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| Author / Editor / Organization | Title | Year | Journal / Proceedings / Book | BibTeX type | ||
|---|---|---|---|---|---|---|
| Keenan, K.G.; Santos, V.J.; Venkadesan, M. & Valero-Cuevas, F.J. | Maximal voluntary fingertip force production is not limited by movement speed in combined motion and force tasks | 2009 |
J Neurosci Vol. 29 (27) , pp. 8784-8789 |
article | ||
| Abstract: Numerous studies of limbs and fingers propose that force–velocity properties of muscle limit maximal voluntary force production during anisometric tasks, i.e., when muscles are shortening or lengthening. Although this proposition appears logical, our study on the simultaneous production of fingertip motion and force disagrees with this commonly held notion. We asked eight consenting adults to use their dominant index fingertip to maximize voluntary downward force against a horizontal surface at specific postures (static trials), and also during an anisometric "scratching" task of rhythmically moving the fingertip along a 5.8 pm 0.5 cm target line. The metronome-timed flexion–extension movement speed varied 36-fold from ``slow'' (1.0 pm 0.5 cm/s) to ``fast'' (35.9 pm 7.8 cm/s). As expected, maximal downward voluntary force diminished (44.8 pm 15.6 p = 0.001) when any motion (slow or fast) was added to the task. Surprisingly, however, a 36-fold increase in speed did not affect this reduction in force magnitude. These remarkable results for such an ordinary task challenge the dominant role often attributed to force–velocity properties of muscle and provide insight into neuromechanical interactions. We propose an explanation that the simultaneous enforcement of mechanical constraints for motion and force reduces the set of feasible motor commands sufficiently so that force–velocity properties cease to be the force-limiting factor. While additional work is necessary to reveal the governing mechanisms, the dramatic influence that the simultaneous enforcement of motion and force constraints has on force output begins to explain the vulnerability of dexterous function to development, aging, and even mild neuromuscular pathology. | ||||||
BibTeX:
@article{Keenan2009,
author = {Kevin G Keenan and Veronica J Santos and Madhusudhan Venkadesan and Francisco J Valero-Cuevas},
title = {Maximal voluntary fingertip force production is not limited by movement speed in combined motion and force tasks},
journal = {J Neurosci},
year = {2009},
volume = {29},
number = {27},
pages = {8784--8789},
doi = {http://dx.doi.org/10.1523/jneurosci.0853-09.2009}
}
|
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| Valero-Cuevas, F.J.; Venkadesan, M. & Todorov, E. | Structured variability of muscle activations supports the minimal intervention principle of motor control. | 2009 |
J Neurophysiol Vol. 102 (1) , pp. 59-68 |
article | ||
| Abstract: Numerous observations of structured motor variability indicate that the sensorimotor system preferentially controls task-relevant parameters while allowing task-irrelevant ones to fluctuate. Optimality models show that controlling a redundant musculo-skeletal system in this manner meets task demands while minimizing control effort. Although this line of inquiry has been very productive, the data are mostly behavioral with no direct physiological evidence on the level of muscle or neural activity. Furthermore, biomechanical coupling, signal-dependent noise, and alternative causes of trial-to-trial variability confound behavioral studies. Here we address those confounds and present evidence that the nervous system preferentially controls task-relevant parameters on the muscle level. We asked subjects to produce vertical fingertip force vectors of prescribed constant or time-varying magnitudes while maintaining a constant finger posture. We recorded intramuscular electromyograms (EMGs) simultaneously from all seven index finger muscles during this task. The experiment design and selective fine-wire muscle recordings allowed us to account for a median of 91% of the variance of fingertip forces given the EMG signals. By analyzing muscle coordination in the seven-dimensional EMG signal space, we find that variance-per-dimension is consistently smaller in the task-relevant subspace than in the task-irrelevant subspace. This first direct physiological evidence on the muscle level for preferential control of task-relevant parameters strongly suggest the use of a neural control strategy compatible with the principle of minimal intervention. Additionally, variance is nonnegligible in all seven dimensions, which is at odds with the view that muscle activation patterns are composed from a small number of synergies. | ||||||
BibTeX:
@article{ValeroCuevas2009,
author = {Francisco J Valero-Cuevas and Madhusudhan Venkadesan and Emanuel Todorov},
title = {Structured variability of muscle activations supports the minimal intervention principle of motor control.},
journal = {J Neurophysiol},
year = {2009},
volume = {102},
number = {1},
pages = {59--68},
doi = {http://dx.doi.org/10.1152/jn.90324.2008}
}
|
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| Venkadesan, M. & Valero-Cuevas, F.J. | Effects of neuromuscular lags on controlling contact transitions. | 2009 |
Philos Transact A Math Phys Eng Sci Vol. 367 (1891) , pp. 1163-1179 |
article | ||
