HaWCoS: the "hands-free" wheelchair control system

ABSTRACT

A system allowing to control an electrically powered wheelchair without using the hands is introduced. HaWCoS -- the "Hands-free" Wheelchair Control System -- relies upon muscle contractions as input signals. The working principle is as follows. The constant stream of EMG signals associated with any arbitrary muscle of the wheelchair driver is monitored and reduced to a stream of contraction events. The reduced stream affects an internal program state which is translated into appropriate commands understood by the wheelchair electronics. The feasibility of the proposed approach is illustrated by a prototypical implementation for a state-of-the-art wheelchair. Operating a HaWCoS-wheelchair requires extremely little effort, which makes the system suitable even for people suffering from very severe physical disabilities.

References

1. C. W. Anderson, S. V. Devulapalli, and E. A. Stolz. EEG as a means of communication: preliminary experiments in EEG analysis using neural networks. In ASSETS '94 -- Proceedings of the First Annual ACM Conference on Assistive Technologies, pages 141--147, 1994.
2. N. Birbaumer, N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. Kübler, J. Perelmouter, E. Taub, and H. Flor. A spelling device for the paralyzed. Nature, 398:297--298, 1999.
3. Z. Bozorgzadeh, G. E. Birch, and S. G. Mason. The LFASD brain computer interface: On-line identification of imagined finger flexions in the spontaneous EEG of able-bodied subjects. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 4, pages 2385--2388, 2000.
4. J. D. Crisman, M. E. Cleary, and J. C. Rojas. The deictically controlled wheelchair. Image and Vision Computing, 16:235--249, 1998.
5. E.-A. Degermann, R. Dahlberg, D. Wallen, E. Björklund, and D. Lundman. Ergonomic and technical evaluation of an eye-controlled computer with 'eyegaze'. Work, 5:213--221, 1995.
6. T. Felzer and B. Freisleben. An input device for human-computer interaction based on muscle control, submitted for publication, 2001.
7. T. Felzer and B. Freisleben. BRAINLINK: A software tool supporting the development of an EEG-based brain-computer interface, submitted for publication, 2001.
8. O. Fukuda, T. Tsuji, and M. Kaneko. Pattern classification of EEG signals using a log-linearized Gaussian mixture neural network. In Proceedings of the IEEE International Conference on Neural Networks, volume 5, pages 2479--2484, 1995.
9. P. R. Kennedy, R. A. E. Bakay, M. M. Moore, K. Adams, and J. Goldwaithe. Direct control of a computer from the human central nervous system. IEEE Transactions On Rehabilitation Engineering, 8(2): 198--202, 2000.
10. A. Kübler, B. Kotchoubey, T. Hinterberger, N. Ghanayim, J. Perelmouter, M. Schauer, C. Fritsch, E. Taub, and N. Birbaumer. The thought translation device: a neurophysiological approach to communication in total motor paralysis. Exp. Brain Res., 124:223--232, 1999.
11. P. Martín, M. Mazo, I. Fernández, J. L. Lázaro, F. J. Rodríguez, and A. Gardel. Multifunctional and autonomous, high performance architecture: application to a wheelchair for disabled people that integrates different control and guidance strategies. Microprocessors and Microsystems, 23:1--6, 1999.
12. M. Mazo, F. J. Rodríguez, J. L. Lázaro, J. Ureña, J. C. García, E. Santiso, P. Revenga, and J. J. García. Electronic control for a wheel-chair guided by oral commands and ultrasonic and infrared sensors. IFAC Artificial Intelligence in Real Time Control, pages 249--254, 1994.
13. M. Mazo, F. J. Rodríguez, J. L. Lázaro, J. Ureña, J. C. García, E. Santiso, and P. A. Revenga. Electronic control of a wheelchair guided by voice commands. Control Eng. Practice, 3(5):665--674, 1995.
14. K. S. Park and K. T. Lee. Eye-controlled human/computer interface using the line-of-sight and the intentional blink. Computers & Industrial Engineering, 30(3):463--473, 1996.
15. J. Perelmouter and N. Birbaumer. A binary spelling interface with random errors. IEEE Transactions On Rehabilitation Engineering, 8(2):227--232, 2000.
16. M. Polak and A. Kostov. Training setup for control of neural prosthesis using brain-computer interface. In Proceedings of The First Joint BMES/EMBS Conference: Serving Humanity, Advancing Technology, page 446, 1999.
17. Th. Röfer and A. Lankenau. Architecture and applications of the Bremen autonomous wheelchair. Information Sciences, 126:1--20, 2000.
18. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error propagation. In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing: Explorations in the Microstructures of Cognition, volume 1, pages 318--362, Cambridge, MA, 1986. MIT Press.
19. J. J. Tecce, J. Gips, C. P. Olivieri, L. J. Pok, and M. R. Consiglio. Eye movement control of computer functions. International Journal of Psychophysiology, 29:319--325, 1998.
20. J. R. Wolpaw, N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, G. Schalk, E. Donchin, L. A. Quatrano, C. J. Robinson, and T. M. Vaughan. Brain-computer interface technology: A review of the first international meeting. IEEE Transactions On Rehabilitation Engineering, 8(2): 164--173, 2000.
21. J. R. Wolpaw, D. J. McFarland, and T. M. Vaughan. Brain-computer interface research at the Wadsworth Center. IEEE Transactions On Rehabilitation Engineering, 8(2):222--226, 2000.
22. W. Zhang, V. G. Duffy, R. Linn, and A. Luximon. Voice recognition based human-computer interface design. Computers & Industrial Engineering, 37:305--308, 1999.

Index Terms

• Applied computing

  • Operations research

    • Consumer products

• Human-centered computing

  • Accessibility

  • Human computer interaction (HCI)

    • Empirical studies in HCI

• Social and professional topics

  • Professional topics

    • Computing profession

      • Assistive technologies

  • User characteristics

    • People with disabilities