Minimum Acceleration Criterion with Constraints Implies Bang-Bang Control as an Underlying Principle for Optimal Trajectories of Arm Reaching Movements

2008 ◽  
Vol 20 (3) ◽  
pp. 779-812 ◽  
Author(s):  
Shay Ben-Itzhak ◽  
Amir Karniel

Rapid arm-reaching movements serve as an excellent test bed for any theory about trajectory formation. How are these movements planned? A minimum acceleration criterion has been examined in the past, and the solution obtained, based on the Euler-Poisson equation, failed to predict that the hand would begin and end the movement at rest (i.e., with zero acceleration). Therefore, this criterion was rejected in favor of the minimum jerk, which was proved to be successful in describing many features of human movements. This letter follows an alternative approach and solves the minimum acceleration problem with constraints using Pontryagin's minimum principle. We use the minimum principle to obtain minimum acceleration trajectories and use the jerk as a control signal. In order to find a solution that does not include nonphysiological impulse functions, constraints on the maximum and minimum jerk values are assumed. The analytical solution provides a three-phase piecewise constant jerk signal (bang-bang control) where the magnitude of the jerk and the two switching times depend on the magnitude of the maximum and minimum available jerk values. This result fits the observed trajectories of reaching movements and takes into account both the extrinsic coordinates and the muscle limitations in a single framework. The minimum acceleration with constraints principle is discussed as a unifying approach for many observations about the neural control of movements.

2004 ◽  
Vol 91 (1) ◽  
Author(s):  
Ken Ohta ◽  
Mikhail M. Svinin ◽  
ZhiWei Luo ◽  
Shigeyuki Hosoe ◽  
Rafael Laboissi�re

1993 ◽  
Vol 76 (3) ◽  
pp. 875-884 ◽  
Author(s):  
Taketo Furuna ◽  
Hiroshi Nagasaki

The minimum-jerk model predicts the smoothest trajectory for a class of human movements and so provides us with a kinematic measurement of skilled motor performance. To establish the limits of the model's validity, the predicted and experimentally defined movement trajectories and the joint coordination were compared in two-joint arm movements, bringing the hand from the initial position to the final position through a specified point (a via-point). Kinematic data of the movements were obtained through the SELSPOT system. The movement path, tangential velocity, and coordinated change in positions of the shoulder and elbow joints evidently deviated from those predicted by the model. These results suggest that the minimum-jerk model is not valid for movements under extreme conditions which are highly dependent on musculoskeletal dynamics.


2020 ◽  
Vol 124 (1) ◽  
pp. 295-304 ◽  
Author(s):  
Raz Leib ◽  
Marta Russo ◽  
Andrea d’Avella ◽  
Ilana Nisky

While ballistic hand reaching movements are characterized by smooth position and velocity signals, the activity of the muscles exhibits bursts and silent periods. Here, we propose that a model based on bang-bang control provides the link between the abrupt changes in the muscle activity and the smooth reaching trajectory. Using bang-bang control instead of continuous control may simplify the design of prostheses and other physical human-robot interaction systems.


2014 ◽  
Vol 95 (10) ◽  
pp. e26
Author(s):  
Sambit Mohapatra ◽  
Evan Chan ◽  
Rachael Harrington ◽  
Alexander Dromerick ◽  
Peter Turkeltaub ◽  
...  

2018 ◽  
Vol 26 (10) ◽  
pp. 2033-2043 ◽  
Author(s):  
Reza Sharif Razavian ◽  
Borna Ghannadi ◽  
Naser Mehrabi ◽  
Mark Charlet ◽  
John McPhee

2002 ◽  
Vol 87 (2) ◽  
pp. 1123-1128 ◽  
Author(s):  
Eiji Hoshi ◽  
Jun Tanji

We compared neuronal activity in the dorsal and ventral premotor areas (PMd and PMv, respectively) when monkeys were preparing to perform arm-reaching movements in a motor-set period before their actual execution. They were required to select one of four possible movements (reaching to a target on the left or right, using either the left or right arm) in accordance with two sets of instruction cues, followed by a delay period, and a subsequent motor-set period. During the motor-set period, the monkeys were required to get ready for a movement-trigger signal to start the arm-reach promptly. We analyzed the activity of 211 PMd and 109 PMv neurons that showed selectivity for the combination of the two instruction cues during the motor-set period. A majority (53%) of PMd neurons exhibited activity significantly tuned to both target location and arm use, and an approximately equal number of PMd neurons showed selectivity to either forthcoming arm use or target location. In contrast, 60% of PMv neurons showed selectivity for target location only and not for arm use. These findings point to preference in the use of neuronal activity in the two areas: preparation for action in the PMd and preparation for target acquisition in the PMv.


