Abstract
Surface electromyography (sEMG) signals offer information on the natural control of muscle contraction but struggle to identify temporal pattern parameters for several degrees of motion of voluntary hand movements. The complex nature of these signals renders the movement prediction task difficult; therefore, feature extraction and selection algorithms are a natural choice to transform time domain data into a new space domain to enhance recognition. The purpose of this work was to conduct an analysis of a former forearm sEMG database to improve a model to classify 15 defined hand movements. A simpler classification model was created from algorithms, such as naive Bayes (NB), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA). Also, novel preprocessing of the EMG signal data was employed and modeled the movement in virtual simulation software. In the preprocessing, outliers were eliminated, and a scatter matrix algorithm was used to transform the data into a new space to increase the differentiation between distinct classes. The processing window was 62.5 ms to generate a classification and integrate one video frame movement. Experiments yielded promising results, achieving a 93.76% recognition rate in an independent test set. The biomechanical wrist model available in OpenSim was completed by adding the missing degrees of freedom of the fingers to simulate the movement generated from the proposed classification model. The sequence of movement was converted to a biomechanical model and constructed into a video object with the potential for real time use.
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References
Liu, Y., Ning, Y., He, J., Li, S., Zhou, P., Zhang, Y.: Internal muscle activity imaging from multi-channel surface EMG recordings: a validation study. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, pp. 3559–3561 (2014)
Sun, Y., Li, C., Li, G., Jiang, G., Jiang, D., Liu, H., Zheng, Z., Shu, W.: Gesture recognition based on kinect and sEMG signal fusion. Mob. Netw. Appl. 23(4), 797–805 (2018)
Wahid, M.F., Tafreshi, R., Al-Sowaidi, M., Langari, R.: Subject-independent hand gesture recognition using normalization and machine learning algorithms. J. Comput. Sci. 27, 69–76 (2018)
He, J., Sheng, X., Zhu, X., Jiang, C., Jiang, N.: Spatial information enhances myoelectric control performance with only two channels. IEEE Trans. Industr. Inf. PP(c), 1 (2018)
Saikia, A., Kakoty, N.M., Phuka, N., Balakrisxnan, M., Sahai, N., Paul, S., Bhatia, D.: Combination of EMG features and stability index for finger movements recognition. Proc. Comput. Sci. 133, 92–98 (2018)
Resnik, L., Huang, H.H., Winslow, A., Crouch, D.L., Zhang, F., Wolk, N.: Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control. J. NeuroEng. Rehabil. 15(1), 113 (2018)
Feng, N., Shi, Q., Wang, H., Gong, J., Liu, C., Lu, Z.: A soft robotic hand: design, analysis, sEMG control, and experiment. Int. J. Adv. Manuf. Technol. 97(1–4), 319–333 (2018)
Menegaldo, L.L.: Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters. Biol. Cybern. 111(5–6), 335–346 (2017)
Kim, M., Chung, W.K.: Spatial sEMG pattern-based finger motion estimation in a small area using a microneedle-based high-density interface. IEEE Robot. Autom. Lett. 3(1), 234–241 (2017)
Sheibani, A., Pourmina, M.A.: Study and analysis of EMG signal and its application in controlling the movement of a prosthetic limb. Health Technol. 6(4), 277–284 (2016)
Paleari, M., Di Girolamo, M., Celadon, N., Favetto, A., Ariano, P.: On optimal electrode conguration to estimate hand movements from forearm surface electromyography. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2015, pp. 6086–6089 (2015)
Castro, M.C., Arjunan, S.P., Kumar, D.K.: Selection of suitable hand gestures for reliable myoelectric human computer interface. BioMed. Eng. Online 14(1), 111 (2015)
Kumar, D.K., Arjunan, S.P., Singh, V.P.: Towards identification of finger flexions using single channel surface electromyography – able bodied and amputee subjects. J. NeuroEng. Rehabil. 10(1), 17 (2013)
Jeong, E.C., Kim, S.J., Song, Y.R., Lee, S.M.: Comparison of wrist motion classification methods using surface electromyogram. J. Cent. South Univ. 20(4), 960–968 (2013)
Daley, H., Englehart, K., Hargrove, L., Kuruganti, U.: High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control. J. Electromyogr. Kinesiol. 22(3), 478–484 (2012)
