Optimal Control of Redundant Muscles in Step-Tracking Wrist Movements

Haruno, Masahiko; Wolpert, Daniel M.

doi:10.1152/jn.00404.2005

Cited by 70 publications

(65 citation statements)

References 25 publications

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“…h, Percentage of time-out errors, as signaled during the experiment. Note that for data analysis purposes, we increased the threshold on movement duration by 100 ms. studied here, phenomena that are problematic for these models include the undershoot of primary saccades (Harris, 1995), the overshoot of rapid wrist movements (Hoffman and Strick, 1999;Haruno and Wolpert, 2005), and the lack of equifinality (or failure to reach the target) in certain adaptation paradigms (Lackner and Dizio, 1994;Hinder and Milner, 2003).…”

Section: Optimal Feedback Control Versus Alternative Modelsmentioning

confidence: 99%

Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control

Liú¹,

Todorov²

2007

J. Neurosci.

390

400

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Everyday movements pursue diverse and often conflicting mixtures of task goals, requiring sensorimotor strategies customized for the task at hand. Such customization is mostly ignored by traditional theories emphasizing movement geometry and servo control. In contrast, the relationship between the task and the strategy most suitable for accomplishing it lies at the core of our optimal feedback control theory of coordination. Here, we show that the predicted sensitivity to task goals affords natural explanations to a number of novel psychophysical findings. Our point of departure is the little-known fact that corrections for target perturbations introduced late in a reaching movement are incomplete. We show that this is not simply attributable to lack of time, in contradiction with alternative models and, somewhat paradoxically, in agreement with our model. Analysis of optimal feedback gains reveals that the effect is partly attributable to a previously unknown trade-off between stability and accuracy. This yields a testable prediction: if stability requirements are decreased, then accuracy should increase. We confirm the prediction experimentally in three-dimensional obstacle avoidance and interception tasks in which subjects hit a robotic target with programmable impedance. In additional agreement with the theory, we find that subjects do not rely on rigid control strategies but instead exploit every opportunity for increased performance. The modeling methodology needed to capture this extra flexibility is more general than the linear-quadratic methods we used previously. The results suggest that the remarkable flexibility of motor behavior arises from sensorimotor control laws optimized for composite cost functions.

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Section: Optimal Feedback Control Versus Alternative Modelsmentioning

confidence: 99%

Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control

Liú¹,

Todorov²

2007

J. Neurosci.

390

400

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“…Variability in force production ("motor noise") has also been hypothesized as a cost to optimize (Harris and Wolpert, 1998;Haruno and Wolpert, 2005;O'Sullivan et al, 2009;Diedrichsen et al, 2010) but it covaries with muscle activity under natural conditions (Jones et al, 2002). To dissociate these factors, we manipulated the virtual biomechanics to increase the signaldependent noise associated with muscle activation in one of the five muscles (i.e., an extensor muscle, n ϭ 3; or a flexor muscle, n ϭ 3).…”

Section: Adaptation To Novel Virtual Biomechanics With Motor Noise-exmentioning

confidence: 99%

“…The computational framework of optimal control theory has gained influence as a general theory of motor coordination because it can specify uniquely how behavioral goals should be achieved by minimizing costs such as the effort or variability of movement (Pedotti et al, 1978;Crowninshield and Brand, 1981;Davy and Audu, 1987;Loeb et al, 1990;Harris and Wolpert, 1998;Todorov and Jordan, 2002;Scott, 2004;Todorov, 2004;O'Sullivan et al, 2009;Diedrichsen et al, 2010). Although some aspects of natural motor behavior, such as typical patterns of muscle activity, are consistent with the output of optimal control models (Fagg et al, 2002;Haruno and Wolpert, 2005;Diedrichsen et al, 2010), how the nervous system generates this behavior is unknown. For instance, it is unclear whether the CNS achieves behavior that appears to be optimal through online, top-down optimization, or because the architecture of distributed sensorimotor networks has evolved to favor successful and efficient behavior.…”

