참고문헌
- Ahsan, Md Rezwanul, Muhammad I. Ibrahimy, and Othman O. Khalifa. "EMG signal classification for human computer interaction: a review.", European Journal of Scientific Research, vol 33, no. 3, pp. 480-501, 2009.
- Abreu, Teixeira and Figueiredo, "Evaluating Sign Language Recognition Using the Myo Armband." XVIII Symposium on Virtual and Augmented Reality(SVR) on IEEE, 2016.
- W Geng, Y Du and W Jin, "Gesture recognition by instantaneous surface EMG images." Scientific reports, 6, 36571, 2016. https://doi.org/10.1038/srep36571
- E. C. Jeong, S. J. Kim, Y. R. Song, S, M, Lee. "Artificial Neural Network based Motion Classification Algorithm using Surface Electromyogram", Rehabilitation Engineering & Assistive Technology Society of Korea, vol. 6, no. 1, pp. 67-74, 2012.
- Khushaba, Rami N. et al., "Combined influence of forearm orientation and muscular contraction on EMG pattern recognition.", Expert Systems with Applications, vol. 61, pp. 154-161, 2016. https://doi.org/10.1016/j.eswa.2016.05.031
- Sathiyanarayanan, Mithileysh, and Sharanya Rajan., "MYO Armband for physiotherapy healthcare: A case study using gesture recognition application.", Communication Systems and Networks, 8th International Conference on. IEEE, 2016.
- Phinyomark, Angkoon and Pornchai Phukpattaranont, and Chusak Limsakul, "Feature reduction and selection for EMG signal classification." Expert Systems with Applications, vol. 39, no. 8, pp. 7420-7431, 2012. https://doi.org/10.1016/j.eswa.2012.01.102
- Caesarendra and Wahyu, "A classification method of hand EMG signals based on principal component analysis and artificial neural network." International Conference on Instrumentation, Control and Automation (ICA), Bandung, Indonesia, August, 2016.
- Purushothaman, Geethanjali, and K. K. Ray. "EMG based man-machine interaction-A pattern recognition research platform." Robotics and Autonomous Systems, vol. 62, no. 6, pp. 864-870, 2014. https://doi.org/10.1016/j.robot.2014.01.008
- Ahsan, Md Rezwanul and Muhammad I. Ibrahimy, "EMG signal classification for human computer interaction: a review." European Journal of Scientific Research, vol. 33, no. 3, pp. 480-501, 2009.
- Ariyanto and Mochammad, "Finger movement pattern recognition method using artificial neural network based on electromyography (EMG) sensor." Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), Bandung, Indonesia, pp. 29-30, 2015.
- Y. R. Song, S. J. Kim, E. C. Jeong, S. M. Lee., "A Gaussian Mixture Model Based Pattern Classification Glgorithm of Rorearm Electromyogram", Rehabilitation Engineering & Assistive Technology Society of Korea, vol. 5, no. 1, pp. 95-101, 2011.
- Sapsanis, Christos, George Georgoulas, and Anthony Tzes., "EMG based classification of basic hand movements based on time-frequency features." Control & Automation (MED), 21st Mediterranean Conference on. IEEE, 2013.
- Federolf, P. A., K. A. Boyer and T. P. Andriacchi. "Application of principal component analysis in clinical gait research: identification of systematic differences between healthy and medial knee-osteoarthritic gait." Journal of biomechanics, vol. 46, no. 13, pp. 2173-2178, 2013. https://doi.org/10.1016/j.jbiomech.2013.06.032
- Bosco and Gianfranco. "Principal component analysis of electromyographic signals: an overview." The Open Rehabilitation Journal, vol. 3, no. 1, pp. 127-131, 2010. https://doi.org/10.2174/1874943701003010127
- Al-Faiz, Mohammed Z, A. Ali and Abbas H. Miry, ""A k-nearest neighbor based algorithm for human arm movements recognition using EMG signals." Energy, Power and Control (EPC-IQ), Basrah, Iraq, November, 2010.