Abstract
Identifying movement abnormalities from raw Electromyography (EMG) data requires three steps that are the data pre-processing, the feature extraction and training a classifier. As EMG data shows large variation (even for consecutive trials in a single subject) probabilistic classifiers like naive Bayes or probabilistic support vector machines have been proposed. The used feature representations (e.g., principal components analysis, non negative matrix factorization, wavelet transformation) however, can not capture the variation. Here, we propose a fully Bayesian approach where both, the features and the classifier, are probabilistic models. The generative model reproduces the observed variance in the EMG data, provides an estimate of the reliability of the predictions and can be applied in combination with dimensionality reduction techniques such as PCA and NMF. In first tests, we found that these probabilistic extensions outperforms classical approaches in terms of the prediction of knee abnormalities from few samples with a performance of 86 percent of correctly classified abnormalities.
The original version of this chapter was inadvertently published with an incorrect chapter pagination 662–666 and DOI 10.1007/978-3-319-32703-7_128. The page range and the DOI has been re-assigned. The correct page range is 668–672 and the DOI is 10.1007/978-3-319-32703-7_129. The erratum to this chapter is available at DOI: 10.1007/978-3-319-32703-7_260
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-32703-7_260
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References
P. Konrad, “The abc of emg,” A practical introduction to kinesiological electromyography, vol. 1, 2005.
J. E. Duggan and G. B. Drummond, “Abdominal muscle activity and intraabdominal pressure after upper abdominal surgery.” Anesthesia & Analgesia, vol. 69, no. 5, pp. 598–603, 1989.
N. F. Güler and S. Koçer, “Use of support vector machines and neural network in diagnosis of neuromuscular disorders,” Journal of medical systems, vol. 29, no. 3, pp. 271–284, 2005.
C. Castellini and P. van der Smagt, “Surface emg in advanced hand prosthetics,” Biological cybernetics, vol. 100, no. 1, pp. 35–47, 2009.
D. M. Rittenhouse, H. A. Abdullah, R. J. Runciman, and O. Basir, “A neural network model for reconstructing emg signals from eight shoulder muscles: Consequences for rehabilitation robotics and biofeedback,” Journal of biomechanics, vol. 39, no. 10, pp. 1924–1932, 2006.
D.-p. Yang, J.-d. Zhao, Y.-k. Gu, X.-q. Wang, N. Li, L. Jiang, H. Liu, H. Huang, and D.-w. Zhao, “An anthropomorphic robot hand developed based on underactuated mechanism and controlled by emg signals,” Journal of Bionic Engineering, vol. 6, no. 3, pp. 255–263, 2009.
S. Bitzer and P. van der Smagt, “Learning emg control of a robotic hand: towards active prostheses,” in Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on. IEEE, 2006, pp. 2819–2823.
J. L. McKay and L. H. Ting, “Optimization of muscle activity for task-level goals predicts complex changes in limb forces across biomechanical contexts,” PLoS Comput. Biol, vol. 8, no. 4, p. e1002465, 2012.
Z. O. Khokhar, Z. G. Xiao, C. Menon et al., “Surface emg pattern recognition for real-time control of a wrist exoskeleton,” Biomedical engineering online, vol. 9, no. 1, p. 41, 2010.
M. Bland et al., An introduction to medical statistics. Oxford University Press, 2000, no. Ed. 3.
A. Hiraiwa, K. Shimohara, and Y. Tokunaga, “Emg pattern analysis and classification by neural network,” in Systems, Man and Cybernetics, 1989. Conference Proceedings., IEEE International Conference on. IEEE, 1989, pp. 1113–1115.
C. Christodoulou, C. S. Pattichis et al., “Unsupervised pattern recognition for the classification of emg signals,” Biomedical Engineering, IEEE Transactions on, vol. 46, no. 2, pp. 169–178, 1999.
G. Wang, Z. Wang, W. Chen, and J. Zhuang, “Classification of surface emg signals using optimal wavelet packet method based on davies-bouldin criterion,” Medical and Biological Engineering and Computing, vol. 44, no. 10, pp. 865–872, 2006.
S. S. Nair, R. M. French, D. Laroche, and E. Thomas, “The application of machine learning algorithms to the analysis of electromyographic patterns from arthritic patients,” Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 18, no. 2, pp. 174–184, 2010.
A. Subasi, “Classification of emg signals using pso optimized svm for diagnosis of neuromuscular disorders,” Computers in biology and medicine, vol. 43, no. 5, pp. 576–586, 2013.
M. Reaz, M. Hussain, and F. Mohd-Yasin, “Techniques of emg signal analysis: detection, processing, classification and applications,” Biological procedures online, vol. 8, no. 1, pp. 11–35, 2006.
M. C. Tresch, V. C. Cheung, and A. d’Avella, “Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets,” Journal of Neurophysiology, vol. 95, no. 4, pp. 2199–2212, 2006.
I. Daubechies et al., Ten lectures on wavelets. SIAM, 1992, vol. 61.
A. D. Chan and K. B. Englehart, “Continuous myoelectric control for powered prostheses using hidden markov models,” Biomedical Engineering, IEEE Transactions on, vol. 52, no. 1, pp. 121–124, 2005.
E. Benetos, M. Kotti, and C. Kotropoulos, “Musical instrument classification using non-negative matrix factorization algorithms,” in Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on. IEEE, 2006.
A. Paraschos, C. Daniel, J. Peters, and G. Neumann, “Probabilistic movement primitives,” in Advances in Neural Information Processing Systems (NIPS), Cambridge, MA: MIT Press., 2013. [Online]. Available: http://www.ias.tu-darmstadt.de/uploads/Publications/Paraschos_NIPS_2013.pdf
E. Rueckert and A. d’Avella, “Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems,” Frontiers in computational neuroscience, vol. 7, 2013.
E. Rueckert, J. Mundo, A. Paraschos, J. Peters, and G. Neumann, “Extracting low-dimensional control variables for movement primitives,” in Proceedings of the International Conference on Robotics and Automation (ICRA), 2015.
Y. P. Ivanenko, R. E. Poppele, and F. Lacquaniti, “Five basic muscle activation patterns account for muscle activity during human locomotion,” The Journal of physiology, vol. 556, no. 1, pp. 267–282, 2004.
A. d’Avella, P. Saltiel, and E. Bizzi, “Combinations of muscle synergies in the construction of a natural motor behavior,” Nature neuroscience, vol. 6, no. 3, pp. 300–308, 2003.
M. Lichman, “UCI machine learning repository,” http://archive.ics.uci.edu/ml/datasets/EMG+dataset+in+Lower+Limb, 2013.
D. J. Berndt and J. Clifford, “Using dynamic time warping to find patterns in time series.” in KDD workshop, vol. 10, no. 16. Seattle, WA, 1994, pp. 359–370.
H. Huang, T. Kuiken, R. D. Lipschutz et al., “A strategy for identifying locomotion modes using surface electromyography,” Biomedical Engineering, IEEE Transactions on, vol. 56, no. 1, pp. 65–73, 2009.
A. H. Al-Timemy, G. Bugmann, J. Escudero, and N. Outram, “Classification of finger movements for the dexterous hand prosthesis control with surface electromyography,” Biomedical and Health Informatics, IEEE Journal of, vol. 17, no. 3, pp. 608–618, 2013.
C. M. Bishop, Pattern recognition and machine learning. springer, 2006.
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Kohlschuetter, J., Peters, J., Rueckert, E. (2016). Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_129
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