Citation: Jose Guadalupe Beltran-Hernandez, Jose Ruiz-Pinales, Pedro Lopez-Rodriguez, Jose Luis Lopez-Ramirez, Juan Gabriel Avina-Cervantes. Multi-Stroke handwriting character recognition based on sEMG using convolutional-recurrent neural networks[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 5432-5448. doi: 10.3934/mbe.2020293
[1] | J. E. Maldarelli, B. A. Kahrs, S. C. Hunt, J. J. Lockman, Development of early handwriting: Visual-motor control during letter copying, Dev. Psychol., 51 (2015), 879-888. |
[2] | J. Calvo-Zaragoza, J. Oncina, Recognition of pen-based music notation with finite-state machines, Expert Syst. Appl., 72 (2017), 395-406. |
[3] | N. Mendes, M. Sim£o, P. Neto, Segmentation of electromyography signals for pattern recognition, IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, 2019, 732-737. |
[4] | M. Słapek, S. Paszkiel, Detection of gestures without begin and end markers by fitting into Bèzier curves with least squares method, Pattern Recognit. Lett., 100 (2017), 83-88. |
[5] | K. A. Lamkin-Kennard, M. B. Popovic, Sensors: Natural and Synthetic Sensors, Biomechatronics, Elsevier, 2019, 81-107. |
[6] | J. Wu, X. Li, W. Liu, Z. Jane Wang, sEMG Signal Processing Methods: A Review, J. Phys., 1237 (2019), 032008. |
[7] | E. Guigon, P. Baraduc, M. Desmurget, Computational Motor Control: Redundancy and Invariance, J. Neurophysiol., 97 (2007), 331-347. |
[8] | C. J. De Luca, Physiology and Mathematics of Myoelectric Signals, IEEE Trans. Biomed. Eng., BME-26 (1979), 313-325. |
[9] | M. Sim£o, N. Mendes, O. Gibaru, P. Neto, A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction, IEEE Access, 7 (2019), 39564-39582. |
[10] | Y. Gloumakov, J. Bimbo, A. M. Dollar, Trajectory Control For a Myoelectric Prosthetic Wrist, Myoelectric Controls Symposium, 2020. |
[11] | A. Lansari, F. Bouslama, M. Khasawneh, A. Al-Rawi, A novel electromyography (EMG) based classification approach for Arabic handwriting, Proceedings of the International Joint Conference on Neural Networks, 2003. |
[12] | G. Huang, D. Zhang, X. Zheng, X. Zhu, An EMG-based handwriting recognition through dynamic time warping, in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE, 2010, 4902-4905. |
[13] | M. Linderman, M. A. Lebedev, J. S. Erlichman, Recognition of Handwriting from Electromyography, PLoS ONE, 4 (2009), e6791. |
[14] | C. Li, Z. Ma, L. Yao, D. Zhang, Improvements on EMG-based handwriting recognition with DTW algorithm, in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2013. |
[15] | I. Chihi, A. Afef, B. Mohamed, Analysis of Handwriting Velocity to Identify Handwriting Process from Electromyographic Signals, Am. J. Appl. Sci., 9 (2012), 1742-1756. |
[16] | M. A. Slim, A. Abdelkrim, M. Benrejeb, An efficient handwriting velocity modelling for electromyographic signals reconstruction using Radial Basis Function neural networks, 2015 7th International Conference on Modelling, Identification and Control (ICMIC), IEEE, 2015, 1-6. |
[17] | E. Okorokova, M. Lebedev, M. Linderman, A. Ossadtchi, A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings, Front. Neurosci., 9 (2015), 1-15. |
[18] | W. Wei, Q. Dai, Y. Wong, Y. Hu, M. Kankanhalli, W. Geng, Surface-Electromyography-Based Gesture Recognition by Multi-View Deep Learning, IEEE Trans. Biomed. Eng., 66 (2019), 2964-2973. |
[19] | A. Dash, A. Sahu, R. Shringi, J. Gamboa, M. Z. Afzal, M. I. Malik, et al., AirScript-Creating Documents in Air, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), IEEE, 2017. |
[20] | P. Roy, S. Ghosh, U. Pal, A CNN Based Framework for Unistroke Numeral Recognition in Air-Writing, 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE, 2018, 404-409. |
[21] | N. Rusk, Deep learning, Nat. Methods, 13 (2016), 35. |
[22] | H. Chen, Y. Zhang, G. Li, Y. Fang, H. Liu, Surface electromyography feature extraction via convolutional neural network, Int. J. Mach. Learn. Cybern., 11 (2020), 185-196. |
