Sensitivity Analysis of a Parametric Hand Exoskeleton Designed to Match Natural Human Grasping Motion

  • Thomas M. W. Burton
  • Ravi Vaidyanathan
  • Stuart C. Burgess
  • A. J. Turton
  • Chris Melhuish
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7429)

Abstract

This paper describes the simulated analysis of a fully scalable, parametrically designed hand exoskeleton previously developed as part of a stroke rehabilitation program within the Bristol Robotics Laboratory. The device is parametrically designed to match the location and trajectories of the joints within a normal healthy human hand. However, testing of fully scalable designs which can be custom fit to a person using parametric design can be costly, time consuming and potentially hazardous if ill-fitting. Here a method is presented which allows for the performance of a parametric design to be tested. A virtual mechanism with induced manufacturing tolerances is modelled and its interactions with the hand are simulated. The performance can then be assessed by the devices ability to achieve the objective trajectory within the simulation. The results show that for the designed hand exoskeleton, with a manufacturing tolerance of 0.2mm across parts the resulting average trajectory error is less than 0.2 degrees with an average tip error of less than 0.5 mm. The results also demonstrate that for a large tolerance of 1mm across all dimensions, the trajectory error can reach as high as 30.9 degrees. This result justifies the use of parametric design to develop mechanisms matching natural human motion. While the results are for a parametrically scalable hand exoskeleton, it is believed the methodology is applicable to any bio-compatible assistive device.