Section: Introductionmentioning

confidence: 99%

Muscle Coordination Is Habitual Rather than Optimal

Rugy¹,

Loeb²,

Carroll³

2012

J. Neurosci.

201

163

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When sharing load among multiple muscles, humans appear to select an optimal pattern of activation that minimizes costs such as the effort or variability of movement. How the nervous system achieves this behavior, however, is unknown. Here we show that contrary to predictions from optimal control theory, habitual muscle activation patterns are surprisingly robust to changes in limb biomechanics. We first developed a method to simulate joint forces in real time from electromyographic recordings of the wrist muscles. When the model was altered to simulate the effects of paralyzing a muscle, the subjects simply increased the recruitment of all muscles to accomplish the task, rather than recruiting only the useful muscles. When the model was altered to make the force output of one muscle unusually noisy, the subjects again persisted in recruiting all muscles rather than eliminating the noisy one. Such habitual coordination patterns were also unaffected by real modifications of biomechanics produced by selectively damaging a muscle without affecting sensory feedback. Subjects naturally use different patterns of muscle contraction to produce the same forces in different pronation-supination postures, but when the simulation was based on a posture different from the actual posture, the recruitment patterns tended to agree with the actual rather than the simulated posture. The results appear inconsistent with computation of motor programs by an optimal controller in the brain. Rather, the brain may learn and recall command programs that result in muscle coordination patterns generated by lower sensorimotor circuitry that are functionally "good-enough."

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“…These stages encompass the simultaneous activity of several muscles acting on one joint (Haruno & Wolpert, 2005), multi-joint synergies (Debicki & Gribble, 2005;Gottlieb, Song, Hong, Almeida, & Corcos, 1996;Latash, Aruin, & Shapiro, 1995;Santello, Flanders, & Soechting, 1998;Yang, Zhang, Huang & Jin, 2002) or the combination of basic behavioral modules (Bizzi, 2003;d'Avella & Bizzi, 2005;d'Avella, Saltiel, & Mussa-Ivaldi, Giszter, & Bizzi, 1994;Sanger, 2000). Although several mathematical methods are suitable for detecting synergies, some of which exhibit even better performance than Principal Component Analysis (Tresch, Cheung, & d'Avella, 2006), PCA is a common tool in the analysis of motor behavior and often reveals evidence of fundamental building blocks of the studied movements (Daffertshofer, Lamoth, Meijer, & Beek, 2004).…”

Section: Introductionmentioning

confidence: 99%

Inter-joint coupling and joint angle synergies of human catching movements

Bockemühl

Troje

Dürr

2010

Human Movement Science

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A central question in motor control is how the central nervous system (CNS) deals with redundant degrees of freedom (DoFs) inherent in the musculoskeletal system. One way to simplify control of a redundant system is to combine several DoFs into synergies. In reaching movements of the human arm, redundancy occurs at the kinematic level because there are an unlimited number of arm postures for each position of the hand. Redundancy also occurs at the level of muscle forces because each arm posture can be maintained by a set of muscle activation patterns. Both postural and force-related motor synergies may contribute to simplify the control problem. The present study analyzes the kinematic complexity of natural, unrestrained human arm movements, and detects the amount of kinematic synergy in a vast variety of arm postures. We have measured interjoint coupling of the human arm and shoulder girdle during fast, unrestrained and untrained catching movements. Participants were asked to catch a ball launched towards them on 16 different trajectories. These had to be reached from two different initial positions. Movement of the right arm was recorded using optical motion capture and was transformed into 10 joint angle time courses, corresponding to 3 DoFs of the shoulder girdle and 7 of the arm. The resulting time series of the arm postures were analyzed by Principal Components Analysis (PCA). We found that the first three principal components (PCs) always captured more than 97% of the variance. Furthermore, subspaces spanned by PC sets associated with different catching positions varied smoothly across the arm's workspace. When we pooled complete sets of movements, three PCs, the theoretical minimum for reaching in 3D space, were sufficient to explain 80% of the data's variance. We assumed that the linearly correlated DoFs of each significant PC represent cardinal joint angle synergies, and showed that catching movements towards a multitude of targets in the arm's workspace can be generated efficiently by linear combinations of three of such synergies. The contribution of each synergy changed during a single catching movement and often varied systematically with target location. We conclude that unrestrained, one-handed catching movements are dominated by strong kinematic couplings between the joints that reduce the kinematic complexity of the human arm and shoulder girdle to three non-redundant DoFs. ACCEPTED MANUSCRIPT4

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Optimal Control of Redundant Muscles in Step-Tracking Wrist Movements

Cited by 70 publications

References 25 publications

Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control

Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control

Muscle Coordination Is Habitual Rather than Optimal

Inter-joint coupling and joint angle synergies of human catching movements

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