Experimental Brain Research

, Volume 230, Issue 2, pp 251–260 | Cite as

A model of motor performance during surface penetration: from physics to voluntary control

  • Roberta L. Klatzky
  • Pnina Gershon
  • Vikas Shivaprabhu
  • Randy Lee
  • Bing Wu
  • George Stetten
  • Robert H. Swendsen
Research Article


The act of puncturing a surface with a hand-held tool is a ubiquitous but complex motor behavior that requires precise force control to avoid potentially severe consequences. We present a detailed model of puncture over a time course of approximately 1,000 ms, which is fit to kinematic data from individual punctures, obtained via a simulation with high-fidelity force feedback. The model describes puncture as proceeding from purely physically determined interactions between the surface and tool, through decline of force due to biomechanical viscosity, to cortically mediated voluntary control. When fit to the data, it yields parameters for the inertial mass of the tool/person coupling, time characteristic of force decline, onset of active braking, stopping time and distance, and late oscillatory behavior, all of which the analysis relates to physical variables manipulated in the simulation. While the present data characterize distinct phases of motor performance in a group of healthy young adults, the approach could potentially be extended to quantify the performance of individuals from other populations, e.g., with sensory–motor impairments. Applications to surgical force control devices are also considered.


Haptic Motor Model Force control Physics Biomechanics Oscillation Application 


  1. Aron AR, Poldrack RA (2006) Cortical and subcortical contributions to stop signal response inhibition: role of the subthalamic nucleus. J Neurosci 26:424–2433CrossRefGoogle Scholar
  2. Chambers CD, Bellgrove MA, Stokes MG, Henderson TR, Garavan H, Robertson IH, Morris AP, Mattingley JB (2006) Executive “brake failure” following deactivation of human frontal lobe. J Cogn Neurosci 18:444–455PubMedGoogle Scholar
  3. Di Domizio J, Keir PJ (2010) Forearm posture and grip effects during push and pull tasks. Ergonomics 53:336–343PubMedCrossRefGoogle Scholar
  4. Feldman AG, Levin MF (2009) The equilibrium-point hypothesis—past, present, and future. In: Sternad D (ed) Progress in motor control. Springer, New York, pp 699–726CrossRefGoogle Scholar
  5. Fitts PM (1954) The information capacity of the human motor system in controlling the amplitude of movement. J Exp Psychol 47:381–391PubMedCrossRefGoogle Scholar
  6. Fleming I, Balicki M, Koo J, Iordachita I, Mitchell B, Handa J, Hager G, Taylor R (2008) Cooperative robot assistant for retinal microsurgery. In: MICCAI 2008, Part II, LNCS, vol 5242, pp 543–550Google Scholar
  7. Franklin DW, Wolpert DM (2011) Computational mechanisms of sensorimotor control. Neuron 72:425–442PubMedCrossRefGoogle Scholar
  8. Fu MJ, Çavuşoğlu MC (2012) Human arm-and-hand dynamics model with variability analyses for a stylus-based haptic interface. IEEE Trans Syst Man Cybern Part B 42:1633–1644CrossRefGoogle Scholar
  9. Fung YC (1967) Elasticity of soft tissues in simple elongation. Am J Physiol 213:1532–1544PubMedGoogle Scholar
  10. Gupta PK, Jensen PS, de Juan E Jr (1999) Surgical forces and tactile perception during retinal microsurgery. Medical image computing and computer-assisted intervention. In: Lecture notes in computer science, vol 1679, pp 1218–1225Google Scholar
  11. Hollis RL, Salcudean SE (1993) Lorentz levitation technology: a new approach to fine motion robotics, teleoperation, haptic interfaces, and vibration isolation. In: Proceedings of 5th international symposium on robotics research, Hidden Valley, PA, 1–4 Oct 1993Google Scholar
  12. Jagtap AD, Riviere CN (2004) Applied force during vitreoretinal microsurgery with handheld instruments. In: Proceedings of the 26th annual international conference of the IEEE engineering in Medicine and Biology Society, San Francisco, CA, USA, 1–5 Sept 2004Google Scholar
  13. Jaric S, Gottlieb GL, Latash ML, Corcos DM (1998) Changes in the symmetry of rapid movements: effects of velocity and viscosity. Exp Brain Res 120:52–60PubMedCrossRefGoogle Scholar
  14. Jeannerod M (1984) The timing of natural prehension movements. J Motor Behav 16:235–254CrossRefGoogle Scholar
  15. Johansson RS, Flanagan JR (2009) Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat Rev Neurosci 10:345–359PubMedCrossRefGoogle Scholar
  16. Kisiel-Sajewica K, Jaskolski A, Jaskolska A (2005) Current knowledge in studies on relaxation from voluntary contraction. Hum Mov 6:136–148Google Scholar