Keywords

Hand Exoskeleton Sensitivity Analysis Assistive Robotics Rehabilitation 

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References

  1. 1.
    M. R. Laboratories. Merck manual geriatrics (1997), http://www.merck.com/mrkshared/mmg/home.jsp
  2. 2.
    Tonkin, R.: Stroke statistics (2007), http://www.stroke.org
  3. 3.
    Kwakkel, G., Kollen, B.J., van der Grond, J., Prevo, A.J.: Probability of regaining dexterity in the flaccid upper limb: Impact of severity of paresis and time since onset in acute stroke. Stroke 34(9), 2181–2186 (2003)CrossRefGoogle Scholar
  4. 4.
    Khaw, J.T.: Epidemiology of stroke. Journal of Neurology, Neurosurgery, and Psychiatry 61(4), 333–338 (1996)CrossRefGoogle Scholar
  5. 5.
    Wolf, P.A., Grotta, J.C.: Cerebrovascular disease. Circulation 102(90004), IV–75–IV–80 (2000)Google Scholar
  6. 6.
    Wade, D.: National clinical guidelines for stroke: London royal college of physicians (2000), www.rcplondon.ac.uk/resources/stroke-guidelines
  7. 7.
    Napier, J.R.: The prehensile movements of the human hand. The Journal of Bone and Joint Surgery 38B(4), 902–913 (1956)Google Scholar
  8. 8.
    Landsmeer, J.M.F.: Power grip and precision handling. Annals of the Rheumatic Diseases 21(2), 164–170 (1962)CrossRefGoogle Scholar
  9. 9.
    Nordin, M., Frankel, V.H.: Basic biomechanics of the musculoskeletal system. Lippincott Williams I& Wilkins (2001)Google Scholar
  10. 10.
    Wing, A.M., Fraser, C.: The contribution of the thumb to reaching movements. The Quarterly Journal of Experimental Psychology 35, 297–309 (1983)CrossRefGoogle Scholar
  11. 11.
    Seo, N., Rymer, W., Kamper, D.: Altered digit force direction during pinch grip following stroke. Experimental Brain Research 202, 891–901 (2010)CrossRefGoogle Scholar
  12. 12.
    Lang, C.E., DeJong, S.L., Beebe, J.A.: Recovery of thumb and finger extension and its relation to grasp performance after stroke. Journal of Neurophysiology 102(1), 451–459 (2009)CrossRefGoogle Scholar
  13. 13.
    Carr, J.H., Shepherd, R.B.: A Motor Relearning Programme for Stroke. Aspen Publishers (1987)Google Scholar
  14. 14.
    Woldag, H., Hummelsheim, H.: Evidence-based physiotherapeutic concepts for improving arm and hand function in stroke patients. Journal of Neurology 249, 518–528 (2002)CrossRefGoogle Scholar
  15. 15.
    Takahashi, C., Der-yeghiaian, L., Le, V., Motiwala, R., Cramer, S.: Robot-based hand motor therapy after stroke. Brain 131(2), 425–437 (2008)CrossRefGoogle Scholar
  16. 16.
    Kwakkel, G., Kollen, B.J., Krebs, H.I.: Effects of robot-assisted therapy on upper limb recovery after stroke: A systematic review. Neurorehabilitation and Neural Repair 22(2), 111–121 (2008)CrossRefGoogle Scholar
  17. 17.
    Krebs, H., Hogan, N., Aisen, M.L., Volpe, B.: Robot-aided neurorehabilitation. IEEE Transactions on Rehabilitation Engineering 6(1), 75–87 (1998)CrossRefGoogle Scholar
  18. 18.
    Salman, B., Vahdat, S., Lambercy, O., Dovat, L., Burdet, E., Milner, T.: Changes in muscle activation patterns following robot-assisted training of hand function after stroke. In: International Conference on Intelligent Robots and Systems, pp. 5145–5150 (October 2010)Google Scholar
  19. 19.
    Bouzit, M., Burdea, G., Popescu, G., Boian, R.: The rutgers master ii - new design force-feedback glove. IEEE/ASME Transactions on Mechatronics 7(2), 256–263 (2002)CrossRefGoogle Scholar
  20. 20.
    Stergiopoulos, P., Fuchs, P., Laurgeau, C.: Design of a 2-finger hand exoskeleton for vr grasping simulation. In: EuroHaptics 2003 (2003)Google Scholar
  21. 21.
    Dicicco, M., Lucas, L., Matsuoka, Y.: Comparison of two control strategies for a muscle controlled orthotic exoskeleton for the hand. In: IEEE International Conference on Robotics and Automation, pp. 1622–1627 (2004)Google Scholar
  22. 22.
    Mulas, M., Folgheraiter, M., Gini, G.: An emg-controlled exoskeleton for hand rehabilitation. In: International Conference on Rehabilitation Robotics, pp. 371–374 (2005)Google Scholar
  23. 23.
    Wege, A., Kondak, K., Hommel, G.: Mechanical design and motion control of a hand exoskeleton for rehabilitation. In: IEEE ICMA (2005)Google Scholar
  24. 24.
    Worsnopp, T., Peshkin, M., Colgate, J., Kamper, D.: Actuated finger exoskeleton for hand rehabilitation following. In: International Conference on Rehabilitation Robotics, pp. 896–901 (2007)Google Scholar
  25. 25.
    Fu, Y., Wang, P., Wang, S., Liu, H., Zhang, F.: Design and development of a portable exoskeleton based CPM machine for rehabilitation of hand injuries. In: International Conference on Robotics and Biomimetics, pp. 1476–1481 (2007)Google Scholar
  26. 26.
    Chiri, A., Giovacchini, F., Vitiello, N., Cattin, E., Roccella, S., Vecchi, F., Carrozza, M.: Handexos: Towards an exoskeleton device for the rehabilitation of the hand. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1106–1111 (October 2009)Google Scholar
  27. 27.
    Mohamaddan, S., Komeda, T.: Wire-driven mechanism for finger rehabilitation device, pp. 1015–1018 (2010)Google Scholar
  28. 28.
    Tong, K., Ho, S., Pang, P., Hu, X., Tam, W., Fung, K., Wei, X., Chen, P., Chen, M.: An intention driven hand functions task training robotic system. In: IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3406–3409 (September 2010)Google Scholar
  29. 29.
    de Araujo, R.C., Junior, F.L., Rocha, D.N., Sono, T.S., Pinotti, M.: Effects of intensive arm training with an electromechanical orthosis in chronic stroke patients: A preliminary study. Achives of Medical Rehabilitation 92, 1746–1753 (2011)CrossRefGoogle Scholar
  30. 30.
    Marvin, C.: Portable hand cpm w/soft splint 8091 (2007)Google Scholar
  31. 31.
    Takahashi, C., Le, V., Cramer, S.: A robotic device for hand motor therapy after stroke. Journal of Neurology, 17–20 (2005)Google Scholar
  32. 32.
    Burton, T., Vaidyanathan, R., Burgess, S., Turton, A., Melhuish, C.: A parameterized kinematic model of the human hand. In: Towards Autonomous Robotic Systems (TAROS), Plymouth, pp. 34–40 (September 2010)Google Scholar
  33. 33.
    Burton, T.M.W., Vaidyanathan, R., Burgess, S., Turton, A.J., Melhuish, C.: Development of a parametric kinematic model of the human hand and a novel robotic exoskeleton. In: IEEE International Conference on Rehabilitation Robotics (ICORR), Zurich (June 2011)Google Scholar
  34. 34.
    Pheasant, S., Haslegrave, C.M.: Bodyspace: Anthropometry, Ergonomics And The Design Of Work. Taylor I& Francis (October 2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Thomas M. W. Burton
    • 1
    • 2
  • Ravi Vaidyanathan
    • 3
    • 4
  • Stuart C. Burgess
    • 5
  • A. J. Turton
    • 1
    • 2
  • Chris Melhuish
    • 1
    • 2
  1. 1.Bristol Robotics LaboratoryUniversity of BristolUK
  2. 2.The University of the West of EnglandUK
  3. 3.Department of Mechanical EngineeringImperial CollegeLondonUK
  4. 4.The Department of Systems EngineeringThe US Naval Postgraduate SchoolMontereyUSA
  5. 5.Department of Mechanical EngineeringUniversity of BristolUK

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