Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Formation and control of optimal trajectory in human multijoint arm movement

  • Published:
Biological Cybernetics Aims and scope Submit manuscript

Abstract

In this paper, we study trajectory planning and control in voluntary, human arm movements. When a hand is moved to a target, the central nervous system must select one specific trajectory among an infinite number of possible trajectories that lead to the target position. First, we discuss what criterion is adopted for trajectory determination. Several researchers measured the hand trajectories of skilled movements and found common invariant features. For example, when moving the hand between a pair of targets, subjects tended to generate roughly straight hand paths with bell-shaped speed profiles. On the basis of these observations and dynamic optimization theory, we propose a mathematical model which accounts for formation of hand trajectories. This model is formulated by defining an objective function, a measure of performance for any possible movement: square of the rate of change of torque integrated over the entire movement. That is, the objective function C T is defined as follows:

$$C_T = \frac{1}{2}{}^t\int\limits_0^f {\sum\limits_{i = 1}^n {\left( {\frac{{{\text{d}}z_i }}{{{\text{d}}t}}} \right)^2 {\text{d}}t,} } $$

where z iis the torque generated by the i-th actuator (muslce) out of n actuators, and t fis the movement time. Since this objective function critically depends on the complex nonlinear dynamics of the musculoskeletal system, it is very difficult to determine the unique trajectory which yields the best performance. We overcome this difficult by developing an iterative scheme, with which the optimal trajectory and the associated motor command are simultaneously computed. To evaluate our model, human hand trajectories were experimentally measured under various behavioral situations. These results supported the idea that the human hand trajectory is planned and controlled in accordance with the minimum torquechange criterion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Abend W, Bizzi E, Morasso P (1982) Human arm trajectory formation. Brain 105:331–348

    Google Scholar 

  • Atkeson CG, Hollerbach JM (1985) Kinematic features of unrestrained vertical arm movements. J Neurosci 5:2318–2330

    Google Scholar 

  • Bizzi E, Accornero N, Chapple W, Hogan N (1984) Posture control and trajectory formation during arm movement. J Neurosci 4:2738–2744

    Google Scholar 

  • Bryson AE, Ho YC (1975) Applied optimal control, Wiley, New York

    Google Scholar 

  • Bullock D, Grossberg S (1988) Neural dynamics of planned arm movements: Emergent invariants and speed-accuracy properties during trajectory formation. Psychol Rev 95:49–90

    Google Scholar 

  • Cannon SC, Zahalak GI (1982) The mechanical behavior of active human skeletal muscle in small oscillations. J Biomech 15:111–121

    Google Scholar 

  • Flash T (1987) The control of hand equilibrium trajectories in multi-joint arm movements. Biol Cybern 57:257–274

    Google Scholar 

  • Flash T, Hogan N (1985) The coordination of arm movements: An experimentally confirmed mathematical model. J. Neurosci 5:1688–1703

    Google Scholar 

  • Hasan Z (1986) Optimized movement trajectories and joint stiffness in unperturbed, inertially loaded movements. Biol Cybern 53:373–382

    Google Scholar 

  • Hogan N (1984) An organizing principle for a class of voluntary movements. J Neurosci 4:2745–2754

    Google Scholar 

  • Hollerbach JM, Flash T (1982) Dynamic interactions between limb segments during planar arm movement. Biol Cybern 44:66–77

    Google Scholar 

  • Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79:2554–2558

    Google Scholar 

  • Hopfield JJ, Tank DW (1985) “Neural” computation of decisions in optimization problems. Biol Cybern 52:141–152

    Google Scholar 

  • Kawato M, Furukawa K, Suzuki R (1987) A hierarchical neuralnetwork model for control and learning of voluntary movement. Biol Cybern 57:169–185

    Google Scholar 

  • Kawato M, Uno Y, Isobe M, Suzuki R (1988a) A hierarchical neural network model for voluntary movement with application to robotics. IEEE Control Sys Mag 8:8–16

    Google Scholar 

  • Kawato M, Isobe M, Maeda Y, Suzuki R (1988b) Coordinates transformation and learning control for visually-guided voluntary movement with iteration: A Newton-like method in a function space. Biol Cybern 59:161–177

    Google Scholar 

  • Koch C, Marroquin J, Yuille A (1986) Analog “neuronal” networks in early vision. Proc Natl Acad Sci USA 83:4263–4267

    Google Scholar 

  • Marr D (1982) Vision. Freeman, New York

    Google Scholar 

  • Mitsui T (1981) Newton method for the boundary-value problems of the differential equations (in Japanese). Math Sci 218:41–46

    Google Scholar 

  • Morasso P (1981) Spatial control of arm movements. Exp Brain Res 42:223–227

    Google Scholar 

  • Nelson WL (1983) Physical principles for economies of skilled movements. Biol Cybern 46:135–147

    Google Scholar 

  • Ojika T, Kasue Y (1979) Initial-value adjusting method for the solution of nonlinear multipoint boundary-value problems. J Math Anal Appl 69:359–371

    Google Scholar 

  • Poggio T, Torre V, Koch C (1985) Computational vision and regularization theory. Nature 317:314–319

    Google Scholar 

  • Polite A, Bizzi E (1979) Characteristics of the motor programs underlying arm movements in monkeys. J Neurophysiol 42:183–194

    Google Scholar 

  • Uno Y, Kawato M, Suzuki R (1987) Formation of optimum trajectory in control of arm movement — minimum torque- change model — Japan IEICE Technical Report, MBE86-79, 9–16

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Uno, Y., Kawato, M. & Suzuki, R. Formation and control of optimal trajectory in human multijoint arm movement. Biol. Cybern. 61, 89–101 (1989). https://doi.org/10.1007/BF00204593

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00204593

Keywords