ABSTRACT
Human Machine Interface (HMI) application based on Electrooculogram (EOG) signals for converting user intention into control command finds promising scope in development of prosthetic devices for persons suffering from motor impairment. In the present work, the EOG signals based control system has been investigated in offline environment. The signal has been acquired through g.LADYbird active electrodes placed at distinct positions on human face around the eyes. A classifier model has been trained by feature matrix which encapsulates the time domain features extracted by using Dual Tree Complex Wavelet Transform (DTCWT). Linear Support Vector Machine (SVM) classifier has been used to develop a classified trained model by using 240 training data sets recorded from 12 healthy subjects. The MATLAB simulation showed 99.2% classification accuracy for horizontal eye movement in two directions, left and right. The classified signals have been converted into commands through Arduino to grasp and release an object by prosthetic myoelectric hand.
- B. Champaty, J. Jose, K. Pal, and A. Thirugnanam, Development of EOG Based Human Machine Interface control System for Motorized Wheelchair," in International Conference on Magnetics, Machines and Drives, Kottayam, India, 2014.Google Scholar
- M. M. U. Atique, S. H. Rakib, and K. Siddique-e-rabbani, "An Electrooculogram Based Control System," in 5th International Conference on Informatics, Electronics and Vision, Dhaka, Bangladesh, pp. 809--812, 2016.Google Scholar
- G. W. Doyle TE, Kucerovsky Z, "Design of an Electroocular computing interface," in Canadian Conference on Electrical and Computer Engineering, Ottawa, Canada, pp. 1458--1461, 2006.Google Scholar
- Bulling, Andreas Ward, Jamie A Gellersen, Hans Tro, Gerhard, "Eye Movement Analysis for Activity Recognition Using Electrooculography", in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, pp. 741--753, 2011. Google ScholarDigital Library
- Dong, Enzeng, Li, Changhai, Chen, Chao," An EOG Signals Recognition Method Based on Improved Threshold Dual Tree Complex Wavelet Transform", IEEE International Conference on Mechatronics and Automation, pp. 954--959, 2016.Google Scholar
- M. Pontil and A. Verri, "Support Vector Machines for 3D Object Recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 6, pp. 637--646, 1998. Google ScholarDigital Library
- Fierro Massimo, Ha Ho-Gun, Ha, Yeong-Ho," Noise reduction based on partial-reference, dual-tree complex wavelet transform shrinkage", IEEE transactions on image processing, pp. 1859--72, 2013. Google ScholarDigital Library
- Selesnick I W, Baraniuk R G, Kingsbury N G, "The dual-tree complex wavelet transform," IEEE Signal Processing Magazine, vol. 22, no. 6, pp. 123- 151, 2005.Google ScholarCross Ref
- O. Chapelle, P. Haffner, and V. N. Vapnik, "Support Vector Machines for Histogram-Based Image Classification," IEEE Trans. Neural Networks, vol. 10, no. 5, pp. 1055--1064, 1999. Google ScholarDigital Library
- C. Chao and M. Horng, "The Construction of Support Vector Machine Classifier Using the Firefly Algorithm," Comput. Intell. Neurosci., vol. 2015, 2015. Google ScholarDigital Library
- M. J. Berg Alexander C, Zhang Hao, Maire Michael, "SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category," IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2, pp. 2126--2136, 2006. Google ScholarDigital Library
- A. M. Gaur, Karanveer and Amod Kumar, "Design and development of touch based switching of myoelectric arm", Communication Control and Computing Technologies (ICCCCT), 2010 IEEE International Conference, pp. 247--249, 2010.Google Scholar
Index Terms
- An Electrooculogram Signal Based Control System in Offline Environment
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