<i>In vitro-virtual-reality</i>: an anatomically explicit musculoskeletal simulation powered by <i>in vitro</i> muscle using closed loop tissue-software interaction

Richards, Christopher T.; Eberhard, Enrico A.

doi:10.1242/jeb.210054

Cited by 10 publications

(5 citation statements)

References 73 publications

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“…Given these results, it is critical that the approaches to studying isolated muscles advance beyond the standard historical protocols. While direct measures of muscle force and length in vivo may not be practical for all systems, recent technical advances provide the potential to bridge the gaps in current understanding by using real-time feedback control to allow isolated muscles to interact dynamically with models (physical or virtual) (Robertson and Sawicki, 2015; Richards and Eberhard 2020; Mendoza et al 2023). Further work and new approaches are needed to understand how the nonlinear interactions between activation and strain trajectory influence muscle force and work output during dynamic contractions in vivo .…”

Section: Discussionmentioning

confidence: 99%

Linkingin vivomuscle dynamics toin situforce-length and force-velocity reveals that guinea fowl lateral gastrocnemius operates at shorter than optimal lengths

Schwaner,

Mayfield,

Azizi

et al. 2023

Preprint

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Force-length (F-L) and force-velocity (F-V) properties characterize skeletal muscles intrinsic properties under controlled conditions, and it is thought that these properties can inform and predict in vivo muscle function. Here, we map dynamic in vivo operating range and mechanical function during walking and running, to the measured in situ F-L and F-V characteristics of guinea fowl (Numida meleagris) lateral gastrocnemius (LG), a primary ankle extensor. We use in vivo patterns of muscle tendon force, fascicle length, and activation to test the hypothesis that muscle fascicles operate at optimal lengths and velocities to maximize force or power production during walking and running. Our findings only partly support our hypothesis: in vivo LG velocities are consistent with optimizing power during work production, and economy of force at higher loads. However, LG does not operate at lengths on the force plateau (+/-5% Fmax) during force production. LG length was near L0 at the time of EMG onset but shortened rapidly such that force development during stance occurred almost entirely on the ascending limb of the F-L curve, at shorter than optimal lengths. These data suggest that muscle fascicles shorten across optimal lengths in late swing, to optimize the potential for rapid force development near the swing-stance transition. This may provide resistance against unexpected perturbations that require rapid force development at foot contact. We also found evidence of passive force rise (in absence of EMG activity) in late swing, at lengths where passive force is zero in situ, suggesting that dynamic history dependent and viscoelastic effects may contribute to in vivo force development. Direct comparison of in vivo work loops and physiological operating ranges to traditional measures of F-L and F-V properties suggests the need for new approaches to characterize dynamic muscle properties in controlled conditions that more closely resemble in vivo dynamics.

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Section: Discussionmentioning

confidence: 99%

Linkingin vivomuscle dynamics toin situforce-length and force-velocity reveals that guinea fowl lateral gastrocnemius operates at shorter than optimal lengths

Schwaner,

Mayfield,

Azizi

et al. 2023

Preprint

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“…The muscle also has low-pass filter characteristics, making the control problem hard for classical approaches-in addition to the typical redundancy of having more muscles than DoF. In all our experiments, we use the MuJoCo internal muscle model, which approximates these characteristics and has been used for other muscle-based studies [17,11,23,24]. One limitation consists in the non-elasticity of the tendon.…”

Section: Muscle Modelingmentioning

confidence: 99%

DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems

Schumacher¹,

Häufle²,

Büchler³

et al. 2022

Preprint

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Muscle-actuated organisms are capable of learning an unparalleled diversity of dexterous movements despite their vast amount of muscles. Reinforcement learning (RL) on large musculoskeletal models, however, has not been able to show similar performance. We conjecture that ineffective exploration in large overactuated action spaces is a key problem. This is supported by the finding that common exploration noise strategies are inadequate in synthetic examples of overactuated systems. We identify differential extrinsic plasticity (DEP), a method from the domain of self-organization, as being able to induce state-space covering exploration within seconds of interaction. By integrating DEP into RL, we achieve fast learning of reaching and locomotion in musculoskeletal systems, outperforming current approaches in all considered tasks in sample efficiency and robustness. * See https://sites.google.com/view/dep-rl for videos. Code will be published at a later date.Preprint. Under review.

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“…Given the highly integrated nature of these systems, we recommend performing dynamic analyses that include direct measurements of muscle properties (pennation angle, starting fiber length, ofl) and resistive forces to most accurately predict the influence of changes in lever mechanics on kinematics. There are several open source musculoskeletal modeling programs available to do this (Seth et al, 2018;Todorov et al, 2012) and published examples of in house built models or studies using opensource software abound (De Schepper et al, 2008;Farris et al, 2014;Hutchinson et al, 2015;Ilton et al, 2018;Richards and Eberhard, 2020;Roberts, 2003). See the supplemental materials for example code used in this manuscript.…”

Section: What Could Replace Quasi-static Analyses?mentioning

confidence: 99%

Simple muscle-lever systems are not so simple: The need for dynamic analyses to predict lever mechanics that maximize speed

Osgood

Sutton

Cox

2020

Preprint

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Levers impose a force-velocity trade-off. In static conditions, a larger moment arm increases a muscle's force capacity, and a smaller moment arm amplifies output velocity. However, muscle force is influenced by contractile velocity and fiber length, while contractile velocity is influenced by the inertial properties of the lever system. We hypothesize that these dynamic effects constrain the functional output of a muscle-lever system. We predict that there is an optimal moment arm to maximize output velocity for any given muscle-lever configuration. Here we test this hypothesis by computationally building and systematically modifying a simple lever system. We generated 3600 modifications of this model with muscles with varying optimal fiber lengths, moment arms and starting normalized muscle lengths. For each model we simulated the motion that results from 100% activation and extracted the maximum output lever velocity. In contrast to a tradeoff between force and velocity in a lever system, we found that there was, instead, an optimal moment arm which maximized both velocity and total impulse. Increasing output velocity always required increasing output force. From this we conclude that in a dynamic lever system where muscle activation is held constant, there is no tradeoff between force and velocity.

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In vitro-virtual-reality: an anatomically explicit musculoskeletal simulation powered by in vitro muscle using closed loop tissue-software interaction

Cited by 10 publications

References 73 publications

Linkingin vivomuscle dynamics toin situforce-length and force-velocity reveals that guinea fowl lateral gastrocnemius operates at shorter than optimal lengths

Linkingin vivomuscle dynamics toin situforce-length and force-velocity reveals that guinea fowl lateral gastrocnemius operates at shorter than optimal lengths

DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems

Simple muscle-lever systems are not so simple: The need for dynamic analyses to predict lever mechanics that maximize speed

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