Advertisement

A Study of Feasibility of a Human Finger Exoskeleton

  • Daniele Cafolla
  • Giuseppe Carbone
Part of the Studies in Computational Intelligence book series (SCI, volume 544)

Abstract

Finger impairment following stroke results in significant deficit in hand manipulation and the performance of everyday tasks. Recent advances in rehabilitation robotics have shown improvement in efficacy of rehabilitation. Current devices, however, lack the capacity to accurately interface with the human finger at levels of velocity and torque comparable to the performance of everyday hand manipulation tasks. This paper tries to fill this need with a newly designed system intended to aid in hand rehabilitation. A 3D CAD model and simulations have been developed for verifying the engineering feasibility.

Keywords

robotics hands exoskeleton hand rehabilitation robot services 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    AA.VV. The Merck manual of diagnosis and therapy. In: Berkow, R. (ed.) Merck Research Laboratories, Rahway, N.J., XVI ed. (1992)Google Scholar
  2. 2.
    Stroke Statistic: 2009 Update. Centennial, CO: National Stroke Association (2009)Google Scholar
  3. 3.
    Kwakkel, G., Kollen, B.J., Van der Grond, J., Prevo, A.J.: Probability of regaining dexterity in the flaccid upper limb. The Impact of Severity of Paresis and Time Since Onset in Acute Stroke, Stroke 34, 2181–2186 (2003)Google Scholar
  4. 4.
    Carbone, G., 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, 2181–2186 (2003)Google Scholar
  5. 5.
    Ceccarelli, M.: Experimental Tests on Feasible Operation of a Finger Mechanism in the LARM Hand. Mechanics Based Design of Structures and Machines An International Journal 36(1), 1–13 (2008)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Heart Disease and Stroke Statistics: Update, Dallas, Tex: American Heart Association (2005)Google Scholar
  7. 7.
    Duncan, P.W., Bode, R.K., Min Lai, S., Perera, S.: Rasch analysis of a new stroke specific outcome scale: the Stroke Impact Scale. Arch. Phys. Med. Rehabil. 84(7), 950–963 (2003)CrossRefGoogle Scholar
  8. 8.
    Carey, J.R., Durfee, W.K., Bhatt, E., Nagpal, A., Weinstein, S.A., Anderson, K.M., Lewis, S.M.: Comparison of finger tracking versus simple movement training via telerehabilitation to alter hand function and cortical reorganization after stroke. Neurorehabil Neural Repair 21(3), 216–232 (2007)CrossRefGoogle Scholar
  9. 9.
    Carey, J.R., Kimberley, T.J., Lewis, S.M., Auerbach, E.J., Dorsey, L., Rundquist, P., Ugurbil, K.: Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain 125(pt 4), 773–788 (2002)CrossRefGoogle Scholar
  10. 10.
    Hesse, S., Schulte-Tigges, G., Konrad, M., Bardeleben, A., Werner, C.: Robot assisted arm trainer for the passive and active practice of bilateral forearm and wrist movements in hemiparetic subjects. Arch. Phys. Med. Rehabil. 84(6), 915–920 (2003)CrossRefGoogle Scholar
  11. 11.
    Kamper, D.G., Harvey, R.L., Suresh, S., Rymer, W.Z.: Relative contributions of neural mechanisms versus muscle mechanics in promoting finger extension deficits following stroke. Muscle Nerve 28(3), 309–318 (2003)Google Scholar
  12. 12.
    Dovat, L., Lambercy, O., Salman, B., Johnson, V., Milner, T., Gassert, R., Burdet, E., Leong, T.C.: A technique to train finger coordination and independence after stroke. Disabil. Rehabil. Assist. Technol. 5(4), 279–287 (2010)CrossRefGoogle Scholar
  13. 13.
    Krebs, H.I., Hogan, N., Volpe, B.T., Aisen, M.L., Edelstein, L., Diels, C.: Overview of clinical trials with MIT-MANUS: a robot-aided neuro-rehabilitation facility. Technol. Health Care 7(6), 419–423 (1999)Google Scholar
  14. 14.
    Lum, P.S., Burgar Reinkensmeyer, D.J., Kahn, L.E., Averbuch, M., McKenna-Cole, A., Schmit, B.D., Rymer, W.Z.: Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM guide. J. Rehabil. Res. Dev. 37(6), 653–662 (2000)Google Scholar
  15. 15.
    Jones, C.L., Wang, F., Osswald, C., Kang, X., Sarkar, N., Kamper, D.G.: Control and Kinematic Performance Analysis of an Actuated Finger Exoskeleton for Hand Rehabilitation following Stroke. In: Proceedings of the 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, September 26-29. The University of Tokyo, Tokyo (2010)Google Scholar
  16. 16.
    Takahashi, C., Der-Yeghiaian, L., Le, V., Cramer, S.C.: A robotic device for hand motor therapy after stroke. In: Proceedings of IEEE 9th International Conference on Rehabilitation Robotics: Frontiers of the Human-Machine Interface Chicago, Illinois, pp. 17–20 (2005)Google Scholar
  17. 17.
    Shor, P.C.: Evidence for strength imbalances as a significant contributor to abnormal synergies in hemiparetic subjects. Muscle Nerve 27(2), 211–221 (2003)CrossRefMathSciNetGoogle Scholar
  18. 18.
    Ceccarelli, M., Carbone, G.: Design of LARM Hand: problems and solutions. In: IEEE-TTTC International Conference on Automation, Quality and Testing, Robotics, AQTR 2008, Cluj-Napoca, pp. 298–303 (2008); (best paper award): in Journal of Control Engineering and Applied Informatics 10(2), 39-46 (2008)Google Scholar
  19. 19.
    Ceccarelli, M., Nava Rodriguez, N.E., Carbone, G.: Optimal Design of Driving Mechanism in a 1-d.o.f. Anthropomorphic Finger. In: International Workshop on Computational Kinematics, paper 03CK 2005, Cassino (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.LARM: Laboratory of Robotics and Mechatronics – DICEMUniversity of Cassino and South LatiumCassinoItaly

Personalised recommendations