A Reconfigurable Embedded Input Device for Kinetically Challenged Persons

  • Apostolos Dollas
  • Kyprianos Papademetriou
  • Nikolaos Aslanides
  • Tom Kean
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2147)


A new input device for kinetically challenged persons has been developed. This device is based on solid-state accelerometers to sense motion in space, a microcontroller to sample the data in real time, and an embedded FPGA to distinguish types of motion from programmable lists of motions. The FPGA computational model for the first version, presented in this paper, is an implementation of finite state machines (FSM) running in parallel, one for each type of motion which is detected by the system. The design is modular, allowing for different lists of motions and/or thresholds on input data to be incorporated with reconfiguration of the FPGA. A personal computer is used to determine the appropriate settings for each motion, which are then converted to FSM. The architecture of the system, types of motions it detects, and its performance characteristics are presented in this work.


Finite State Machine Digital Signal Processor Dynamic Time Warping Input Device Dynamic Time Warping Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Lau C, O’ Leary S. “Comparison of computer input devices for persons with severe physical disabilities”, Am.J.Occup. Ther. Nov. 1993, 47:11.Google Scholar
  2. 2.
    Hawley M.S. et al. “Wheelchair mounted integrated control systems for multiply handicapped people”, J. Biomed Eng. 1992 May, 14:3.Google Scholar
  3. 3.
    Luttgens K. et al Kinesiology-Scientific Basis of Human Motion” 8th Ed.,Brown and Benchmark Publishers, 1992, ISBN 0-697-11632-8.Google Scholar
  4. 4.
    Armando B. Barreto, Scott D. Scargle, Malek Adjouadi, “A practical EMG-based human-computer interface for users with motor disabilities”, Journal of Rehabilitation Research and Development Vol. 37 No. 1, January/February 2000Google Scholar
  5. 5.
    C. Verplaetse “Inertial proprioceptive devices: Self-motion-sensing toys and tools”, IBM Systems Journal, Vol 35, NOS 3&4, 1996.Google Scholar
  6. 6.
    T. Oates; M. D. Schmill and P. R. Cohen, “Identifying Qualitatively Different Outcomes of Actions: Gaining Autonomy Through Learning”. In Proceedings of the Fourth International Conference on Autonomous Agents, pages 110–111, 2000.Google Scholar
  7. 7.
    Jim Broyles, Nicolas Karlsson, Rob Swanson, and Eduardo Velarde, “The Signature Verification System”, January 1997, news/issue6/2.html.
  8. 8.
    Jean-Louis, Racine Jeremy Risner, “Air Drummer”,
  9. 9.
    Kayser-Threde GMBH, 3D Eye Tracking Device, Technical Description, November 2000, Munchen.Google Scholar
  10. 10.
    S. Scalera, M. Falco, and B. Nelson, “A Reconfigurable Computing Architecture for Microsensors”, In Proceedings of the FCCM April 2000, pages 59–67Google Scholar
  11. 11.
    A. Kaufman, F. Dachille, B. Chen, I. Bitter, K. Kreeger, N. Zhang, and Q. Tang, “Real-Time Volume Rendering”, Special Issue on 3D Imaging of the International of Imaging Systems and Technology, 2000, pp.Google Scholar
  12. 12.
    C. Verplaetse, “Can A Pen Remember What It Has Written Using Inertial Navigation ?:AnEvaluation Of Current Accelerometer Technology”, part of a class project for “Physics and Media” class,

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Apostolos Dollas
    • 1
  • Kyprianos Papademetriou
    • 1
  • Nikolaos Aslanides
    • 1
  • Tom Kean
    • 2
  1. 1.Dept. of Electronic and Comp. Eng.Technical University of CreteChania, CreteGreece
  2. 2.Algotronix Ltd.ScotlandUK

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