| Abstract: We present a numerical exploration of contact transitions with the fingertip. When picking up objects our fingertips must make contact at specific locations, and-upon contact-maintain posture while producing well-directed force vectors. However, the joint torques for moving the fingertip towards a surface (tau(m)) are different from those for producing static force vectors (tau(f)). We previously described the neural control of such abrupt transitions in humans, and found that unavoidable errors arise because sensorimotor time delays and lags prevent an instantaneous switch between different torques. Here, we use numerical optimization on a finger model to reveal physical bounds for controlling such rapid contact transitions. Resembling human data, it is necessary to anticipatorily switch joint torques to tau(f )at about 30 ms before contact to minimize the initial misdirection of the fingertip force vector. This anticipatory strategy arises in our deterministic model from neuromuscular lags, and not from optimizing for robustness to noise/uncertainties. Importantly, the optimal solution also leads to a trade-off between the speed of force magnitude increase versus the accuracy of initial force direction. This is an alternative to prevailing theories that propose multiplicative noise in muscles as the driver of speed-accuracy trade-offs. We instead find that the speed-accuracy trade-off arises solely from neuromuscular lags. Finally, because our model intentionally uses idealized assumptions, its agreement with human data suggests that the biological system is controlled in a way that approaches the physical boundaries of performance. | ||||||
BibTeX:
@article{Venkadesan2009a,
author = {Madhusudhan Venkadesan and Francisco J Valero-Cuevas},
title = {Effects of neuromuscular lags on controlling contact transitions.},
journal = {Philos Transact A Math Phys Eng Sci},
year = {2009},
volume = {367},
number = {1891},
pages = {1163--1179},
doi = {http://dx.doi.org/10.1098/rsta.2008.0261}
}
|
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| Venkadesan, M. & Valero-Cuevas, F.J. | Neural control of motion-to-force transitions with the fingertip. | 2008 |
J Neurosci Vol. 28 (6) , pp. 1366-1373 |
article | ||
| Abstract: The neural control of tasks such as rapid acquisition of precision pinch remains unknown. Therefore, we investigated the neural control of finger musculature when the index fingertip abruptly transitions from motion to static force production. Nine subjects produced a downward tapping motion followed by vertical fingertip force against a rigid surface. We simultaneously recorded three-dimensional fingertip force, plus the complete muscle coordination pattern using intramuscular electromyograms from all seven index finger muscles. We found that the muscle coordination pattern clearly switched from that for motion to that for isometric force approximately 65 ms before contact (p = 0.0004). Mathematical modeling and analysis revealed that the underlying neural control also switched between mutually incompatible strategies in a time-critical manner. Importantly, this abrupt switch in underlying neural control polluted fingertip force vector direction beyond what is explained by muscle activation-contraction dynamics and neuromuscular noise (p < or = 0.003). We further ruled out an impedance control strategy in a separate test showing no systematic change in initial force magnitude for catch trials where the tapping surface was surreptitiously lowered and raised (p = 0.93). We conclude that the nervous system predictively switches between mutually incompatible neural control strategies to bridge the abrupt transition in mechanical constraints between motion and static force. Moreover because the nervous system cannot switch between control strategies instantaneously or exactly, there arise physical limits to the accuracy of force production on contact. The need for such a neurally demanding and time-critical strategy for routine motion-to-force transitions with the fingertip may explain the existence of specialized neural circuits for the human hand. | ||||||
BibTeX:
@article{Venkadesan2008,
author = {Madhusudhan Venkadesan and Francisco J Valero-Cuevas},
title = {Neural control of motion-to-force transitions with the fingertip.},
journal = {J Neurosci},
year = {2008},
volume = {28},
number = {6},
pages = {1366--1373},
doi = {http://dx.doi.org/10.1523/jneurosci.4993-07.2008}
}
|
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| Venkadesan, M. | Dynamic dexterous manipulation: Benefits of the edge of instability in exploring complex dynamical behavior | 2007 | School: Cornell University | phdthesis | ||
| Abstract: Behaviors such as fine manipulation in humans are quintessentially nonlinear, dynamic and complex. Exploring, quantifying and characterizing such complex dynamic systems is essential to expand our understanding of biological systems and to design artificial systems that can match the versatility and performance of their biological counterparts. However, we currently lack means to quantify or model nonlinear dynamical behavior for most such complex systems. Here we present a paradigm that exploits ubiquitous low-dimensional phenomena at the edge of instability in arbitrarily complex nonlinear dynamical systems to quantify and understand their behavior. Specifically, we examine dynamic manipulation at the edge of instability by asking subjects to compress a slender spring using their thumbpad almost to a point of instability. The spatiotemporal dynamics of a one-dimensional nonlinear dynamical system based on bifurcation theory and dynamics of spring buckling was indistinguishable from experimental measurement at the edge of instability. We then use this model to answer a neurophysiologically important question: why do we normally handle objects effortlessly without looking