Classification of the Angular Position During Wrist Flexion-Extension Based on EMG Signals
To evaluate a group of features in a myoelectric pattern recognition algorithm to differentiate between five angular positions of the wrist during flexion-extension movements.Materials and Methods: An experimental configuration was made to capture the EMG and wrist joint angle related to flexion-ext...
- Autores:
-
Fajardo Perdomo, María Alexandra
Guardo Gómez, Verónica
Orjuela Cañón, Álvaro David
Ruíz Olaya, Andrés Felipe
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Escuela Colombiana de Ingeniería Julio Garavito
- Repositorio:
- Repositorio Institucional ECI
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.escuelaing.edu.co:001/3247
- Acceso en línea:
- https://repositorio.escuelaing.edu.co/handle/001/3247
https://repositorio.escuelaing.edu.co/
- Palabra clave:
- Medicina física
Medicine physical
Articulaciones
Joints
Biomecánica
Biomechanics
Intencionalidad de movimiento
Señales de electromiografía
Reconocimiento de patrones
Técnicas de aprendizaje automático
Redes neuronales artificiales
Movement intent
Electromyography signals
Pattern recognition
Machine learning techniques
artificial neural networks
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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| dc.title.eng.fl_str_mv |
Classification of the Angular Position During Wrist Flexion-Extension Based on EMG Signals |
| dc.title.alternative.spa.fl_str_mv |
Clasificación de la posición angular en flexoextensión de la muñeca a partir de señales EMG |
| title |
Classification of the Angular Position During Wrist Flexion-Extension Based on EMG Signals |
| spellingShingle |
Classification of the Angular Position During Wrist Flexion-Extension Based on EMG Signals Medicina física Medicine physical Articulaciones Joints Biomecánica Biomechanics Intencionalidad de movimiento Señales de electromiografía Reconocimiento de patrones Técnicas de aprendizaje automático Redes neuronales artificiales Movement intent Electromyography signals Pattern recognition Machine learning techniques artificial neural networks |
| title_short |
Classification of the Angular Position During Wrist Flexion-Extension Based on EMG Signals |
| title_full |
Classification of the Angular Position During Wrist Flexion-Extension Based on EMG Signals |
| title_fullStr |
Classification of the Angular Position During Wrist Flexion-Extension Based on EMG Signals |
| title_full_unstemmed |
Classification of the Angular Position During Wrist Flexion-Extension Based on EMG Signals |
| title_sort |
Classification of the Angular Position During Wrist Flexion-Extension Based on EMG Signals |
| dc.creator.fl_str_mv |
Fajardo Perdomo, María Alexandra Guardo Gómez, Verónica Orjuela Cañón, Álvaro David Ruíz Olaya, Andrés Felipe |
| dc.contributor.author.none.fl_str_mv |
Fajardo Perdomo, María Alexandra Guardo Gómez, Verónica Orjuela Cañón, Álvaro David Ruíz Olaya, Andrés Felipe |
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GiBiome |
| dc.subject.armarc.none.fl_str_mv |
Medicina física Medicine physical Articulaciones Joints Biomecánica Biomechanics |
| topic |
Medicina física Medicine physical Articulaciones Joints Biomecánica Biomechanics Intencionalidad de movimiento Señales de electromiografía Reconocimiento de patrones Técnicas de aprendizaje automático Redes neuronales artificiales Movement intent Electromyography signals Pattern recognition Machine learning techniques artificial neural networks |
| dc.subject.proposal.spa.fl_str_mv |
Intencionalidad de movimiento Señales de electromiografía Reconocimiento de patrones Técnicas de aprendizaje automático Redes neuronales artificiales |
