Optimal trajectory formation of constrained human arm reaching movements

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


2011 ◽  
Vol 21 (11) ◽  
pp. 3293-3303 ◽  
Author(s):  
FEREYDOON NOWSHIRAVAN RAHATABAD ◽  
ALI FALLAH ◽  
AMIR HOMAYOUN JAFARI

In this paper, the feasibility of observing chaotic behavior in the model of a human arm is discussed. Two-Link Arm driven by Six Muscles (TLASM) which is a well-known model of planar human arm reaching movements in the horizontal plane is investigated. Reinforcement learning (RL) that is considered as a model for Dopamine-based learning in the brain is used to control the TLSAM. Finally, the existence of chaos phenomena in the TLASM model controlled with RL is researched using tools like bifurcation maps, Lyapunov exponents, phase-plane trajectories, and spectral analysis using FFT. Results yield that chaos phenomena may occur in the overall system by changing some internal parameters of muscles that have a physiological explanation.


2018 ◽  
Vol 99 ◽  
pp. 84-96 ◽  
Author(s):  
Kai Liu ◽  
Cai-Hua Xiong ◽  
Lei He ◽  
Wen-Bin Chen ◽  
Xiao-Lin Huang

1999 ◽  
Vol 81 (5) ◽  
pp. 2582-2586 ◽  
Author(s):  
Kiisa C. Nishikawa ◽  
Sara T. Murray ◽  
Martha Flanders

Do arm postures vary with the speed of reaching? For reaching movements in one plane, the hand has been observed to follow a similar path regardless of speed. Recent work on the control of more complex reaching movements raises the question of whether a similar “speed invariance” also holds for the additional degrees of freedom. Therefore we examined human arm movements involving initial and final hand locations distributed throughout the three-dimensional (3D) workspace of the arm. Despite this added complexity, arm kinematics (summarized by the spatial orientation of the “plane of the arm” and the 3D curvature of the hand path) changed very little for movements performed over a wide range of speeds. If the total force (dynamic + quasistatic) had been optimized by the control system (e.g., as in a minimization of the change in joint torques or the change in muscular forces), the optimal solution would change with speed; slow movements would reflect the minimal antigravity torques, whereas fast movements would be more strongly influenced by dynamic factors. The speed-invariant postures observed in this study are instead consistent with a hypothesized optimization of only the dynamic forces.


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

1997 ◽  
Vol 78 (6) ◽  
pp. 2985-2998 ◽  
Author(s):  
Gerald L. Gottlieb ◽  
Qilai Song ◽  
Gil L. Almeida ◽  
Di-An Hong ◽  
Daniel Corcos

Gottlieb, Gerald L., Qilai Song, Gil L. Almeida, Di-an Hong, and Daniel Corcos. Directional control of planar human arm movement. J. Neurophysiol. 78: 2985–2998, 1997. We examined the patterns of joint kinematics and torques in two kinds of sagittal plane reaching movements. One consisted of movements from a fixed initial position with the arm partially outstretched, to different targets, equidistant from the initial position and located according to the hours of a clock. The other series added movements from different initial positions and directions and >40–80 cm distances. Dynamic muscle torque was calculated by inverse dynamic equations with the gravitational components removed. In making movements in almost every direction, the dynamic components of the muscle torques at both the elbow and shoulder were related almost linearly to each other. Both were similarly shaped, biphasic, almost synchronous and symmetrical pulses. These findings are consistent with our previously reported observations, which we termed a linear synergy. The relative scaling of the two joint torques changes continuously and regularly with movement direction. This was confirmed by calculating a vector defined by the dynamic components of the shoulder and elbow torques. The vector rotates smoothly about an ellipse in intrinsic, joint torque space as the direction of hand motion rotates about a circle in extrinsic Cartesian space. This confirms a second implication of linear synergy that the scaling constant between the linearly related joint torques is directionally dependent. Multiple linear regression showed that the torque at each joint scales as a simple linear function of the angular displacement at both joints, in spite of the complex nonlinear dynamics of multijoint movement. The coefficients of this function are independent of the initial arm position and movement distance and are the same for all subjects. This is an unanticipated finding. We discuss these observations in terms of the hypothesis that voluntary, multiple degrees of freedom, rapid reaching movements may use rule-based, feed-forward control of dynamic joint torque. Rule-based control of joint torque with separate dynamic and static controllers is an alternative to models such as those based on the equilibrium point hypotheses that rely on a positionally based controller to produce both dynamic and static torque components. It is also an alternative to feed-forward models that directly solve the problems of inverse dynamics. Our experimental findings are not necessarily incompatible with any of the alternative models, but they describe new, additional findings for which we need to account. The rules are chosen by the nervous system according to features of the kinematic task to couple muscle contraction at the shoulder and elbow in a linear synergy. Speed and load control preserves the relative magnitudes of the dynamic torques while directional control is accomplished by modulating them in a differential manner. This control system operates in parallel with a positional control system that solves the problems of postural stability.


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.


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