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      Modern three-dimensional digital methods for studying locomotor biomechanics in tetrapods

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          ABSTRACT

          Here, we review the modern interface of three-dimensional (3D) empirical (e.g. motion capture) and theoretical (e.g. modelling and simulation) approaches to the study of terrestrial locomotion using appendages in tetrapod vertebrates. These tools span a spectrum from more empirical approaches such as XROMM, to potentially more intermediate approaches such as finite element analysis, to more theoretical approaches such as dynamic musculoskeletal simulations or conceptual models. These methods have much in common beyond the importance of 3D digital technologies, and are powerfully synergistic when integrated, opening a wide range of hypotheses that can be tested. We discuss the pitfalls and challenges of these 3D methods, leading to consideration of the problems and potential in their current and future usage. The tools (hardware and software) and approaches (e.g. methods for using hardware and software) in the 3D analysis of tetrapod locomotion have matured to the point where now we can use this integration to answer questions we could never have tackled 20 years ago, and apply insights gleaned from them to other fields.

          Abstract

          Summary: Three-dimensional digital methods useful for the study of tetrapod locomotor biomechanics have matured in recent decades. Here, we summarise their progress, relevance and potential.

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          DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

          Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
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            OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement

            Movement is fundamental to human and animal life, emerging through interaction of complex neural, muscular, and skeletal systems. Study of movement draws from and contributes to diverse fields, including biology, neuroscience, mechanics, and robotics. OpenSim unites methods from these fields to create fast and accurate simulations of movement, enabling two fundamental tasks. First, the software can calculate variables that are difficult to measure experimentally, such as the forces generated by muscles and the stretch and recoil of tendons during movement. Second, OpenSim can predict novel movements from models of motor control, such as kinematic adaptations of human gait during loaded or inclined walking. Changes in musculoskeletal dynamics following surgery or due to human–device interaction can also be simulated; these simulations have played a vital role in several applications, including the design of implantable mechanical devices to improve human grasping in individuals with paralysis. OpenSim is an extensible and user-friendly software package built on decades of knowledge about computational modeling and simulation of biomechanical systems. OpenSim’s design enables computational scientists to create new state-of-the-art software tools and empowers others to use these tools in research and clinical applications. OpenSim supports a large and growing community of biomechanics and rehabilitation researchers, facilitating exchange of models and simulations for reproducing and extending discoveries. Examples, tutorials, documentation, and an active user forum support this community. The OpenSim software is covered by the Apache License 2.0, which permits its use for any purpose including both nonprofit and commercial applications. The source code is freely and anonymously accessible on GitHub, where the community is welcomed to make contributions. Platform-specific installers of OpenSim include a GUI and are available on simtk.org.
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              Flexing computational muscle: modeling and simulation of musculotendon dynamics.

              Muscle-driven simulations of human and animal motion are widely used to complement physical experiments for studying movement dynamics. Musculotendon models are an essential component of muscle-driven simulations, yet neither the computational speed nor the biological accuracy of the simulated forces has been adequately evaluated. Here we compare the speed and accuracy of three musculotendon models: two with an elastic tendon (an equilibrium model and a damped equilibrium model) and one with a rigid tendon. Our simulation benchmarks demonstrate that the equilibrium and damped equilibrium models produce similar force profiles but have different computational speeds. At low activation, the damped equilibrium model is 29 times faster than the equilibrium model when using an explicit integrator and 3 times faster when using an implicit integrator; at high activation, the two models have similar simulation speeds. In the special case of simulating a muscle with a short tendon, the rigid-tendon model produces forces that match those generated by the elastic-tendon models, but simulates 2-54 times faster when an explicit integrator is used and 6-31 times faster when an implicit integrator is used. The equilibrium, damped equilibrium, and rigid-tendon models reproduce forces generated by maximally-activated biological muscle with mean absolute errors less than 8.9%, 8.9%, and 20.9% of the maximum isometric muscle force, respectively. When compared to forces generated by submaximally-activated biological muscle, the forces produced by the equilibrium, damped equilibrium, and rigid-tendon models have mean absolute errors less than 16.2%, 16.4%, and 18.5%, respectively. To encourage further development of musculotendon models, we provide implementations of each of these models in OpenSim version 3.1 and benchmark data online, enabling others to reproduce our results and test their models of musculotendon dynamics.
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                Author and article information

                Contributors
                Journal
                J Exp Biol
                J Exp Biol
                JEB
                The Journal of Experimental Biology
                The Company of Biologists Ltd
                0022-0949
                1477-9145
                25 April 2023
                22 February 2023
                22 February 2023
                : 226
                : Suppl 1 , Special Issue: A Century of Comparative Biomechanics: Emerging and Historical Perspectives on an Interdisciplinary Field
                : jeb245132
                Affiliations
                [ 1 ]Department of Earth Sciences, University of Cambridge , Cambridge, CB2 3EQ, UK
                [ 2 ]Palaeontological Institute and Museum, University of Zurich , 8006 Zürich, Switzerland
                [ 3 ]Structure and Motion Laboratory, Department of Comparative Biomedical Sciences, Royal Veterinary College , North Mymms, AL9 7TA, UK
                [ 4 ]McDonald Institute for Archaeological Research, University of Cambridge , Cambridge, CB2 3ER, UK
                Author notes
                [*]

                In alphabetical order.

                []Author for correspondence ( jhutchinson@ 123456rvc.ac.uk )

                Competing interests

                The authors declare no competing or financial interests.

                Author information
                http://orcid.org/0000-0003-4876-1311
                http://orcid.org/0000-0003-3640-9695
                http://orcid.org/0000-0002-8299-3434
                http://orcid.org/0000-0002-9575-4387
                http://orcid.org/0000-0002-6767-7038
                Article
                JEB245132
                10.1242/jeb.245132
                10042237
                36810943
                d902eccd-43c3-42c5-898c-a39cb1106f40
                © 2023. Published by The Company of Biologists Ltd

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                Funding
                Funded by: Horizon 2020, http://dx.doi.org/10.13039/100010661;
                Award ID: 695517
                Funded by: The Royal Veterinary College;
                Categories
                Review

                Molecular biology
                animation,bone,dynamics,gait,muscle,optimization
                Molecular biology
                animation, bone, dynamics, gait, muscle, optimization

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