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If the models are sufficiently accurate, they may embed complex relationships within the sensorimotor system. These potential benefits are limited by the challenge of implementing fast and accurate musculoskeletal computations. A typical hand muscle spans over 3 degrees of freedom (DOF), wrapping over complex geometrical constraints that change its moment arms and lead to complex posture-dependent variation in torque generation. Here, we report a method to accurately and efficiently calculate musculotendon length and moment arms across all physiological postures of the forearm muscles that actuate the hand and wrist. Then, we use this model to test the hypothesis that the functional similarities of muscle actions are embedded in muscle structure. The posture dependent muscle geometry, moment arms and lengths of modeled muscles were captured using autogenerating polynomials that expanded their optimal selection of terms using information measurements. The iterative process approximated 33 musculotendon actuators, each spanning up to 6 DOFs in an 18 DOF model of the human arm and hand, defined over the full physiological range of motion. Using these polynomials, the entire forearm anatomy could be computed in &lt;10 \u03bcs, which is far better than what is required for real-time performance, and with low errors in moment arms (below 5%) and lengths (below 0.4%). Moreover, we demonstrate that the number of elements in these autogenerating polynomials does not increase exponentially with increasing muscle complexity; complexity increases linearly instead. Dimensionality reduction using the polynomial terms alone resulted in clusters comprised of muscles with similar functions, indicating the high accuracy of approximating models. We propose that this novel method of describing musculoskeletal biomechanics might further improve the applications of detailed and scalable models to describe human movement.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1008350","type":"journal-article","created":{"date-parts":[[2020,12,16]],"date-time":"2020-12-16T16:38:31Z","timestamp":1608136711000},"page":"e1008350","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":16,"title":["Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0513-9194","authenticated-orcid":true,"given":"Anton","family":"Sobinov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew T.","family":"Boots","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6408-9433","authenticated-orcid":true,"given":"Valeriya","family":"Gritsenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9072-3119","authenticated-orcid":true,"given":"Lee E.","family":"Fisher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6202-5818","authenticated-orcid":true,"given":"Robert A.","family":"Gaunt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5946-6409","authenticated-orcid":true,"given":"Sergiy","family":"Yakovenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2020,12,16]]},"reference":[{"key":"pcbi.1008350.ref001","article-title":"Literature review on needs of upper limb prosthesis users","author":"F Cordella","year":"2016","journal-title":"Frontiers in Neuroscience"},{"key":"pcbi.1008350.ref002","doi-asserted-by":"crossref","first-page":"e112091","DOI":"10.1371\/journal.pone.0112091","article-title":"A simple ERP method for quantitative analysis of cognitive workload in myoelectric prosthesis control and human-machine interaction","volume":"9","author":"S Deeny","year":"2014","journal-title":"PloS One"},{"key":"pcbi.1008350.ref003","article-title":"Hierarchical models of object recognition in cortex","author":"M Riesenhuber","year":"1999","journal-title":"Nat Neurosci"},{"key":"pcbi.1008350.ref004","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s11633-017-1054-2","article-title":"Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review","volume":"14","author":"T Poggio","year":"2017","journal-title":"Int J Autom Comput"},{"key":"pcbi.1008350.ref005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-08987-4","article-title":"Unmasking Clever Hans predictors and assessing what machines really learn","volume":"10","author":"S Lapuschkin","year":"2019","journal-title":"Nat Commun"},{"key":"pcbi.1008350.ref006","first-page":"1693","volume-title":"Advances in Neural Information Processing Systems 28","author":"KM Hermann","year":"2015"},{"key":"pcbi.1008350.ref007","first-page":"1","author":"J Weston","year":"2014","journal-title":"Memory Networks"},{"key":"pcbi.1008350.ref008","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/S0959-4388(99)00028-8","article-title":"Internal models for motor control and trajectory planning","volume":"9","author":"M. 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