  17. Lacquaniti F, Terzuolo C, Viviani P (1983) The law relating the kinematic and figural aspects of drawing movements. Acta Psychol 54:115–130CrossRefGoogle Scholar
  18. Lipps DB, Galecki AT, Ashton-Miller JA (2011) On the implications of a sex difference in the reaction times of sprinters at the Beijing Olympics. PLoS ONE 6:e26141. doi:101371/journalpone0026141 PubMedCrossRefGoogle Scholar
  19. Meyer DE, Abrams RA, Kornblum S, Wright CE, Smith JEK (1988) Optimality in human motor performance: ideal control of rapid aimed movements. Psychol Rev 95:340–370PubMedCrossRefGoogle Scholar
  20. Milanovic S, Blesic S, Jaric S (2000) Changes in movement variables associated with transient overshoot of the final position. J Motor Behav 32:115–120CrossRefGoogle Scholar
  21. Milner-Brown HS, Stein RB, Yemm R (1973) The contractile properties of human motor units during voluntary isometric contractions. J Physiol 228:285–306PubMedGoogle Scholar
  22. Mussa-Ivaldi FA, Hogan N, Bizzi E (1985) Neural, mechanical and geometric factors subserving arm in humans. J Neurosci 5:2732–2743PubMedGoogle Scholar
  23. Nambu A, Tokuno H, Takada M (2002) Functional significance of the cortico-subthalamo-pallidal ‘hyperdirect’ pathway. Neurosci Res 43:111–117PubMedCrossRefGoogle Scholar
  24. Neubert FX, Mars RB, Buch ER, Olivier E, Rushworth MF (2010) Cortical and subcortical interactions during action reprogramming and their related white matter pathways. Proc Natl Acad Sci 107:13240–13245PubMedCrossRefGoogle Scholar
  25. Prattichizzo D, Pacchierotti C, Rosatti G (2012) Cutaneous force feedback as a sensory subtraction technique in haptics. IEEE Trans Haptics 5:289–300CrossRefGoogle Scholar
  26. Rosenbaum DA, Loukopoulos LD, Meulenbroek RGJ, Vaughan J, Engelbrecht SE (1995) Planning reaches by evaluating stored postures. Psychol Rev 102:28–67PubMedCrossRefGoogle Scholar
  27. Salcudean SE, Yan J (1994) Towards a force-reflecting motion-scaling system for micro-surgery. In: Proceedings of IEEE international conference on robotics and automation, San Diego, CA, vol 3, pp 2296–2301Google Scholar
  28. Sarver JJ, Robinson PS, Elliott DM (2003) Methods for quasi-linear viscoelastic modeling of soft tissue: application to incremental stress-relaxation experiments. Trans ASME 125:754–758Google Scholar
  29. Shadmehr R, Krakauer JW (2008) A computational neuroanatomy for motor control. Exp Brain Res 185:359–381PubMedCrossRefGoogle Scholar
  30. Sommer MA, Wurtz RH (2008) Brain circuits for the internal monitoring of movements. Annu Rev Neurosci 31:317–338PubMedCrossRefGoogle Scholar
  31. Stetten G, Wu B, Klatzky R, Galeotti J, Siegel M, Lee R, Hollis R (2011) Hand-held force magnifier for surgical instruments. In: Taylor R (ed) Lecture notes in computer science, vol 6689, pp 90–100Google Scholar
  32. Taylor R, Bames A, Kumar R, Gupta P, Wang Z, Jensen P, Whitcomb L, deJuan E, Stoianovici D, Kavoussi L (1999) A steady-hand robotic system for microsurgical augmentation. In: Delp SL, DiGoia M, Jaramaz B (eds) Lecture notes in computer science, vol 1679, pp 1031–1041Google Scholar
  33. Tee KP, Burdet E, Chew CM, Milner TE (2004) A model of force and impedance in human arm movements stiffness is linearly related to the magnitude of the joint torque and increased to compensate for environment instability. Biol Cybern 90:368–375PubMedCrossRefGoogle Scholar
  34. Tsuji T, Takeda Y, Tanaka Y (2004) Analysis of mechanical impedance in human arm movements using a virtual tennis system. Biol Cybern 91:295–305PubMedCrossRefGoogle Scholar
  35. Weiss EJ, Flanders M (2011) Somatosensory comparison during haptic tracing. Cereb Cortex 21:425–434PubMedCrossRefGoogle Scholar
  36. Woodworth RS, Schlossberg H (1960) Experimental psychology: revised edition. Henry Holt, New YorkGoogle Scholar
  37. Zandbelt BB, Bloemendaal M, Hoogendam JM, Kahn RS, Vink M (2013) Transcranial magnetic stimulation and functional MRI reveal cortical and subcortical interactions during stop-signal response inhibition. J Cogn Neurosci 25:157–174PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Roberta L. Klatzky
    • 1
  • Pnina Gershon
    • 1
  • Vikas Shivaprabhu
    • 2
  • Randy Lee
    • 3
  • Bing Wu
    • 4
  • George Stetten
    • 3
  • Robert H. Swendsen
    • 5
  1. 1.Department of PsychologyCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of BioengineeringUniversity of PittsburghPittsburghUSA
  3. 3.Department of BioengineeringCarnegie Mellon UniversityPittsburghUSA
  4. 4.Technological Entrepreneurship and Innovation ManagementArizona State UniversityPhoenixUSA
  5. 5.Department of PhysicsCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations