Literature DB >> 34211636

A segmented forearm model of hand pronation-supination approximates joint moments for real time applications.

Matthew G Yough1, Russell L Hardesty2, Sergiy Yakovenko3, Valeriya Gritsenko3.   

Abstract

Musculoskeletal modeling is a new computational tool to reverse engineer human control systems, which require efficient algorithms running in real-time. Human hand pronation-supination movement is accomplished by movement of the radius and ulna bones relative to each other via the complex proximal and distal radioulnar joints, each with multiple degrees of freedom (DOFs). Here, we report two simplified models of this complex kinematic transformation implemented as a part of a 20 DOF model of the hand and forearm. The pronation/supination DOF was implemented as a single rotation joint either within the forearm segment or separating proximal and distal parts of the forearm segment. Torques produced by the inverse dynamic simulations with anatomical architecture of the forearm (OpenSim model) were used as the "gold standard" in the comparison of two simple models. Joint placement was iteratively optimized to achieve the closest representation of torques during realistic hand movements. The model with a split forearm segment performed better than the model with a solid forearm segment in simulating pronation/supination torques. We conclude that simplifying pronation/supination DOF as a single-axis rotation between arm segments is a viable strategy to reduce the complexity of multi-DOF dynamic simulations.

Entities:  

Year:  2021        PMID: 34211636      PMCID: PMC8243400          DOI: 10.1109/ner49283.2021.9441405

Source DB:  PubMed          Journal:  Int IEEE EMBS Conf Neural Eng        ISSN: 1948-3546


  15 in total

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Authors:  Katherine R S Holzbaur; Wendy M Murray; Scott L Delp
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Authors:  Armin Biess; Dario G Liebermann; Tamar Flash
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3.  Afferent input, efference copy, signal noise, and biases in perception of joint angle during active versus passive elbow movements.

Authors:  V Gritsenko; N I Krouchev; J F Kalaska
Journal:  J Neurophysiol       Date:  2007-07-05       Impact factor: 2.714

4.  Consistency among musculoskeletal models: caveat utilitor.

Authors:  David W Wagner; Vahagn Stepanyan; James M Shippen; Matthew S Demers; Robin S Gibbons; Brian J Andrews; Graham H Creasey; Gary S Beaupre
Journal:  Ann Biomed Eng       Date:  2013-06-18       Impact factor: 3.934

5.  Neuromechanics: an integrative approach for understanding motor control.

Authors:  Kiisa Nishikawa; Andrew A Biewener; Peter Aerts; Anna N Ahn; Hillel J Chiel; Monica A Daley; Thomas L Daniel; Robert J Full; Melina E Hale; Tyson L Hedrick; A Kristopher Lappin; T Richard Nichols; Roger D Quinn; Richard A Satterlie; Brett Szymik
Journal:  Integr Comp Biol       Date:  2007-05-27       Impact factor: 3.326

6.  A platform for dynamic simulation and control of movement based on OpenSim and MATLAB.

Authors:  Misagh Mansouri; Jeffrey A Reinbolt
Journal:  J Biomech       Date:  2012-03-30       Impact factor: 2.712

7.  Real-time simulation of hand motion for prosthesis control.

Authors:  Dimitra Blana; Edward K Chadwick; Antonie J van den Bogert; Wendy M Murray
Journal:  Comput Methods Biomech Biomed Engin       Date:  2016-11-20       Impact factor: 1.763

8.  Biomechanical Constraints Underlying Motor Primitives Derived from the Musculoskeletal Anatomy of the Human Arm.

Authors:  Valeriya Gritsenko; Russell L Hardesty; Matthew T Boots; Sergiy Yakovenko
Journal:  PLoS One       Date:  2016-10-13       Impact factor: 3.240

9.  Gravitational and Dynamic Components of Muscle Torque Underlie Tonic and Phasic Muscle Activity during Goal-Directed Reaching.

Authors:  Erienne V Olesh; Bradley S Pollard; Valeriya Gritsenko
Journal:  Front Hum Neurosci       Date:  2017-09-26       Impact factor: 3.169

10.  Neuro-Musculoskeletal Mapping for Man-Machine Interfacing.

Authors:  Tamas Kapelner; Massimo Sartori; Francesco Negro; Dario Farina
Journal:  Sci Rep       Date:  2020-04-02       Impact factor: 4.379

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