| dc.subject.proposal.eng.fl_str_mv |
Movement intent Electromyography signals Pattern recognition Machine learning techniques artificial neural networks |
| description |
To evaluate a group of features in a myoelectric pattern recognition algorithm to differentiate between five angular positions of the wrist during flexion-extension movements.Materials and Methods: An experimental configuration was made to capture the EMG and wrist joint angle related to flexion-extension movements. After that, a myoelectric pattern recognition algorithm based on a multilayer perceptron artificial neural network (ANN) was implemented. Three different groups were used: Time domain characteristics, autoregressive (AR) model parameters, and representation of time frequency using Wavelet transform (WT). Results and Discussion: The experimental results of 10 healthy subjects indicate that the coefficients of the AR models offer the best parameters for classification, with a differentiation rate of 78 % for the five angular positions studied. The combination of frequency and time frequency resulted in a differentiation rate that reached 82 %. |
| publishDate |
2021 |
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2021 |
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2024-09-05T17:27:11Z |
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2024-09-05T17:27:11Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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0123-2126 |
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https://repositorio.escuelaing.edu.co/handle/001/3247 |
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2011-2769 |
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Universidad Escuela Colombiana de Ingeniería Julio Garavito |
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Repositorio Digital |
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https://repositorio.escuelaing.edu.co/ |
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0123-2126 2011-2769 Universidad Escuela Colombiana de Ingeniería Julio Garavito Repositorio Digital |
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eng |
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eng |
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Vol. 25 (2021) |
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21 |
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1 |
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25 |
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Ingenieria y Universidad |
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R. Merletti and P. A. Parker, Electromyography: Physiology, Engineering, and Non-invasive Applications. Hoboken, NJ: John Wiley & Sons, 2004 C. Germany, “A low cost signal acquisition board design for myopathy’s EMG database construction,” in 13th Int. Multi-Conf. Syst. Signals Devices (SSD), 2016, pp. 274–279. doi: 10.1109/SSD.2016.7473767 S. Kalwa, “Neuromuscular disease classification based on discrete wavelet transform of dominant motor unit action potential of EMG signal,” in 2015 Int. Conf. Inf. Process. (ICIP), 2015. doi: 10.1109/INFOP.2015.7489474 A. A. Ali, A. Albarahany, and L. Quan, “EMG signals detection technique in voluntary muscle movement,” in 6th Int. Conf. New Trends Inf. Sci. Serv. Sci. Data Min. (ISSDM), Taipei, Taiwan, 2012, pp. 738–742. Available: https://ieeexplore.ieee.org/document/6528730 S. Thongpanja, A. Phinyomark, F. Quaine, Y. Laurillau, C. Limsakul, and P. Phukpattaranont, “Probability density functions of stationary surface EMG signals in noisy environments,” IEEE Trans. Instrum. Meas., 2016, vol. 65, no. 7, pp. 1547–1557. Available: https://ieeexplore.ieee.org/document/7438830 