CR32","first-page":"jeb243292","volume":"225","author":"DS Moen","year":"2022","unstructured":"Moen DS, Cabrera-Guzm\u00e1n E, Caviedes-Solis IW, Gonz\u00e1lez-Bernal E, and A.R.J.J., Hanna B. Phylogenetic Anal Adaptation Comp Physiol Biomechanics: Overv case Study Therm Physiol Treefrogs. 2022;225(Suppl1):jeb243292.","journal-title":"Phylogenetic Anal Adaptation Comp Physiol Biomechanics: Overv case Study Therm Physiol Treefrogs"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03651-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03651-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03651-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T06:11:28Z","timestamp":1738735888000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03651-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,5]]},"references-count":32,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["3651"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03651-1","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,5]]},"assertion":[{"value":"30 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 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":"Not Applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human and\/or Animals"}},{"value":"It was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"138"}}