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An analytical approach to the problem of inverse optimization with additive objective functions: an application to human prehension

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Abstract

We consider the problem of what is being optimized in human actions with respect to various aspects of human movements and different motor tasks. From the mathematical point of view this problem consists of finding an unknown objective function given the values at which it reaches its minimum. This problem is called the inverse optimization problem. Until now the main approach to this problems has been the cut-and-try method, which consists of introducing an objective function and checking how it reflects the experimental data. Using this approach, different objective functions have been proposed for the same motor action. In the current paper we focus on inverse optimization problems with additive objective functions and linear constraints. Such problems are typical in human movement science. The problem of muscle (or finger) force sharing is an example. For such problems we obtain sufficient conditions for uniqueness and propose a method for determining the objective functions. To illustrate our method we analyze the problem of force sharing among the fingers in a grasping task. We estimate the objective function from the experimental data and show that it can predict the force-sharing pattern for a vast range of external forces and torques applied to the grasped object. The resulting objective function is quadratic with essentially non-zero linear terms.

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Abbreviations

x :

An independent variable

J :

An objective function of an optimization problem

\({\mathcal C}\) :

Constraints for an optimization problem

\({\left\langle J,\mathcal C\right\rangle}\) :

An optimization problem with the objective function J and the constrains \({\mathcal{C}}\)

f i (·), g i (·):

Scalar functions

C, b:

A matrix and a vector of the linear constraints Cxb

a i :

A scalar value

\({\mathcal I}\) :

A set of indexes

References

  • Ahuja RK, Orlin JB (2001) Inverse optimization. Oper Res 49(5): 771–783

    Article  MATH  MathSciNet  Google Scholar 

  • Ait-Haddou R, Binding P, Herzog W (2000) Theoretical considerations on cocontraction of sets of agonistic and antagonistic muscles. J Biomech 33(9): 1105–1111

    Article  Google Scholar 

  • Anderson FC, Pandy MG (1999) A dynamic optimization solution for vertical jumping in three dimensions. Comput Methods Biomech Biomed Eng 2(3): 201–231

    Article  Google Scholar 

  • Bernstein NA (1967) The coordination and regulation of movements. Pergamon, Oxford

    Google Scholar 

  • Biess A, Liebermann DG, Flash T (2007) A computational model for redundant human three-dimensional pointing movements: integration of independent spatial and temporal motor plans simplifies movement dynamics. J Neurosci 27(48): 13045–13064

    Article  Google Scholar 

  • Bottasso CL, Prilutsky BI, Croce A, Imberti E, Sartirana S (2006) A numerical procedure for inferring from experimental data the optimization cost functions using a multibody model of the neuro-musculoskeletal system. Multibody Syst Dynam 16: 123–154

    Article  MATH  MathSciNet  Google Scholar 

  • Cole KJ, Johansson RS (1993) Friction at the digit-object interface scales the sensorimotor transformation for grip responses to pulling loads. Exp Brain Res 95(3): 523–532

    Article  Google Scholar 

  • Collins JJ (1995) The redundant nature of locomotor optimization laws. J Biomech 28(3): 251–267

    Article  Google Scholar 

  • Crowninshield RD, Brand RA (1981) A physiologically based criterion of muscle force prediction in locomotion. J Biomech 14(11): 793–801

    Article  Google Scholar 

  • Cruse H, Wischmeyer E, Brwer M, Brockfeld P, Dress A (1990) On the cost functions for the control of the human arm movement. Biol Cybern 62(6): 519–528

    Article  Google Scholar 

  • Edelman S, Flash T (1987) A model of handwriting. Biol Cybern 57(1–2): 25–36

    Article  Google Scholar 

  • Engelbrecht S (2001) Minimum principles in motor control. J Math Psychol 45(3): 497–542

    Article  MATH  MathSciNet  Google Scholar 

  • Erdemir A, McLean S, Herzog W, van den Bogert AJ (2007) Model-based estimation of muscle forces exerted during movements. Clin Biomech (Bristol, Avon) 22(2): 131–154

    Article  Google Scholar 

  • Flash T, Hogan N (1985) The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci 5(7): 1688–1703

    Google Scholar 

  • Herzog W (1992) Sensitivity of muscle force estimations to changes in muscle input parameters using nonlinear optimization approaches. J Biomech Eng 114(2): 267–268