F. Ortes, D. Karabulut, and Y. Z. Arslan, “General perspectives on electromyography signal features and classifiers used for control of human arm prosthetics,” in Adv. Methodol. Technol. Eng. Environ. Sci. Hershey, PA: IGI Global, 2019, pp. 1–17. https://doi.org/10.4018/978-1-5225-7359-3.ch001 C. Garg, “Development of a software module for feature extraction and classification of EMG signals,” in Conf. Commun. Control Intell. Syst. (CCIS), 2015, pp. 250–254 C. J. G. Duque, L. D. Muñoz, J. G. Mejía, and E. D. Trejos, “Discrete wavelet transform and k-nn classification in EMG signals for diagnosis of neuromuscular disorders,” in Image, Signal Process. Artificial Vision (STSIVA), 2014 XIX Sym., pp. 1–5. Available: https://ieeexplore.ieee.org/document/7010171 M. Jahan, M. Manas, B. B. Sharma, and B. B. Gogoi, “Feature extraction and pattern recognition of EMG-based signal for hand movements,” in Adv. Comput. Commun. (ISACC), 2015 Int. Sym., pp. 49– 52. Available: https://ieeexplore.ieee.org/document/7377314 C. Sapsanis, G. Georgoulas, and A. Tzes, “EMG based classification of basic hand movements based on time-frequency features,” in Control Autom. (MED), 2013 21st Mediterranean Conf., pp. 716–722. Available: https://ieeexplore.ieee.org/document/6608802 L. Pan, D. Zhang, X. Sheng, and X. Zhu, “Residuals of autoregressive model providing additional information for feature extraction of pattern recognition-based myoelectric control,” in Eng. Med. Biol. S. (EMBC), 2015 37th Annu. Int. Conf. IEEE, pp. 7270–7273. Available: https://ieeexplore.ieee.org/document/7320070 I. Haider, M. Shahbaz, M. Abdullah, and M. Nazim, “Feature Extraction for Identification of Extension and Flexion Movement of Wrist using EMG Signals,” in Electr. Comput. Eng. (CCECE), 2015 IEEE 28th Can. Conf., pp. 792–795. Available: https://ieeexplore.ieee.org/document/7129375 J. Liu, “Feature dimensionality reduction for myoelectric pattern recognition: A comparison study of feature selection and feature projection methods,” Med. Eng. Phys., vol. 36, no. 12, pp. 1716–1720, 2014. Available: https://doi.org/10.1016/j.medengphy.2014.09.011 G. Purushothaman and R. Vikas, “Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals,” Australas. Phys. Eng. Sci. Med., vol. 41, no. 2, pp. 549–559, 2018. doi: 10.1007/s13246-018-0646-7 N. Nazmi, M. Abdul Rahman, S.-I. Yamamoto, S. Ahmad, H. Zamzuri, and S. Mazlan, “A review of classification techniques of EMG signals during isotonic and isometric contractions,” Sensors, vol. 16, no. 8, p. 1304, 2016. doi: 10.3390/s16081304 M.-K. Kim, M. Kim, E. Oh, and S.-P. Kim, “A review on the computational methods for emotional state estimation from the human EEG,” Comput. Math. Methods Med., vol. 2013, 2013. doi: 10.1155/2013/573734 R. M. Rangayyan, Biomedical Signal Analysis. New Jersey: John Wiley & Sons, 2015. P. Onsy, A. Alim, M. Moselhy, and E. F. Mroueh, “EMG Signal Processing and Diagnostic of Muscle Diseases,” in 2012 6th Int. Conf. New Trends Inf. Sci. Serv. Sci. Data Mining (ISSDM), pp. 1–6. Available: https://ieeexplore.ieee.org/document/6462866 M. H. Jali, T. A. Izzuddin, Z. H. Bohari, H. I. Jaafar, and M. N. M. Nasir, “Pattern recognition of EMG signal during load lifting using Artificial Neural Network (ANN),” in Control Syst., Comput. Eng. (ICCSCE), 2015 IEEE Int. Conf., pp. 172–177. Available: https://ieeexplore.ieee.org/abstract/document/7482179 M. Sood, “A novel module based approach for classifying epileptic seizures using EEG signals,” in Ind. Inform. Comput. Syst. (CIICS), 2016 Int. Conf., pp. 3–7. Available: https://ieeexplore.ieee.org/document/7462406 N. Jiang, J. L. G. Vest-Nielsen, S. Muceli, and D. Farina, “EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees,” J. Neuroeng. Rehabil., vol. 