Real-Time Classification of EMG Signals for Controlling Robot Gripper
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Abstract
This paper presents the acquisition system that is able to classify hand gestures using electromyography (EMG) signals from a MYO armband and/or the finger positions using a 5DT data glove. Both devices make the EMG recording system easier because the hand gesture is identified automatically by the data captured from the 5DT data glove. The recorded EMG data can be analyzed using classification techniques, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Artificial neural network (ANN), for creating the hand gesture prediction model. The proposed system can classify three gestures, including rest hand, close hand, and open hand. The recorded data using the MYO armband, the 5DT data glove, and the ANN technique provide maximum accuracy of 95.95% with ±1.13 SD while the captured data using the MYO solely with ANN deliver maximum accuracy of 97.37% with ±2.83 SD. Due to overlap variance, both performances are not significantly different. The distance-based method is also used for analyzing the system. The time consumption to record the relevant information using the MYO armband and the 5DT data glove is approximately 5 minutes. However, the recorded system using the MYO armband solely takes around 12 minutes due to manual data registration process. Subsequently, the time consumptions of these two systems show that the data recording system based on both devices is 2.4 times faster than that of using the MYO armband only.
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Agamemnon, K., Iris, K., Mustafa, E., Kianoush, N., &Sethu, V. (2019). Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements. Journal of NeuroEngineering and Rehabilitation, 14, 71.
Al-Ani, A., & Deriche, M.. (2002). Feature selection using a mutual information based measure. In Object recognition supported by user interaction for service robots, 4, 82–85.
Al-Timemy, A. H., Khushaba, R. N., Bugmann, G., & Escudero, J. (2016). Improving the Performance Against Force Variation of EMG Controlled Multifunctional Upper-Limb Prostheses for Transradial Amputees. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(6), 650–661.
Barrios, L. A. (2010). Real-time control of a multi-fingered robot hand using EMG signals [Master’s thesis]. San Diego State University. https://digitallibrary.sdsu.edu/islandora/object/sdsu%3A4720
Capa, E., Cotur, Y., Gumus, C., Kaplanoglu, E., & Ozkan, M. (2014). Comparative EMG classification of index finger (pp. 1-3). Istanbul, Turkey. https://doi.org/ 10.1109/BIYOMUT.2014.7026365
Castellini, C., Artemiadis, P., Wininger, M., Ajoudani, A., Alimusaj, M., Bicchi, A., Caputo, B., Craelius, W., Dosen, S., Englehart, K., Farina, D., Gijsberts, A., Godfrey, S. B., Hargrove, L., Ison, M., Kuiken, T., Marković, M., Pilarski, P. M., Rupp, R., & Scheme, E. (2004). Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography. Frontiers in Neurorobotics, 8, 22.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Data Gloves 5DT. (2018). 5DT. http://www.5dt.com/data-glovesde Sá, J. P. M. (2012). Pattern Recognition: Concepts, Methods and Applications. Springer Science & Business Media.
Fougner, A., Scheme, E., Chan, A. D. C., Englehart, K.,,& Stavdahl, Ø. (2011). Resolving the Limb Positione Effect in Myoelectric Pattern Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(6),644–651.
Gauthaam, M., & Kumar, S. (2011, February). EMG controlled bionic arm [Paper presentation]. 2011 National Conference on Innovations in Emerging Technology, Erode, India
Gu, Y., Yang, D., Huang, Q., Yang W., & Liu, H. (2018). Robust EMG pattern recognition in the presence of confounding factors: Features classifiers and adaptive learning. Expert Systems with Applications, 96, 208-217.
Hakonen, M., Piitulainen, H. ., & Visala, A. (2015). Current state of digital signal processing in myoelectric interfaces and related applications. Biomedical Signal Processing & Control, 18, 334–359.
Jiang, N., Dosen, S., Muller, K., & Farina, D. (2012). Myoelectric Control of Artificial Limbs amp-Is There a Need to Change Focus?. IEEE Signal Processing Magazine, 29(5), 152–150.
Jianwei, L., Dinggua, Z., Xinjun, S.,& Xianyang, Z. (2014). Quantification and solutions of arm movements effect on sEMG pattern recognition. Biomedical Signal Processing and control. Science Direct, 13, 189-197.
Karen, T., Michael, C., Eduardo, R. & Diego, F. (2018). Stepping-stones to Transhumanism: An EMG-controlled Low-cost Prosthetic Hand for Academia (pp. 807-812). Funchal, Portugal.https://doi.org/10.1109/IS.2018.8710489
Keinosuke, F. (1990). Introduction to Statistical Pattern Recognition (2nd ed.). Academic Press.
Konig, A. (2000). Dimensionality reduction techniques for multivariate data classification, interactive visualization, and analysis-systematic feature se lection vs. extraction. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings, 1, 44–55.
Konig, A., Eberhard, M., & Wenzel, R. (1998). A transparent and flexible development environment for rapid design of cognitive systems. 24th EUROMICRO Conference, 2, 655–662.
Myo Gesture Control Armband. (2018). MYO Alpha . https://www.myo.com
Nguyen, N. T. N. (2018). Developing A low-cost Myoelectric Prosthetic hand. Metropolia University of Applied Sciences.
Oskoei, M. A.,& Hu, H. (2007). Myoelectric control systems-A survey. Biomedical Signal Processing and Control, 2, 275–294.
Pan, L., Li, J., Xia, W., Zhang, D., & Dong, L. (2015). An effective method, composed of LAMP and dCAPS, to detect different mutations in fenoxaprop-P-ethyl-resistant American sloughgrass (Beckmannia syzigachne Steud.) populations. Pesticide Biochemistry and Physiology, 117, 1–8.
Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012). Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 39(8), 7420–7431.
Raez, M. B. I., Hussain, M. S., & F. Mohd-Yasin. (2006). Techniques of EMG signal analysis: detection. Biological Procedures Online, 8, 11–35.
Roberto, A., Jose, M.S.,& Rosa, M.V. (2010). Edited Nearest Neighbor Rule for Improving Neural Networks Classifications. Advance in neural networks, 6063, 303-310.
Stango, A., Negro, F., & Farina, D. (2015). Spatial Correlation of High Density EMG Signals Provides Features Robust to Electrode Number and Shift in Pattern Recognition for Myocontrol. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(2), 189–198.
Steven, W. S. (2003). The Scientist and Engineer’s Guide to Digital Signal Processing’s Table of Content. California Technical Publishing.
Tipping, M. E., & Bishop, C. M. (1999). Mixtures of Probabilistic Principal Component Analysers. Neural computation, 11(2), 443-482.
Wright, T. W., Hagen, A. D.,& Wood, M. B. (1995). Prosthetic usage in major upper extremity amputations. Journal of Hand Surgery, 20(4), 619–622.
Young, A. J., Hargrove, L. J.,& Kuiken, T. A. (2012). Improving Myoelectric Pattern Recognition Robustness to Electrode Shift by Changing Interelectrode Distance and Electrode Configuration. IEEE Transactions on Biomedical Engineering, 59(3), 645–652.
Zhang, D., Xiong, A., Zhao, X., & Han, J. (2012). PCA and LDA for EMG-based control of bionic mechanical hand (pp. 960-965). Shenyang, China. https://doi.org/10.1109/ICInfA.2012.6246955