Evaluation of Electromyogram Time Characteristics of the Wrist Functional Movements for Intuitive Control of Bionic Prosthesis
Background. Time evaluation features of characteristics (TDF-characteristics) of a surface electromyogram when performing the palm functional movements and the possibility of their implementation in the bionic prosthetic limb intuitive control system.
Objective. The aim of the paper is to develop the analytical model for evaluating the TDF-characteristics of myographic signals for basic functional movements of the patient’s wrist and fingers, as well as studying the possibilities of its implementation as a basis for non-parametric method of classification.
Methods. A one-channel microcontroller based information measuring system was created for the registration of a surface electromyogram. The analytical model for evaluating its TDF-characteristics based on the use of variance and trapezoidal integral features was developed.
Results. The developed model was tested using the method of k-NN classification of the measured signal in the analysis of normalized and non-normalized data of recognition of the fingers’ functional movements. The accuracy of movement classification was 86.11 % and is acceptable for use in the development of methods for automatic control in the control systems of bionic prosthetics of the upper limbs or fingers.
Conclusions. The analytical model for evaluating the TDF-characteristics of myographic signals for the wrist basic functional movements based on one-channel measuring system and a simple method of machine learning technique were proposed in the study. The proposed model is effective when working with a small set of signal characteristics and a limited amount of input data, and its classification accuracy can be increased by using a wider sample of data for training.
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R.G.E. Clement et al., “Bionic prosthetic hands: A review of present technology and future aspirations”, Surgeon, vol. 9, no. 6, pp. 336–340, 2011. doi: 10.1016/j.surge.2011.06.001
C. Pedreira et al., “Neural prostheses: linking brain signals to prosthetic devices”, in Proc. ICROS-SICE Int. Joint Conf., 2009, pp. 1–6.
S. Micera et al., “Control of hand prostheses using peripheral information”, IEEE Rev. Biomed. Eng., vol. 3, pp. 48–68, 2010. doi: 10.1109/RBME.2010.2085429
C.A. Chestek et al., “Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas”, J. Neural Eng., vol. 10, no. 2, pp. 1–11, 2013. doi: 10.1088/1741-2560/10/2/026002
C. Castellini, “Neuro-robotics,” vol. 2, pp. 37–58, 2014. doi: 10.1007/978-94-017-8932-5
J. Shi et al., “Feasibility of controlling prosthetic hand using sonomyography signal in real time: preliminary study”, J. Rehabil. Res. Dev., vol. 47, no. 2, pp. 87–98, 2010. doi: 10.1682/JRRD.2009.03.0031
A. Fougner et al., “Control of upper limb prostheses: Terminology and proportional myoelectric control – A review”, vol. 20, no. 5, pp. 663–677, 2012. doi: 10.1109/TNSRE.2012.2196711
N. Carbonaro et al., “An innovative multisensor controlled prosthetic hand”, in Proc. XIII Mediterranean Conf. Med. Bio. Eng. Computing, 2013, pp. 93–96. doi: 10.1007/978-3-319-00846-2_23
A. Dobrowolski et al., “Analiza widmowa potencjałów jednostek ruchowych”, Biuletyn WAT, vol. 56, no. 1, pp. 83–97, 2007.
M. Bezuhlyi et al., “Creation of the classification of the biotechnical objects means monitoring”, Visnyk NTUU “KPI”. Ser. Pryladobuduvannia, no. 26, pp. 131–138, 2003 (in Ukrainian).
M. Zecca et al., “Control of multifunctional prosthetic hands by processing the electromyographic signal”, Crit. Rev. Biomed. Eng., vol. 30, no. 4–6, pp. 459–485, 2002. doi: 10.1615/CritRevBiomedEng.v30.i456.80
L. Liu et al., “Electromyogram whitening for improved classification accuracy in upper limb prosthesis control”, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 21, no. 5, pp. 767–774, 2013. doi: 10.1109/TNSRE.2013.2243470
R.H. Chowdhury et al., “Surface electromyography signal processing and classification techniques”, Sensors (Basel), vol. 13, no. 9, pp. 12431–12466, 2013. doi: 10.3390/s130912431
A. Balbinot et al., “Decoding arm movements by myoelectric signal and artificial neural networks”, Intell. Control Autom., vol. 2013, no. 4, pp. 87–93, 2013. doi: 10.4236/ica.2013.41012
F. Orabona et al., “Model adaptation with least-squares SVM for adaptive hand prosthetics”, in Proc. IEEE Int. Conf. Robot. Autom., 2009, pp. 2897–2903. doi: 10.1109/ROBOT.2009.5152247
B. Karlık, “Machine learning algorithms for characterization of emg signals”, Int. J. Inf. Electron. Eng., vol. 4, no. 3, pp. 189–194, 2014. doi: 10.7763/IJIEE.2014.V4.433
F.H.Y. Chan et al., “Fuzzy EMG classification for prosthesis control”, IEEE Trans. Rehabil. Eng., vol. 8, no. 3, pp. 305–311, 2000. doi: 10.1109/86.867872
S.H. Park and S.P. Lee, “EMG pattern recognition based on artificial intelligence techniques”, IEEE Trans. Rehabil. Eng., vol. 6, no. 4, pp. 400–405, 1998. doi: 10.1109/86.736154
S. El-Khoury et al., “EMG-based learning approach for estimating wrist motion”, in Proc. 2015 37th Annual Int. Conf. IEEE Eng. Med. Bio. Soc. (EMBC), 2015, pp. 6732–6735. doi: 10.1109/EMBC.2015.7319938
P. Shenoy et al., “Online electromyographic control of a robotic prosthesis”, IEEE Trans. Biomed. Eng., vol. 55, no. 3, pp. 1128–1135, 2008. doi: 10.1109/TBME.2007.909536
A. Gmerek, “Adaptacyjny system sterowania protezą ręki wykorzystujący elektromiografię powierzchniową”, Prace Naukowe Politechniki Warszawskiej. Elektronika, vol. 175, no. 1, pp. 99–110, 2010.
J.M. Hahne et al., “Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control”, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 2, pp. 269–279, 2014. doi: 10.1109/TNSRE.2014.2305520
M. Haris et al., “EMG signal based finger movement recognition for prosthetic hand control”, in Proc. 2015 Communication, Control and Intelligent Systems (CCIS), 2015, pp. 194–198. doi: 10.1109/CCIntelS.2015.7437907
K. Vonsevych et al., “Information-measuring system of myograph of bionic limb prosthesis”, Perspektyvni Tekhnolohii ta Prilady, vol. 10, no. 1, pp. 32–37, 2017 (in Ukrainian).
C. Altın and O. Er, “Comparison of different time and frequency domain feature extraction methods on elbow gesture’s EMG”, Eur. J. Interdiscip. Stud., vol. 5, no. 1, pp. 35–44, 2016.
S. Raschka and M. Vahid, Python Machine Learning Second Edition, 2nd ed. Birmingham B3 2PB, UK: Packt Publishing Ltd., 2017.
B. Charulatha et al., “A Comparative study of different distance metrics that can be used in Fuzzy Clustering Algorithms”, Ijettcs. Org, vol. 2013, 2013.
C.P. Robinson et al., “Pattern classification of hand movements using time domain features of electromyography”, in Proc. 4th Int. Conf. Movement Computing, 2017, pp. 1–6. doi: 10.1145/3077981.3078031
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