9, no. 1, p. 42, 2012. doi: 10.1186/1743-0003-9-42 X. Zhang, X. Li, O. W. Samuel, Z. Huang, P. Fang, and G. Li, “Improving the robustness of electromyogram-pattern recognition for prosthetic control by a postprocessing strategy,” Front. Neurorobot., vol. 11, p. 51, 2017. DOI: 10.3389/fnbot.2017.00051 R. Merletti and D. Farina, Surface Electromyography: Physiology, Engineering, and Applications. New Jersey: John Wiley & Sons, 2016. D. Stegeman and H. Hermens, “Standards for surface electromyography: The European project Surface EMG for non-invasive assessment of muscles (SENIAM),” Enschede Roessingh Res. Dev., pp. 108– 112, 2007. L. Logesparan, A. J. Casson, and E. Rodriguez-Villegas, “Assessing the impact of signal normalization: Preliminary results on epileptic seizure detection,” in Eng. Med. Biol. Soc., EMBC, 2011 Ann. Int. Conf. IEEE, pp. 1439–1442. doi: 10.1109/IEMBS.2011.6090356 J. Wang, L. Tang, and J. E. Bronlund, “A pattern recognition system for myoelectric based prosthesis hand control,” in. Ind. Electron. Appl. (ICIEA), 2015 IEEE 10th Conf., pp. 830–834. doi: 10.1109/ICIEA.2015.7334225 A. López-Delis, A. F. Ruiz-Olaya, T. Freire-Bastos, and D. Delisle-Rodríguez, “A comparison of myoelectric pattern recognition methods to control an upper limb active exoskeleton,” in Iberoamer. Congr. Pattern Recogn., 2013, pp. 100–107. Available: https://link.springer.com/content/pdf/10.1007%2F978-3-642-41827-3_13.pdf S. Negi, Y. Kumar, and V. M. Mishra, “Feature extraction and classification for EMG signals using linear discriminant analysis,” in Adv. Comput. Commun. Autom. (ICACCA) Int. Conf., 2016, pp. 1–6. doi: 10.1109/ICACCAF.2016.7748960 A. López-Delis and A. F. Ruiz Olaya, “Métodos computacionales para el reconocimiento de patrones mioeléctricos en el control de exoesqueletos robóticos: una revisión,” INGE@ UAN-Tenden. Ing., vol. 3, no. 5, 2013. Available: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiwzrLBlMv qAhXFhOAKHZPXAcwQFjAAegQIBBAB&url=http%3A%2F%2Frevistas.uan.edu.co%2Findex.ph p%2Fingeuan%2Farticle%2Fdownload%2F262%2F204&usg=AOvVaw3PTVXBDATNfynSdIuzJGc T M. A. Oskoei and H. Hu, “Myoelectric control systems: A survey,” Biomed. Signal Process. Control, vol. 2, no. 4, pp. 275–294, 2007. Available: https://doi.org/10.1016/j.bspc.2007.07.009 Z. Arief, I. A. Sulistijono, and R. A. Ardiansyah, “Comparison of five time series EMG features extractions using Myo Armband,” in Electron. Symp. (IES), 2015 Int., pp. 11–14. Available: https://ieeexplore.ieee.org/document/7380805 M. Haris, “EMG signal based finger movement recognition for prosthetic hand control,” in Commun. Control Intell. Syst. (CCIS), 2015. Available: https://ieeexplore.ieee.org/document/7437907 B. S. Zheng, M. Murugappan, S. Yaacob, and S. Murugappan, “Human emotional stress analysis through time domain electromyogram features,” in Indu. Electron. (ISIE), IEEE Int. Symp., 2013, pp. 172–177. Available: https://ieeexplore.ieee.org/document/6738989 S. V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction. New Jersey John Wiley & Sons, 2008. L. Ljung, System Identification: Theory for the User, 2nd ed. Up. Saddle River, NJ: PTR Prentice-Hall, 1999 A. Subasi, “Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines,” Comput. Biol. Med., vol. 42, no. 8, pp. 806–815, 2012. doi: 10.1016/j.compbiomed.2012.06.004 K. Englehart, B. Hudgins, P. A. Parker, and M. Stevenson, “Classification of the myoelectric signal using time-frequency based representations,” Med. Eng. Phys., vol. 21, no. 6, pp. 431–438, 1999. Available: https://doi.org/10.1016/S1350-4533(99)00066-1 G. Strang and T. Nguyen, Wavelets and Filter Banks. Wellesley: Cambridge Press, 1996 