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Classification of Hand Motion Using Surface EMG Signals
References
Doringer, J.A. and Hogan, N. 1995. Performance of above elbow body-powered
prostheses in visually guided unconstrained motion tasks. IEEE Transactions on
Biomedical Engineering, 42: 621–631.
Du, S.J. and Vuskovic, M. 2004. Temporal vs. spectral approach to feature extraction
from prehensile EMG signals. Paper read at the IEEE International Conference
on Information Reuse and Integration, Las Vegas, NV, November 2009.
Du, Y.-C., Lin, C.-H., Shyu, L.-Y. and Chen, T. 2010. Portable hand motion classifier
for multi-channel surface electromyography recognition using grey relational
analysis. Expert Systems with Applications, 37(6): 4283–4291.
Farry, K.A., Walker, I.D., and Barabiuk, R.G. 1996. Myoelectric teleoperation of a complex robotic hand. IEEE Transactions on Robotics and Automation, 12: 775–788.
Fukuda, O., Tsuji, T., and Kaneko, M. 1997. An EMG controlled robotics manipulator
using neural networks. Paper read at the IEEE International Workshop on Robot
and Human Communication, Sendai, Japan, October 1997.
Hannaford, B. and Lehman, S. 1986. Short time fourier analysis of the electromyogram: fast movements and constant contraction. IEEE Transactions on Biomedical
Engineering, 33: 1773–1181.
Hudgins, B. and Parker, P. 1993. A new strategy for multifunction myoelectric control.
IEEE Transactions on Biomedical Engineering, 40: 82–94.
Ito, K., Tsuji, T., Kato, A., and Ito, M. 1992. EMG pattern classification for a prosthetic
forearm with three degrees of freedom. Paper read at the IEEE International
Workshop on Robot and Human Communication, Toyko, Japan, September
1992.
Jin, D.W., Zhang, R.H., Zhang, J.C., Wang, R.C., and Gruver, W.A. 2000. An intelligent
above-knee prosthesis with EMG-based terrain identification. Paper read at the
IEEE International Conference on Systems, Man and Cybernetics, Nashville,
TN, October 2002.
Kuribayashi, K., Okimura, K., and Taniguchi, T. 1993. A discrimination system using
neural network for EMG-controller prostheses—Integral type of EMG signal
processing. Paper read at the IEEE/RSJ International Conference on Intelligent
Robots and Systems, Tokyo, Japan, July 1993.
Lange, N., Strother, S.C., Anderson, J.R., Nielsen, F.A., Holmes, A.P., Kolenda, T.,
Savoy, R., and Hansen, L.K. 1999. Plurality and resemblance in fMRI data analysis. Neuroimage, 10: 282–303.
Lin, I.-K. 1989. A concordance correlation coefficient to evaluate reproducibility.
Biometrics, 45(1): 255–268.
Merletti, R. and Parker, P. 2004. Electromyography: Physiology, Engineering and
Noninvasive Applications. Hoboken, NJ: Wiley.
Miller, C.J. 2008. Real-time feature extraction and classification of prehensile EMG
signals. Master’s thesis, San Diego State University.
Mogk, P.M. and Keir, J. 2003. Crosstalk in surface electromyography of the proximal
forearm during gripping tasks. Journal of Electromyography and Kinesiology, 13:
63–71.
Classification of Hand Motion Using Surface EMG Signals
References
Doringer, J.A. and Hogan, N. 1995. Performance of above elbow body-powered
prostheses in visually guided unconstrained motion tasks. IEEE Transactions on
Biomedical Engineering, 42: 621–631.
Du, S.J. and Vuskovic, M. 2004. Temporal vs. spectral approach to feature extraction
from prehensile EMG signals. Paper read at the IEEE International Conference
on Information Reuse and Integration, Las Vegas, NV, November 2009.
Du, Y.-C., Lin, C.-H., Shyu, L.-Y. and Chen, T. 2010. Portable hand motion classifier
for multi-channel surface electromyography recognition using grey relational
analysis. Expert Systems with Applications, 37(6): 4283–4291.
Farry, K.A., Walker, I.D., and Barabiuk, R.G. 1996. Myoelectric teleoperation of a complex robotic hand. IEEE Transactions on Robotics and Automation, 12: 775–788.
Fukuda, O., Tsuji, T., and Kaneko, M. 1997. An EMG controlled robotics manipulator
using neural networks. Paper read at the IEEE International Workshop on Robot
and Human Communication, Sendai, Japan, October 1997.
Hannaford, B. and Lehman, S. 1986. Short time fourier analysis of the electromyogram: fast movements and constant contraction. IEEE Transactions on Biomedical
Engineering, 33: 1773–1181.
Hudgins, B. and Parker, P. 1993. A new strategy for multifunction myoelectric control.
IEEE Transactions on Biomedical Engineering, 40: 82–94.
Ito, K., Tsuji, T., Kato, A., and Ito, M. 1992. EMG pattern classification for a prosthetic
forearm with three degrees of freedom. Paper read at the IEEE International
Workshop on Robot and Human Communication, Toyko, Japan, September
1992.
Jin, D.W., Zhang, R.H., Zhang, J.C., Wang, R.C., and Gruver, W.A. 2000. An intelligent
above-knee prosthesis with EMG-based terrain identification. Paper read at the
IEEE International Conference on Systems, Man and Cybernetics, Nashville,
TN, October 2002.
Kuribayashi, K., Okimura, K., and Taniguchi, T. 1993. A discrimination system using
neural network for EMG-controller prostheses—Integral type of EMG signal
processing. Paper read at the IEEE/RSJ International Conference on Intelligent
Robots and Systems, Tokyo, Japan, July 1993.
Lange, N., Strother, S.C., Anderson, J.R., Nielsen, F.A., Holmes, A.P., Kolenda, T.,
Savoy, R., and Hansen, L.K. 1999. Plurality and resemblance in fMRI data analysis. Neuroimage, 10: 282–303.
Lin, I.-K. 1989. A concordance correlation coefficient to evaluate reproducibility.
Biometrics, 45(1): 255–268.
Merletti, R. and Parker, P. 2004. Electromyography: Physiology, Engineering and
Noninvasive Applications. Hoboken, NJ: Wiley.
Miller, C.J. 2008. Real-time feature extraction and classification of prehensile EMG
signals. Master’s thesis, San Diego State University.
Mogk, P.M. and Keir, J. 2003. Crosstalk in surface electromyography of the proximal
forearm during gripping tasks. Journal of Electromyography and Kinesiology, 13:
63–71.