Basal Ganglia ◽  
2012 ◽  
Vol 2 (4) ◽  
pp. 260 ◽  
Author(s):  
S. Spadacenta ◽  
G. Giannini ◽  
N. Modugno ◽  
G. Mirabella

2017 ◽  
Vol 117 (3) ◽  
pp. 1239-1257 ◽  
Author(s):  
Layne H. Salmond ◽  
Andrew D. Davidson ◽  
Steven K. Charles

Smoothness is a hallmark of healthy movement. Past research indicates that smoothness may be a side product of a control strategy that minimizes error. However, this is not the only reason for smooth movements. Our musculoskeletal system itself contributes to movement smoothness: the mechanical impedance (inertia, damping, and stiffness) of our limbs and joints resists sudden change, resulting in a natural smoothing effect. How the biomechanics and neural control interact to result in an observed level of smoothness is not clear. The purpose of this study is to 1) characterize the smoothness of wrist rotations, 2) compare it with the smoothness of planar shoulder-elbow (reaching) movements, and 3) determine the cause of observed differences in smoothness. Ten healthy subjects performed wrist and reaching movements involving different targets, directions, and speeds. We found wrist movements to be significantly less smooth than reaching movements and to vary in smoothness with movement direction. To identify the causes underlying these observations, we tested a number of hypotheses involving differences in bandwidth, signal-dependent noise, speed, impedance anisotropy, and movement duration. Our simulations revealed that proximal-distal differences in smoothness reflect proximal-distal differences in biomechanics: the greater impedance of the shoulder-elbow filters neural noise more than the wrist. In contrast, differences in signal-dependent noise and speed were not sufficiently large to recreate the observed differences in smoothness. We also found that the variation in wrist movement smoothness with direction appear to be caused by, or at least correlated with, differences in movement duration, not impedance anisotropy. NEW & NOTEWORTHY This article presents the first thorough characterization of the smoothness of wrist rotations (flexion-extension and radial-ulnar deviation) and comparison with the smoothness of reaching (shoulder-elbow) movements. We found wrist rotations to be significantly less smooth than reaching movements and determined that this difference reflects proximal-distal differences in biomechanics: the greater impedance (inertia, damping, stiffness) of the shoulder-elbow filters noise in the command signal more than the impedance of the wrist.


2015 ◽  
Vol 113 (5) ◽  
pp. 1414-1422 ◽  
Author(s):  
Joo-Hyun Song ◽  
Robert M. McPeek

We recently demonstrated that inactivation of the primate superior colliculus (SC) causes a deficit in target selection for arm-reaching movements when the reach target is located in the inactivated field (Song JH, Rafal RD, McPeek RM. Proc Natl Acad Sci USA 108: E1433–E1440, 2011). This is consistent with the notion that the SC is part of a general-purpose target selection network beyond eye movements. To understand better the role of SC activity in reach target selection, we examined how individual SC neurons in the intermediate layers discriminate a reach target from distractors. Monkeys reached to touch a color oddball target among distractors while maintaining fixation. We found that many SC neurons robustly discriminate the goal of the reaching movement before the onset of the reach even though no saccade is made. To identify these cells in the context of conventional SC cell classification schemes, we also recorded visual, delay-period, and saccade-related responses in a delayed saccade task. On average, SC cells that discriminated the reach target from distractors showed significantly higher visual and delay-period activity than nondiscriminating cells, but there was no significant difference in saccade-related activity. Whereas a majority of SC neurons that discriminated the reach target showed significant delay-period activity, all nondiscriminating cells lacked such activity. We also found that some cells without delay-period activity did discriminate the reach target from distractors. We conclude that the majority of intermediate-layer SC cells discriminate a reach target from distractors, consistent with the idea that the SC contains a priority map used for effector-independent target selection.


2016 ◽  
Vol 116 (5) ◽  
pp. 2342-2345 ◽  
Author(s):  
Chunji Wang ◽  
Yupeng Xiao ◽  
Etienne Burdet ◽  
James Gordon ◽  
Nicolas Schweighofer

Whether the central nervous system minimizes variability or effort in planning arm movements can be tested by measuring the preferred movement duration and end-point variability. Here we conducted an experiment in which subjects performed arm reaching movements without visual feedback in fast-, medium-, slow-, and preferred-duration conditions. Results show that 1) total end-point variance was smallest in the medium-duration condition and 2) subjects preferred to carry out movements that were slower than this medium-duration condition. A parsimonious explanation for the overall pattern of end-point errors across fast, medium, preferred, and slow movement durations is that movements are planned to minimize effort as well as end-point error due to both signal-dependent and constant noise.


Sign in / Sign up

Export Citation Format

Share Document