T. Kamali, R. Boostani, and H. Parsaei, “A multi-classifier approach to MUAP classification for diagnosis of neuromuscular disorders,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 1, pp. 191– 200, 2014. doi: 10.1109/TNSRE.2013.2291322 R. Kohavi, “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,” in Int. Joint Conf. Artificial Intell., 1995, pp. 1137–1143. Available: http://ai.stanford.edu/~ronnyk/accEst.pdf S. Haykin, Neural Networks and Learning Machines. New Jersey: Prentice Hall, 2009. A. Akhtar, L. J. Hargrove, and T. Bretl, “Prediction of distal arm joint angles from EMG and shoulder orientation for prosthesis control,” in Eng. Med. Biol. Soc. (EMBC), 2012 Annu. Int. Conf. IEEE, pp. 4160–4163. Available: doi: 10.1109/EMBC.2012.6346883 R. S. Naoum, N. A. Abid, and Z. N. Al-Sultani, “An enhanced resilient backpropagation artificial neural network for intrusion detection system,” Int. J. Comput. Sci. Netw. Secur., vol. 12, no. 3, p. 11, 2012. S. Abe, Support Vector Machines for Pattern Classification, vol. 2. Berlín: Springer, 2005 J. Yousefi and A. Hamilton-Wright, “Characterizing EMG data using machine-learning tools,” Comput. Biol. Med., vol. 51, pp. 1–13, 2014. doi: 10.1016/j.compbiomed.2014.04.018 L. R. Quitadamo et al., “Support vector machines to detect physiological patterns for EEG and EMGbased human: Computer interaction; A review,” J. Neural Eng., vol. 14, no. 1, p. 11001, 2017. doi: 10.1088/1741-2552/14/1/011001 A. Alkan and M. Günay, “Identification of EMG signals using discriminant analysis and SVM classifier,” Expert Syst. Appl., vol. 39, no. 1, pp. 44–47, 2012. doi: 10.1016/j.eswa.2011.06.043 P. Konar and P. Chattopadhyay, “Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs),” Appl. Soft Comput., vol. 11, no. 6, pp. 4203–4211, 2011. doi: 10.1016/j.asoc.2011.03.014 G. L. Prajapati and A. Patle, “On performing classification using SVM with radial basis and polynomial kernel functions,” in 2010 3rd Int. Conf. Emerging Trends Eng. Technol., 2010, pp. 512–515. Available: https://ieeexplore.ieee.org/document/5698379 P. A. Kaplanis, C. S. Pattichis, D. Zazula, and others, “Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders,” Med. Biol. Eng. Comput., vol. 48, no. 8, pp. 773–781, 2010. doi: 10.1007/s11517-010-0629-7 G. R. Naik, D. K. Kumar, and others, “Twin SVM for gesture classification using the surface electromyogram,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 2, pp. 301–308, 2009. Available: https://ieeexplore.ieee.org/document/5353702 X. Li, Q. Huang, J. Zhu, W. Sun, and H. She, “A novel proportional and simultaneous control method for prosthetic hand,” J. Mech. Med. Biol., vol. 17, no. 08, p. 1750120, 2017. doi: 10.1142/S0219519417501202 H. Shim, H. An, S. Lee, E. H. Lee, H. Min, and S. Lee, “EMG pattern classification by split and merge deep belief network,” Symmetry, vol. 8, no. 12, p. 148, 2016. doi: 10.3390/sym8120148 A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “Feature reduction and selection for EMG signal classification,” Expert Syst. Appl., vol. 39, no. 8, pp. 7420–7431, 2012. doi: 10.1016/j.eswa.2012.01.102 |
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Fajardo Perdomo, María Alexandra4c17e04021886ebdc6761a9512b32fe0Guardo Gómez, Verónica026080a14694c28888fa8f3c53796857Orjuela Cañón, Álvaro Davidc3f16ff4f9857ef9a57193e86d4355deRuíz Olaya, Andrés Felipe4c4acbec0eb9cad5eb345b7900107faeGiBiome2024-09-05T17:27:11Z2024-09-05T17:27:11Z20210123-2126https://repositorio.escuelaing.edu.co/handle/001/32472011-2769Universidad Escuela Colombiana de Ingeniería Julio GaravitoRepositorio Digitalhttps://repositorio.escuelaing.edu.co/To evaluate a group of features in a myoelectric pattern recognition algorithm