Separating neural influences from peripheral mechanics: the speed-curvature relation in mechanically constrained actions

Hermus, James; Doeringer, Joseph A.; Sternad, Dagmar; Hogan, Neville

doi:10.1152/jn.00536.2019

Cited by 17 publications

(19 citation statements)

References 85 publications

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“…The shapes are approximately elliptical and show consistent speed fluctuations along the ellipse. Previous work showed that speed minima coincide with curvature maxima [10]. In addition, the elliptic shapes clearly show a difference in orientation between the two directions.…”

Section: A Covariance Ellipse Orientationmentioning

confidence: 59%

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Evidence for Dynamic Primitives in a Constrained Motion Task

Hermus¹,

Sternad²,

Hogan³

2020

2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)

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Ten right-handed male subjects turned a crank (radius 10 cm) in two directions at three constant instructed speeds (fast, medium, very slow) with visual speed feedback. They completed 23 trials at each speed. While the hand was constrained to move in a circle, forces against the constraint were non-zero. To disentangle the influences of biomechanics and neural control we estimated a neurally-determined motion underlying the observed movements and forces. Assuming a plausible mathematical model of interactive dynamics the peripheral neuromechanics could be 'subtracted', revealing an underlying motion that reflects neural influences. We called this data-driven construct the zero-force trajectory. The observed zero-force trajectory was approximately elliptical, with systematic changes of speed with curvature, and its orientation changed with turning direction. Its major axis, estimated by the principal eigenvector of its covariance matrix, differed significantly for different directions, but not with speed. As peripheral neuromuscular compliance (i.e. low mechanical impedance) mitigates the consequences of imperfect execution, the required precision of motion commands is reduced. To produce circular hand motions, this control strategy requires an oscillatory zero-force trajectory that leads hand motion. Due to non-isotropic dynamics, that lead differs between degrees of freedom resulting in an elliptical zero-force trajectory. The ellipses' orientations differ with direction of rotation, as observed in the experimental data. As elliptical motion is generated by two orthogonal sinusoids with non-zero phase difference, these results support our hypothesis that humans simplify this constrainedmotion task by exploiting primitive dynamic actions, oscillations and impedance.

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Section: A Covariance Ellipse Orientationmentioning

confidence: 59%

“…In a previous paper, we presented the method applied in this work [10]. The approach assumes a plausible mathematical model of interactive dynamics and used it to 'subtract off' or 'peel back' peripheral biomechanics to uncover a summary of the underlying neural influences.…”

Section: Introductionmentioning

confidence: 99%

Evidence for Dynamic Primitives in a Constrained Motion Task

Hermus¹,

Sternad²,

Hogan³

2020

2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)

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“…The impedance is a proxy for mechanical or spinal reflex mechanisms with short latencies between 25 to 80 ms [ 86 , 87 ]. In contrast to explicit error corrections in a biological system that involves transcortical sensorimotor loop delays >100 ms, impedance-based corrections are regarded as compensations around a planned sinusoidal zero-force trajectory that are either spinally mediated or that arise from intrinsic muscle mechanics [ 88 , 89 ]. While the constant impedance values assumed in this task are likely to be an oversimplification of the biological system, reliable estimates from real human movements are still missing [ 90 , 91 ].…”

Section: Discussionmentioning

confidence: 99%

Preparing to move: Setting initial conditions to simplify interactions with complex objects

et al. 2021

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Humans dexterously interact with a variety of objects, including those with complex internal dynamics. Even in the simple action of carrying a cup of coffee, the hand not only applies a force to the cup, but also indirectly to the liquid, which elicits complex reaction forces back on the hand. Due to underactuation and nonlinearity, the object’s dynamic response to an action sensitively depends on its initial state and can display unpredictable, even chaotic behavior. With the overarching hypothesis that subjects strive for predictable object-hand interactions, this study examined how subjects explored and prepared the dynamics of an object for subsequent execution of the target task. We specifically hypothesized that subjects find initial conditions that shorten the transients prior to reaching a stable and predictable steady state. Reaching a predictable steady state is desirable as it may reduce the need for online error corrections and facilitate feed forward control. Alternative hypotheses were that subjects seek to reduce effort, increase smoothness, and reduce risk of failure. Motivated by the task of ‘carrying a cup of coffee’, a simplified cup-and-ball model was implemented in a virtual environment. Human subjects interacted with this virtual object via a robotic manipulandum that provided force feedback. Subjects were encouraged to first explore and prepare the cup-and-ball before initiating a rhythmic movement at a specified frequency between two targets without losing the ball. Consistent with the hypotheses, subjects increased the predictability of interaction forces between hand and object and converged to a set of initial conditions followed by significantly decreased transients. The three alternative hypotheses were not supported. Surprisingly, the subjects’ strategy was more effortful and less smooth, unlike the observed behavior in simple reaching movements. Inverse dynamics of the cup-and-ball system and forward simulations with an impedance controller successfully described subjects’ behavior. The initial conditions chosen by the subjects in the experiment matched those that produced the most predictable interactions in simulation. These results present first support for the hypothesis that humans prepare the object to minimize transients and increase stability and, overall, the predictability of hand-object interactions.

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“…As long as the impedance operator is invertible, given measured actual position ( ) and force ( ), the zero-force trajectory can always be identified at any time [Hermus et al, 2020]. The mere existence of the zero-force trajectory 0 , under the assumption that the inverse of the impedance operator exists, is not by itself very surprising.…”

Section: Mechanical Impedancesmentioning

confidence: 99%

“…Nonetheless, based on the Norton equivalent network formulation, the behavior at an interaction port of an arbitrarily complex system can be completely summarized in a simpler form which is functionally equivalent. The other benefit is that the elements comprising the network model are experimentally identifiable (at least in principle) [Hodgson andHogan, 2000, Hermus et al, 2020]. Based on this simplified model, the two parts of the model, the equivalent motion source and equivalent impedance, can be identified from experiments in situ.…”

Section: Norton Equivalent Network Modelmentioning

confidence: 99%

Dynamic Primitives Facilitate Manipulating a Whip

Nah

Krotov

Russo

et al. 2020

2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)

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Human dexterity far exceeds that of modern robots, despite a much slower neuromuscular system. Understanding how this is accomplished may lead to improved robot control. The slow neuromuscular system of humans implies that prediction based on some form of internal model plays a prominent role. However, the nature of the model itself remains unclear. To address this problem, we focused on one of the most complex and exotic tools humans can manipulate-a whip. We tested (in simulation) whether a distant target could be reached with a whip using a (small) number of dynamic primitives, whose parameters could be learned through optimization. This approach was able to manage the complexity of an (extremely) high degree-of-freedom system and discovered the optimal parameters of the upper-limb movement that achieved the task. A detailed model of the whip dynamics was not needed for this approach, which thereby significantly relieved the computational burden of task representation and performance optimization. These results support our hypothesis that composing control using dynamic motor primitives may be a strategy which humans use to enable their remarkable dexterity. A similar approach may contribute to improved robot control.

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Separating neural influences from peripheral mechanics: the speed-curvature relation in mechanically constrained actions

Cited by 17 publications

References 85 publications

Evidence for Dynamic Primitives in a Constrained Motion Task

Evidence for Dynamic Primitives in a Constrained Motion Task

Preparing to move: Setting initial conditions to simplify interactions with complex objects

Dynamic Primitives Facilitate Manipulating a Whip

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