at them, but rely heavily on vision if our fingers were numb? We extend our simple model to incorporate feedback from noisy and time-delayed multiple sensory channels --- namely, thumbpad sensors, non-digital sensors, and vision. Using numerical optimization, we find that the selective use of vision depending on finger numbness is a cumulative effect of noise and time-delays on task-optimal multisensory integration strategies. Next we provide preliminary evidence that an upcoming treatment for thumb osteoarthritis --- intra articular Hyaluronan injection --- causes self-reported improvement in patients primarily due to pain relief and not due to any improvement in their dynamic manipulation ability. We are able to distinguish between the contribution of pain-relief and innate sensorimotor control ability because dynamic manipulation ability at low forces is independent of pain. We conclude with a brief presentation of various directions for short and long term goals that build upon the foundation established by this thesis for exploring dynamic sensorimotor behavior. We observe that there are several clinical, neurophysiological, and control theoretical benefits of studying complex dynamical systems at the edge of instability. The ubiquitous nature of well-classified low-dimensional phenomena at the edge of instability in arbitrarily complex nonlinear dynamical systems makes the techniques developed in this thesis widely applicable to biological or artificial systems alike. |
||||||
BibTeX:
@phdthesis{Venkadesan2007a,
author = {Madhusudhan Venkadesan},
title = {Dynamic dexterous manipulation: Benefits of the edge of instability in exploring complex dynamical behavior},
school = {Cornell University},
year = {2007}
}
|
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| Venkadesan, M.; Guckenheimer, J. & Valero-Cuevas, F.J. | Manipulating the edge of instability. | 2007 |
J Biomech Vol. 40 (8) , pp. 1653-1661 |
article | ||
| Abstract: We investigate the integration of visual and tactile sensory input for dynamic manipulation. Our experimental data and computational modeling reveal that time-delays are as critical to task-optimal multisensory integration as sensorimotor noise. Our focus is a dynamic manipulation task "at the edge of instability." Mathematical bifurcation theory predicts that this system will exhibit well-classified low-dimensional dynamics in this regime. The task was using the thumbpad to compress a slender spring prone to buckling as far as possible, just shy of slipping. As expected from bifurcation theory, principal components analysis gives a projection of the data onto a low dimensional subspace that captures 91-97% of its variance. In this subspace, we formulate a low-order model for the brain+hand+spring dynamics based on known mechanical and neurophysiological properties of the system. By systematically occluding vision and anesthetically blocking thumbpad sensation in 12 consenting subjects, we found that vision contributed to dynamic manipulation only when thumbpad sensation was absent. The reduced ability of the model system to compress the spring with absent sensory channels closely resembled the experimental results. Moreover, we found that the model reproduced the contextual usefulness of vision only if we took account of time-delays. Our results shed light on critical features of dynamic manipulation distinct from those of static pinch, as well as the mechanism likely responsible for loss of manual dexterity and increased reliance on vision when age or neuromuscular disease increase noisiness and/or time-delays during sensorimotor integration. | ||||||
BibTeX:
@article{Venkadesan2007b,
author = {Madhusudhan Venkadesan and John Guckenheimer and Francisco J Valero-Cuevas},
title = {Manipulating the edge of instability.},
journal = {J Biomech},
year = {2007},
volume = {40},
number = {8},
pages = {1653--1661},
doi = {http://dx.doi.org/10.1016/j.jbiomech.2007.01.022}
}
|
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| Valero-Cuevas, F.J.; Smaby, N.; Venkadesan, M.; Peterson, M. & Wright, T. | The strength-dexterity test as a measure of dynamic pinch performance. | 2003 |
J Biomech Vol. 36 (2) , pp. 265-270 |
article | ||
| Abstract: We have developed a method to quantify the dynamic interaction between fingertip force magnitude (strength) and directional control (dexterity) during pinch with a novel strength-dexterity (S-D) test based on the principle of buckling of compression springs. The test consists of asking participants to use key and opposition pinch to attempt to fully compress springs, in random order, with a wide range of combinations of strength and dexterity requirements. The minimum force required to fully compress the spring and the propensity of the spring to buckle define the strength and dexterity requirements, respectively. The S-D score for each pinch style was the sum of the strength values of all springs successfully compressed fully. We tested 3 participant groups: 18 unimpaired young adults ( |
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BibTeX:
@article{ValeroCuevas2003,
author = {Francisco J Valero-Cuevas and Niels Smaby and Madhusudhan Venkadesan and Margaret Peterson and Timothy Wright},
title = {The strength-dexterity test as a measure of dynamic pinch performance.},
journal = {J Biomech},
year = {2003},
volume = {36},
number = {2},
pages = {265--270},
doi = {http://dx.doi.org/10.1016/S0021-9290(02)00340-8}
}
|
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Created by JabRef on 2009.07.08 05:54 EDT, based on filter written by Mark Schenk and Holger Jeromin.