to differentiate between five angular positions of the wrist during flexion-extension movements.Materials and Methods: An experimental configuration was made to capture the EMG and wrist joint angle related to flexion-extension movements. After that, a myoelectric pattern recognition algorithm based on a multilayer perceptron artificial neural network (ANN) was implemented. Three different groups were used: Time domain characteristics, autoregressive (AR) model parameters, and representation of time frequency using Wavelet transform (WT). Results and Discussion: The experimental results of 10 healthy subjects indicate that the coefficients of the AR models offer the best parameters for classification, with a differentiation rate of 78 % for the five angular positions studied. The combination of frequency and time frequency resulted in a differentiation rate that reached 82 %.Evaluar un grupo de características en un algoritmo de reconocimiento de patrones mioeléctricos para discriminar cinco posiciones angulares de la muñeca durante los movimientos de flexoextensión. Materiales y métodos: se realizó una configuración experimental para adquirir EMG y ángulo articular de la muñeca, relacionado con los movimientos de flexión-extensión. Después de eso, se implementó un algoritmo de reconocimiento de patrones mioeléctricos basado en una red neuronal artificial de perceptrón multicapa (ANN). Se emplearon tres grupos diferentes: características de dominio de tiempo, parámetros de modelos autorregresivos (AR) y representación de frecuencia de tiempo usando la transformación Wavelet (WT). Resultados y discusión: los resultados experimentales de 10 sujetos sanos indican que los coeficientes de los modelos AR ofrecen los mejores parámetros para la clasificación, alcanzando una tasa de discriminación del 78 % en cinco posiciones angulares estudiadas. La combinación de frecuencia y frecuencia de tiempo proporcionó una tasa de discriminación que alcanzó el 82 %.21 páginasapplication/pdfengPontificia Universidad JaverianaColombiahttps://doi.org/10.11144/Javeriana.iued25.capdClassification of the Angular Position During Wrist Flexion-Extension Based on EMG SignalsClasificación de la posición angular en flexoextensión de la muñeca a partir de señales EMGArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Vol. 25 (2021)21125Ingenieria y UniversidadR. Merletti and P. A. Parker, Electromyography: Physiology, Engineering, and Non-invasive Applications. Hoboken, NJ: John Wiley & Sons, 2004C. Germany, “A low cost signal acquisition board design for myopathy’s EMG database construction,” in 13th Int. Multi-Conf. Syst. Signals Devices (SSD), 2016, pp. 274–279. doi: 10.1109/SSD.2016.7473767S. Kalwa, “Neuromuscular disease classification based on discrete wavelet transform of dominant motor unit action potential of EMG signal,” in 2015 Int. Conf. Inf. Process. (ICIP), 2015. doi: 10.1109/INFOP.2015.7489474A. A. Ali, A. Albarahany, and L. Quan, “EMG signals detection technique in voluntary muscle movement,” in 6th Int. Conf. New Trends Inf. Sci. Serv. Sci. Data Min. (ISSDM), Taipei, Taiwan, 2012, pp. 738–742. Available: https://ieeexplore.ieee.org/document/6528730S. Thongpanja, A. Phinyomark, F. Quaine, Y. Laurillau, C. Limsakul, and P. Phukpattaranont, “Probability density functions of stationary surface EMG signals in noisy environments,” IEEE Trans. Instrum. Meas., 2016, vol. 65, no. 7, pp. 1547–1557. Available: https://ieeexplore.ieee.org/document/7438830F. Ortes, D. Karabulut, and Y. Z. Arslan, “General perspectives on electromyography signal features and classifiers used for control of human arm prosthetics,” in Adv. Methodol. Technol. Eng. Environ. Sci. Hershey, PA: IGI Global, 