Dennis, T., He, H., Todd, K.: Study of stability of time-domain features for electromyographic pattern recognition. J. NeuroEng. Rehabil. 7, 21 (2010)
Arjunan, S.P., Kumar, D.K.: Decoding subtle forearm exions using fractal features of surface electromyogram from single and multiple sensors. J. NeuroEng. Rehabil. 7(1), 110 (2010)
Walbran, S.H., Calius, E.P., Dunlop, G.R., Anderson, I.A.: A technique for optimizing electrode placement for electromyographic control of prostheses. In: Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, pp. 1331–1334 (2009)
Ryait, H.S., Arora, A.S., Agarwal, R.: Study of issues in the development of surface EMG controlled human hand. J. Mater. Sci. Mater. Med. 20(Suppl. 1), 107 (2009)
Smith, R.J., Tenore, F., Huberdeau, D., Cummings, R.E., Thakor, N.V.: Continuous decoding of nger position from surface EMG signals for the control of powered prostheses. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 197–200 (2008)
Christov, I., Raikova, R., Angelova, S.: Separation of electrocardiographic from electromyographic signals using dynamic ltration. Med. Eng. Phys. 57, 110 (2018)
Veer, K.: A flexible approach for segregating physiological signals. Meas. J. Int. Meas. Confed. 87, 21–26 (2016)
Willigenburg, N.W., Daertshofer, A., Kingma, I., van Dieën, J.H.: Removing ECG contamination from EMG recordings: a comparison of ICA-based and other filtering procedures. J. Electromyogr. Kinesiol. 22(3), 485–493 (2012)
Marque, C., Bisch, C., Dantas, R., Elayoubi, S., Brosse, V., Pérot, C.: Adaptive filtering for ECG rejection from surface EMG recordings. J. Electromyogr. Kinesiol. 15(3), 310–315 (2005)
Kumar, D.K., Melaku, A.: Electrode distance and magnitude of SEMG. In: Proceedings of the Second Joint Engineering in Medicine and Biology, 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, vol. 3, pp. 2477–2480 (2002)
Baratta, R.V., Solomonow, M., Zhou, B.H., Zhu, M.: Methods to reduce the variability of EMG power spectrum estimates. J. Electromyogr. Kinesiol. 8(5), 279–285 (1998)
Repository of Dr. Rmi N. Khushaba online. http://www.rami-khushaba.com/electromyogram-emg-repository.html
Khushaba, R.N., Kodagoda, S.: Electromyogram (EMG) feature reduction using mutual components analysis for multifunction prosthetic fingers control, pp. 1534–1539. IEEE, December 2012 https://doi.org/10.1109/icarcv.2012.6485374
Lei, M., Wang, Z., Feng, Z.: Detecting nonlinearity of action surface EMG signal. Phys. Lett. Sect. A: Gener. Atom. Solid State Phys. 290(5–6), 297–303 (2001). https://doi.org/10.1016/s0375-9601(01)00668-5
Phinyomark, A., Phukpattaranont, P., Limsakul, C.: Feature reduction and selection for EMG signal classification. Expert Syst. Appl. 6(8), 7420–7431 (2012). https://doi.org/10.1016/j.eswa.2012.01.102
Havlik, J., Uhlir, J., Horcik, Z.: Comparison of k-means and bayes classifiers for human body motions classification. In: IFMBE Proceedings. FEE CTU (2009)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179–188 (1936)
Gutierrez-Osuna, R.: CSCE 666 Pattern Analysis—CSE@TAMU. http://courses.cs.tamu.edu/rgutier/csce666 f13/
Lisboa, P.J.G., Ellis, I.O.: Cluster-based visualisation with scatter matrices. Pattern Recogn. Lett. 29, 1814–1823 (2008)
Seth, A., Habib, A.: SimTK Opensim, National Institutes of Health (NIH). https://simtk.org/projects/opensim
Oskoei, M.A., Hu, H.: Myoelectric control systems - to survey. Biomed. Signal Process. Control 2(4), 275–294 (2007). https://doi.org/10.1016/j.bspc.2007.07.009
Amezquita-Garcia, J.A., Bravo-Zanoguera, M.E.: Bioingenieria, UABC. https://simtk.org/projects/moving-fingers
Gonzalez, R.V., Buchanan, T.S., Delp, S.L.: How muscle architecture and moment arms affect wrist flexion-extension moments. J. Biomech. 30, 705–712 (1997)
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Amezquita-Garcia, J.A., Bravo-Zanoguera, M.E., González-Navarro, F.F., Lopez-Avitia, R. (2020). Hand Movement Detection from Surface Electromyography Signals by Machine Learning Techniques. In: González Díaz, C., et al. VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering. CLAIB 2019. IFMBE Proceedings, vol 75. Springer, Cham. https://doi.org/10.1007/978-3-030-30648-9_29
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