Abstract
The objective of this work is to automatically identify basic hand movements: Opening, Closing, Bending, Extension, Pronation and Supination, including the Resting condition. Feature extraction was implemented making use of three approaches: time, frequency and time-frequency domains, obtaining the characteristics Mean Absolute Value (MAV), Root Mean Square (RMS), Wave Length (WL), Autoregressive Coefficients (AR) and Discrete Wavelet Transform (DWT). Principal Component Analysis (PCA) was applied for dimensionality reduction and classification was performed using Linear Discriminant Analysis (LDA). As a result it was possible to identify the movements with success rates that reached 92% with the hybrid vectors conformed by the coefficients MAV, RMS and AR.
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References
Blana, D., Krasoulis, A., Nazarpour, K., Chadwick, E.: Control of a robotic hand with an EMG-driven, real-time biomechanical computer model. In: 8th World Congress of Biomechanics. Newcastle University (2018)
Bulea, T.C., Kilicarslan, A., Ozdemir, R., Paloski, W.H., Contreras-Vidal, J.L.: Simultaneous scalp electroencephalography (EEG), electromyography(EMG), and whole-body segmental inertial recording for multi-modal neural decoding. J. Vis. Exp. 26, e50602 (2013). https://doi.org/10.3791/50602. http://www.jove.com/video/50602
Chan, A.D.C., Green, G.C.: Myoelectric control development toolbox. In: 30th Conference on Canadian Medical Biological Engineering Society, Toronto, Canada, pp. 1–4 (2007). http://www.sce.carleton.ca/faculty/chan/matlab/myoelectriccontroldevelopmenttoolbox.pdf
Chowdhury, R.H., Bin, M., Reaz, I., Alauddin, M., Ali, M., Bakar, A.A.A.: Surface electromyography signal processing and classification techniques. Sensors 13, 12431–12466 (2013). https://doi.org/10.3390/s130912431. http://www.mdpi.com/journal/sensors
Graupe, D., Magnussen, J., Beex, A.: A microprocessor system for multifunctional control of upper-limb prostheses via myoelectric signal identification. IEEE Trans. Autom. Control 23(4), 538–544 (1978). https://doi.org/10.1109/TAC.1978.1101783
Hargrove, L.J., Li, G., Englehart, K.B., Hudgins, B.S.: Principal components analysis preprocessing for improved classification accuracies in pattern-recognition-based myoelectric control. IEEE Trans. Biomed. Eng. 56(5), 1407–1414 (2009). https://doi.org/10.1109/TBME.2008.2008171. http://ieeexplore.ieee.org
Phinyomark, A., Hu, H., Phukpattaranont, P., Limsakul, C.: Application of linear discriminant analysis in dimensionality reduction for hand motion classification. Meas. Sci. Rev. 12(3), 82–89 (2012)
Rouillard, J., Duprès, A., Cabestaing, F., Leclercq, S., Bekaert, M.H., Piau, C., Vannobel, J.M., Lecocq, C.: Hybrid BCI coupling EEG and EMG for severe motor disabilities. Procedia Manuf. 3(Ahfe), 29–36 (2015). https://doi.org/10.1016/j.promfg.2015.07.104. http://dx.doi.org/10.1016/j.promfg.2015.07.104
Saikia, A., Kakoty, N.M., Phukan, N., Balakrishnan, M., Sahai, N., Paul, S., Bhatia, D.: Combination of EMG features and stability index for finger movements recognition. Procedia Comput. Sci. 133, 92–98 (2018)
Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemometr. Intell. Lab. Syst. 2(1), 37–52 (1987). https://doi.org/10.1016/0169-7439(87)80084-9. http://www.sciencedirect.com/science/article/pii/0169743987800849. Proceedings of the Multivariate Statistical Workshop for Geologists and Geochemists
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Hidalgo Torres, L.A., San MartÃn Reyes, Y., Chailloux Peguero, J.D. (2020). Capture of the Voluntary Motor Intention from the Electromyography Signal. In: González DÃaz, 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_4
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