[23] | M. Simão, P. Neto, O. Gibaru, EMG-based online classification of gestures with recurrent neural networks, Pattern Recognit. Lett., 128 (2019), 45-51. |
[24] | Y. Lecun, Y. Bengio, G. Hinton, Deep learning, Nature, 521 (2015), 436-444. |
[25] | D. H. Hubel, T. N. Wiesel, Receptive fields and functional architecture of monkey striate cortex, J. Physiol., 195 (1968), 215-243. |
[26] | D. Marr, Analyzing natural images: A computational theory of texture vision., Cold Spring Harbor symposia on quantitative biology, Cold Spring Harbor Laboratory Press, 1976, 647-662. |
[27] | K. Fukushima, S. Miyake, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Competition and Cooperation in Neural Nets, Springer, Berlin, Heidelberg, 1980, 267-285. |
[28] | C. Lee, P. W. Gallagher, Z. Tu, Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree, Artificial intelligence and statistics, 2016, 464-472. |
[29] | J. Weng, N. Ahuja, T. S. Huang, Cresceptron: A self-organizing neural network which grows adaptively, International Joint Conference on Neural Networks (IJCNN), 1992, 576-581. |
[30] | Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, et al., Backpropagation Applied to Handwritten Zip Code Recognition, Neural Comput., 1 (1989), 541-551. |
[31] | Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE, 86 (1998), 2278-2324. |
[32] | W. Zhang, K. Itoh, J. Tanida, Y. Ichioka, Parallel distributed processing model with local space-invariant interconnections and its optical architecture, Appl. Optics, 29 (1990), 4790-4797. |
[33] | J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, et al., Recent advances in convolutional neural networks, Pattern Recognit., 77 (2018), 354 - 377. |
[34] | A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems 25, 2012. |
[35] | O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, et al., ImageNet Large Scale Visual Recognition Challenge, Int. J. Comput. Vis., 115 (2015), 211-252. doi: 10.1007/s11263-015-0816-y |
[36] | V. Nair, G. E. Hinton, Rectified linear units improve restricted boltzmann machines, in Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML10, Omnipress, Madison, WI, USA, 2010, 807814. |
[37] | N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15 (2014), 1929-1958. |
[38] | J. Wang, L. Perez, The effectiveness of data augmentation in image classification using deep learning, Convolutional Neural Networks Vis. Recognit., 11 (2017). |
[39] | K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2016, 770-778. |
[40] | S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proceedings of the 32nd International Conference on Machine Learning, ICML 2015. |
[41] | M. I. Jordan, Attractor dynamics and parallelism in a connectionist sequential machine, Artificial neural networks: Concept learning. 1990, 112-127. |
[42] | J. L. Elman, Finding Structure in Time, Cognit. Sci., 14 (1990), 179-211. |
[43] | S. Hochreiter, Untersuchungen zu dynamischen neuronalen Netzen, Master's thesis, Institut für Informatik, Technische Universität, Munchen, 1991. |
[44] | Y. Bengio, P. Simard, P. Frasconi, Learning Long-Term Dependencies with Gradient Descent is Difficult, IEEE Trans. Neural Networks, 5 (1994), 157-166. |
[45] | S. Hochreiter, J. Schmidhuber, Long Short-Term Memory, Neural Comput., 9 (1997), 1735-1780. |
[46] | K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, et al., Learning phrase representations using RNN encoder-decoder for statistical machine translation, arXiv preprint arXiv:1406.1078, 1724-1734. |
[47] | T. Dozat, Incorporating nesterov momentum into adam, in International Conference on Learning Representations, ICLR, 2016. |
[48] | L. N. Smith, Cyclical Learning Rates for Training Neural Networks, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, 2017, 464-472. |