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Estimation of Wrist Movements based on a Regression Technique for Wearable Robot Interfaces
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  • Journal title : Journal of KIISE
  • Volume 42, Issue 12,  2015, pp.1544-1550
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2015.42.12.1544
 Title & Authors
Estimation of Wrist Movements based on a Regression Technique for Wearable Robot Interfaces
Park, Ki-Hee; Lee, Seong-Whan;
Recently, the development of practical wearable robot interfaces has resulted in the emergence of wearable robots such as arm prosthetics or lower-limb exoskeletons. In this paper, we propose a novel method of wrist movement intention estimation based on a regression technique using electromyography of human bio-signals. In daily life, changes in user arm position changes cause decreases in performance by modulating EMG signals. Therefore, we propose an estimation method for robust wrist movement intention for arm position changes, combining several movement intention models based on the regression technique trained by different arm positions. In our experimental results, our method estimates wrist movement intention more accurately than previous methods.
Wearable Robot Interfaces;Electromyogram;Pattern Recognition;Regression Technique;
 Cited by
T. S. Saponas, D. S. Tan, D. Morris, J. Turner, and J. A. Landay, "Making Muscle-Computer Interfaces More Practical," SIGCHI Conference on Human Factors in Computing Systems (CHI 2010), Atlanta, GA, USA, Apr. 10-15, 2010.

N. Jiang, S. Dosen, K.-R. Muller, and D. Farina, "Myoelectric Control of Artificial Limbs-Is There a Need to Change Focus?," IEEE Signal Processing Magazine, Vol. 29, No. 5, pp. 152-150, 2012. crossref(new window)

A. Fougner, E. Scheme, A. D. C. Chan, K. Englehart, and O. Stavdahl, "Resolving the Limb Position Effect in Myoelectric Pattern Recognition," IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 19, No. 6, pp. 644-651, 2011. crossref(new window)

Y. Geng, P. Zhou, and G. Li, "Toward Attenuating the Impact of Arm Positions on Electromyography Pattern-Recognition based Motion Classification in Transradial Amputees," Journal of NeuroEngineering and Rehabilitation, Vol. 9, No. 1, pp. 74, 2012. crossref(new window)

R. N. Khushaba, M. Takruri, J. V. Miro, and S. Kodagoda, "Towards Limb Position Invariant Myoelectric Pattern Recognition using Time-dependent Spectral Features," Neural Networks, Vol. 55, pp. 42-58, 2014. crossref(new window)

J. M. Hahne, F. Biessmann, N. Jiang, H. Rehbaum, D. Farina, F. C. Meinecke, K.-R. Muller, and L. C. Parra, "Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control," IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, No. 2, pp. 269-279, 2014. crossref(new window)

C. M. Bishop, Pattern Recognition and Machine Learning. Springer New York, 2006.

H.-I. Suk and S.-W. Lee, "A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 2, pp. 286-299, 2013. crossref(new window)

J.-H. Kim, F. Biessmann, and S.-W. Lee, "Decoding Three-dimensional Trajectory of Executed and Imagined Arm Movements from Electroencephalogram Signals," IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 12, No. 5, pp. 867-876, 2015.

J. C. Platt, "Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods," in Advances in Large Margin Classifiers, A. J. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, Eds. Cambridge: MIT Press, 2000, pp. 1-11.

C.-C. Chang and C.-J. Lin, "LIBSVM: A Library for Support Vector Machines," ACM Transactions on Intelligent Systems and Technology, Vol. 2, No. 3, pp. 1-27, 2011.