EMG Signal Acquisition and Processing Application with CNN Testing for MATLAB
This paper presents the implementation of a versatile MATLAB application focused on acquiring, visualizing and storing the electromyographic (EMG) signals read by the sensors of the Myo Armband device, where it is possible to perform signal processing by means of a predetermined function by the user, in order to be able to build databases of both raw and processed EMG signals. It also includes an option to perform real-time tests of convolutional neural networks that have been trained with the acquired databases. To test the application, it is presented a basic example of acquisition and processing of the acquired signals for the recognition of 2 hand gestures, using Power Spectral Density as feature extraction function, and with the feature maps obtained through the extraction, the training of a convolutional neural network is performed, getting a 95% accuracy of recognition, and additionally, the validation is performed through real-time tests within the application, demonstrating the usefulness and performance of the developed application.
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M. Sathiyanarayanan and S. Rajan, MYO Armband for physiotherapy healthcare: A case study using gesture recognition application In 8th International Conference on Communication Systems and Networks (COMSNETS), IEEE, 2016, pp. 1-6.
W. Tigra, et al., A novel EMG interface for individuals with tetraplegia to pilot robot hand grasping IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016.
F. Khanam and M. Ahmad, Estimation of work done in lower limb using EMG, In IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE, 2015, pp. 431-434.
G. Jang, J. Kim, S. Lee and Y. Choi, EMG-based continuous control scheme with simple classifier for electric-powered wheelchair, IEEE Transactions on Industrial Electronics, 2016, vol. 63, no 6, pp. 3695-3705.
E. Noce, L. Zollo, A. Davalli, R. Sacchetti and E. Guglielmelli, Experimental analysis of the relationship between neural and muscular recordings during hand control, In 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), IEEE, 2016, pp. 1104-1109.
P. K. Artemiadis and K. J. Kyriakopoulos, An EMG-based robot control scheme robust to time-varying EMG signal features, IEEE Transactions on Information Technology in Biomedicine, 2010, vol. 14, no 3, pp. 582-588.
J. I. Furukawa, T. Noda, T. Teramae and J. Morimoto, Human Movement Modeling to Detect Biosignal Sensor Failures for Myoelectric Assistive Robot Control, IEEE Transactions on Robotics, 2017.
P. U. Murillo, R. J. Moreno and M. Mauledeox, Multi User Myographic Characterization for Robotic Arm Manipulation, International Journal of Applied Engineering Research, 2016, vol. 11, no 23, pp. 11299-11304.
Thalmic LabsTM, Technical Specifications. Consulted on July 25, 2017, [Online]. Available in: https://www.myo.com/techspecs
Thalmic LabsTM, Myo SDK Manual: Getting Started. Consulted on July 25, 2017, [Online]. Available in: https://developer.thalmic.com/docs/api_reference/platform/getting-started.html
P. U. Murillo, R. J. Moreno and O. A. Sánchez, Individual Robotic Arms Manipulator Control Employing Electromyographic Signals Acquired by Myo Armbands, International Journal of Applied Engineering Research, 2016, vol. 11, no 23, pp. 11241-11249.
T. Mulling and M. Sathiyanarayanan, Characteristics of hand gesture navigation: a case study using a wearable device (MYO), In Proceedings of the 2015 British HCI Conference, ACM, 2015, pp. 283-284.
J. G. Abreu, J. M. Teixeira, L. S. Figueiredo and V. Teichrieb, Evaluating Sign Language Recognition Using the Myo Armband, In XVIII Symposium on Virtual and Augmented Reality (SVR), IEEE, 2016, pp. 64-70.
Z. Arief, I. A. Sulistijono and R. A. Ardiansyah, Comparison of five time series EMG features extractions using Myo Armband, In International Electronics Symposium (IES), IEEE, 2015, pp. 11-14.
Y. LeCun, Y. Bengio and G. Hinton, Deep learning, Nature, 2015, vol. 521, no 7553, pp. 436-444.
M. Wand and T. Schultz, Pattern learning with deep neural networks in EMG-based speech recognition, In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2014, pp. 4200-4203.
M. D. Zeiler and R. Fergus, Visualizing and Understanding Convolutional Networks, In European conference on computer vision, Springer, Cham, 2014. pp. 818-833.
A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks, In Advances in neural information processing systems, 2012, pp. 1097-1105.
Y. Zhang, et al., Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks. Interspeech 2016, 2016, pp. 410-414.
N. S. Velandia, R. J. Moreno and R. D. H. Beleno, CNN architecture for robotic arm control in a 3D virtual environment by means of by means of EMG signals, Contemporary Engineering Sciences, 2017, Vol. 10, no. 28, pp. 1377-1390.
P. Welch, The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms, IEEE Transactions on audio and electroacoustics, 1967, vol. 15, no 2, pp. 70-73.
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