Artificial Life and Robotics

, Volume 4, Issue 4, pp 227–232 | Cite as

Assistant force compensation for hand movement of patients by using exogenous signals and/or neurophysiological signals

Invited Article
Received:
Accepted:
  • 40 Downloads

Abstract

Because functional diseases of the brain can cause disabilities related to human movement control, a compensation method was developed for improving the performance of hand movements. The compensation for human hand movements can be carried out by adding an assistant force that is generated from artificial equipment attached to a human arm. From the experiment on visual target tracking, it was found that the tracking trajectory was adequately represented by a dynamic model of the motion of an articulated industrial robot arm, and the different abilities for movement control among healthy people and patients were classified by different model parameters as position loop gain, velocity loop gain, and response delay. Dynamic force compensation was approached by considering the different control features of the patients. The effectiveness of the proposed compensation method was verified in a simulation study on an actual industrial robot arm. A human-machine interface, e.g., a brain-computer interface (BCI), for realizing the control of artificial equipment to compensate for human hand movements is also presented and discussed.

Key words

Hand movement disability Compensation for hand movement control Visual target tracking Industrial articulated robot arm Equipment for generating assistant force 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yahagi S, Kasai K (1998) Facilitation of motor-evoked potentials in first forsal interosseous muscle is dependent on different motor images. Electroencephalogr Clin Neurophysiol 109:409–417CrossRefGoogle Scholar
  2. 2.
    Deecke L, Kornhuber HG, Schmitt G (1977) Cerebral potentials preceding voluntary movement in patients with bilateral or unilateral Parkinson akinesia. In: Desmedt JE (eds) Progress clinical neurophysiology, vol 1. Attention, voluntary contraction and event-related cerebral potentials. Karger, Basel, 151–163Google Scholar
  3. 3.
    Dick JPR, Cantello R, Buruma O, et al. (1987) The bereitschaftspotential, L-DOPA and Parkinson's disease. Electroencephalogr clin Neurophysiol 66:263–274CrossRefGoogle Scholar
  4. 4.
    Jacobsen SC, Knutti DF, Johnson RT, et al. (1982) Development of the Utah artificial arm. IEEE Trans Biomed Eng 29:249–269Google Scholar
  5. 5.
    Nakamura M, Nishida S, Mutoh Y, et al. (1990) A recording and processing method for manual tracking of two-dimensional visual targets (in Japanese). Jpn Biol Med Eng, 28:9–17Google Scholar
  6. 6.
    Munasinghe SR, Nakamura M, Goto S, et al. (1999) High-speed precise control of robot arms with assigned speed under torque constraint by trajectory generation in joint coordinates. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics Tokyo, 1999Google Scholar
  7. 7.
    Gruver WA (1994) Intelligent robotics in manufactory, service and rehabilitation: an overview. IEEE Trans Biomed Eng 41:4–11Google Scholar
  8. 8.
    Karniel A, Inbar GF (2000) Human motor control: learning to control a time-varying, nonlinear, many-to-one system. IEEE Trans Syst Man Cybern 30:1–12Google Scholar
  9. 9.
    Wiener N (1961) Cybernetics, 2nd ed. MIT, Cambridge: Wiley, New YorkGoogle Scholar
  10. 10.
    Kelly MF, Parker PA, Scott RN (1990) The application of neural networks to myoelectric signal analysis: a preliminary study. IEEE Trans Biomed Eng 37:221–240CrossRefGoogle Scholar
  11. 11.
    O'Neill PA, Morin EL, Scott RN (1994) Myoelectric signal characteristics from muscles in residual upper limbs. IEEE Trans Rehabil Eng 2:266–270CrossRefGoogle Scholar
  12. 12.
    Gerloff C, Venishi N, Nagamine T, et al. (1998) Cortical activation during fast repetitive finger movements in humans: steady-state movement-related magnetic fields and their cortical generators. Electroencephalogr clin Neurophysiol 109:444–453CrossRefGoogle Scholar
  13. 13.
    Wolpaw JR, Birbaumer N, Heetderks, et al. (2000) Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8:164–173CrossRefGoogle Scholar
  14. 14.
    Kostov A, Polak M (2000) Parallel man-machine training in development of EEG-based cursor control. IEEE Trans Rehabil Eng 8:203–205CrossRefGoogle Scholar

Copyright information

© ISAROB 2000

Authors and Affiliations

  1. 1.Department of Advanced Systems Control EngineeringSaga UniversitySagaJapan
  2. 2.Department of Neurology and Human Brain Research CenterKyoto University Graduate School of MedicineKyotoJapan

Personalised recommendations