    Article  Google Scholar 

  • Herzog W, Binding P (1992) Predictions of antagonistic muscular activity using nonlinear optimization. Math Biosci 111(2): 217–229

    Article  MATH  MathSciNet  Google Scholar 

  • Johansson RS, Westling G (1984) Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects. Exp Brain Res 56(3): 550–564

    Article  Google Scholar 

  • Kuo AD, Zajac FE (1993) Human standing posture: multi-joint movement strategies based on biomechanical constraints. Prog Brain Res 97: 349–358

    Article  Google Scholar 

  • Kuzelicki J, Zefran M, Burger H, Bajd T (2005) Synthesis of standing-up trajectories using dynamic optimization. Gait Posture 21(1): 1–11

    Article  Google Scholar 

  • Niu X, Latash ML, Zatsiorsky VM (2007) Prehension synergies in the grasps with complex friction patterns: local versus synergic effects and the template control. J Neurophysiol 98(1): 16–28

    Article  Google Scholar 

  • Pataky TC, Latash ML, Zatsiorsky VM (2004) Prehension synergies during nonvertical grasping, ii: modeling and optimization. Biol Cybern 91(4): 231–242

    Article  MATH  Google Scholar 

  • Pham QC, Hicheur H, Arechavaleta G, Laumond JP, Berthoz A (2007) The formation of trajectories during goal-oriented locomotion in humans. ii. a maximum smoothness model. Eur J Neurosci 26(8): 2391–2403

    Article  Google Scholar 

  • Plamondon R, Alimi AM, Yergeau P, Leclerc F (1993) Modelling velocity profiles of rapid movements: a comparative study. Biol Cybern 69(2): 119–128

    Article  Google Scholar 

  • Prilutsky BI (2000) Coordination of two- and one-joint muscles: functional consequences and implications for motor control. Motor Control 4(1): 1–44

    Google Scholar 

  • Prilutsky BI, Gregory RJ (2000) Analysis of muscle coordination strategies in cycling. IEEE Trans Rehabil Eng 8(3): 362–370

    Article  Google Scholar 

  • Prilutsky BI, Zatsiorsky VM (2002) Optimization-based models of muscle coordination. Exerc Sport Sci Rev 30(1): 32–38

    Article  Google Scholar 

  • Raikova RT, Prilutsky BI (2001) Sensitivity of predicted muscle forces to parameters of the optimization-based human leg model revealed by analytical and numerical analyses. J Biomech 34(10): 1243–1255

    Article  Google Scholar 

  • Redl C, Gfoehler M, Pandy MG (2007) Sensitivity of muscle force estimates to variations in muscle-tendon properties. Hum Mov Sci 26(2): 306–319

    Article  Google Scholar 

  • Shim JK, Latash ML, Zatsiorsky VM (2003) Prehension synergies: trial-to-trial variability and hierarchical organization of stable performance. Exp Brain Res 152(2): 173–184

    Article  Google Scholar 

  • Siemienski A (2006) Direct solution of the inverse optimization problem of load sharing between muscles. J Biomech 39: S45

    Article  Google Scholar 

  • Tsirakos D, Baltzopoulos V, Bartlett R (1997) Inverse optimization: functional and physiological considerations related to the force-sharing problem. Crit Rev Biomed Eng 25(4–5): 371–407

    Google Scholar 

  • Westling G, Johansson RS (1984) Factors influencing the force control during precision grip. Exp Brain Res 53(2): 277–284

    Article  Google Scholar 

  • Zatsiorsky VM, Latash ML (2008) Multifinger prehension: an overview. J Mot Behav 40(5): 446–476

    Article  Google Scholar 

  • Zatsiorsky VM, Gregory RW, Latash ML (2002) Force and torque production in static multifinger prehension: biomechanics and control. ii. control. Biol Cybern 87(1): 40–49

    Article  MATH  Google Scholar 

  • Zatsiorsky VM, Gao F, Latash ML (2003) Prehension synergies: effects of object geometry and prescribed torques. Exp Brain Res 148(1): 77–87

    Article  Google Scholar 

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Correspondence to Alexander V. Terekhov or Vladimir M. Zatsiorsky.

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Terekhov, A.V., Pesin, Y.B., Niu, X. et al. An analytical approach to the problem of inverse optimization with additive objective functions: an application to human prehension. J. Math. Biol. 61, 423–453 (2010). https://doi.org/10.1007/s00285-009-0306-3

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  • DOI: https://doi.org/10.1007/s00285-009-0306-3

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