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Automatic gesture recognition for wheelchair control

Published: 25 September 2017 Publication History

Abstract

In this work we propose an approach for controlling a wheel chair using Motion Capture, particularly gestures from the face are considered as commands for the basic control operations required for driving a wheelchair. Gesture recognition is carried out training an Artificial Neural Network, which is one of the most successful classifier in Pattern Recognition. Based on our experimentation, the proposed approach was able to detect gestures related to order commands which is useful for controlling a wheelchair by users with restricted mobility or disability in legs, arms or hands.

References

[1]
Kawarazaki, N., and Yamaoka, M. 2014. Face tracking control system for wheelchair with depth image sensor. In Proceedings of the 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, 781--786.
[2]
Oppenheim, M. 2016. HeadBanger: controlling switchable software with head gesture. Journal of Assistive Technologies10,1 (2016), 2--10.
[3]
Halawani, A., Ur Réhman, S., Li, H., and Anani, A. 2012. Active vision for controlling an electric wheelchair. Intelligent Service Robotics 5, 2(2012), 89--98.
[4]
Schwesinger, D., Shariati, A., Montella, C., and Spletzer, J. 2016. A smart wheelchair ecosystem for autonomous navigation in urban environments. Autonomous Robots, (2016), 1--20.
[5]
Viola, P., and Jones, M. 2001. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference Computer Vision and Pattern Recognition (CVPR 2001). IEEE, 511--518.
[6]
Barron, J. L., Fleet, D. J., and Beauchemin, S. S. 1994. Performance of optical flow techniques. International journal of computer vision 12, 1(1994), 43--77.
[7]
Cootes, T. F., Edwards, G. J., and Taylor, C. J. 2001. Active appearance models. IEEE Transactions on pattern analysis and machine intelligence, 23, 6(2011), 681--685.
[8]
Mitchell, Tom M. 1997. Machine Learning. Mc-Graw Hill
[9]
Bradski, G., and Kaehler, A. 2008. Learning OpenCV: Computer vision with the OpenCV library. O'Reilly Media, Inc.
[10]
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. 2009. The WEKA data mining software: an update. ACM SIGKDD explorations newsletter 11, 1(2009), 10--18.
[11]
Milborrow, S., Morkel, J., and Nicolls, F. 2010. The MUCT landmarked face database. Pattern Recognition Association of South Africa.
[12]
Ekman, P., and Rosenberg, E. L. 1997. What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS). Oxford University Press.
[13]
Goodall, C. 1991. Procrustes methods in the statistical analysis of shape.

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  1. Automatic gesture recognition for wheelchair control

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    Interacción '17: Proceedings of the XVIII International Conference on Human Computer Interaction
    September 2017
    268 pages
    ISBN:9781450352291
    DOI:10.1145/3123818
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 25 September 2017

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    Author Tags

    1. artificial neural networks
    2. face tracking
    3. gesture recognition
    4. intelligent wheelchair

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