2019, pp. 1–17. https://doi.org/10.4018/978-1-5225-7359-3.ch001C. Garg, “Development of a software module for feature extraction and classification of EMG signals,” in Conf. Commun. Control Intell. Syst. (CCIS), 2015, pp. 250–254C. J. G. Duque, L. D. Muñoz, J. G. Mejía, and E. D. Trejos, “Discrete wavelet transform and k-nn classification in EMG signals for diagnosis of neuromuscular disorders,” in Image, Signal Process. Artificial Vision (STSIVA), 2014 XIX Sym., pp. 1–5. Available: https://ieeexplore.ieee.org/document/7010171M. Jahan, M. Manas, B. B. Sharma, and B. B. Gogoi, “Feature extraction and pattern recognition of EMG-based signal for hand movements,” in Adv. Comput. Commun. (ISACC), 2015 Int. Sym., pp. 49– 52. Available: https://ieeexplore.ieee.org/document/7377314C. Sapsanis, G. Georgoulas, and A. Tzes, “EMG based classification of basic hand movements based on time-frequency features,” in Control Autom. (MED), 2013 21st Mediterranean Conf., pp. 716–722. Available: https://ieeexplore.ieee.org/document/6608802L. Pan, D. Zhang, X. Sheng, and X. Zhu, “Residuals of autoregressive model providing additional information for feature extraction of pattern recognition-based myoelectric control,” in Eng. Med. Biol. S. (EMBC), 2015 37th Annu. Int. Conf. IEEE, pp. 7270–7273. Available: https://ieeexplore.ieee.org/document/7320070I. Haider, M. Shahbaz, M. Abdullah, and M. Nazim, “Feature Extraction for Identification of Extension and Flexion Movement of Wrist using EMG Signals,” in Electr. Comput. Eng. (CCECE), 2015 IEEE 28th Can. Conf., pp. 792–795. Available: https://ieeexplore.ieee.org/document/7129375J. Liu, “Feature dimensionality reduction for myoelectric pattern recognition: A comparison study of feature selection and feature projection methods,” Med. Eng. Phys., vol. 36, no. 12, pp. 1716–1720, 2014. Available: https://doi.org/10.1016/j.medengphy.2014.09.011G. Purushothaman and R. Vikas, “Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals,” Australas. Phys. Eng. Sci. Med., vol. 41, no. 2, pp. 549–559, 2018. doi: 10.1007/s13246-018-0646-7N. Nazmi, M. Abdul Rahman, S.-I. Yamamoto, S. Ahmad, H. Zamzuri, and S. Mazlan, “A review of classification techniques of EMG signals during isotonic and isometric contractions,” Sensors, vol. 16, no. 8, p. 1304, 2016. doi: 10.3390/s16081304M.-K. Kim, M. Kim, E. Oh, and S.-P. Kim, “A review on the computational methods for emotional state estimation from the human EEG,” Comput. Math. Methods Med., vol. 2013, 2013. doi: 10.1155/2013/573734R. M. Rangayyan, Biomedical Signal Analysis. New Jersey: John Wiley & Sons, 2015.P. Onsy, A. Alim, M. Moselhy, and E. F. Mroueh, “EMG Signal Processing and Diagnostic of Muscle Diseases,” in 2012 6th Int. Conf. New Trends Inf. Sci. Serv. Sci. Data Mining (ISSDM), pp. 1–6. Available: https://ieeexplore.ieee.org/document/6462866M. H. Jali, T. A. Izzuddin, Z. H. Bohari, H. I. Jaafar, and M. N. M. Nasir, “Pattern recognition of EMG signal during load lifting using Artificial Neural Network (ANN),” in Control Syst., Comput. Eng. (ICCSCE), 2015 IEEE Int. Conf., pp. 172–177. Available: https://ieeexplore.ieee.org/abstract/document/7482179M. Sood, “A novel module based approach for classifying epileptic seizures using EEG signals,” in Ind. Inform. Comput. Syst. (CIICS), 2016 Int. Conf., pp. 3–7. Available: https://ieeexplore.ieee.org/document/7462406N. Jiang, J. L. G. Vest-Nielsen, S. Muceli, and D. Farina, “EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees,” J. Neuroeng. Rehabil., vol. 9, no. 1, p. 42, 2012. doi: 10.1186/1743-0003-9-42X. Zhang, X. Li, O. W. Samuel, Z. Huang, P. Fang, and G. Li, “Improving the robustness of electromyogram-pattern recognition for prosthetic control by a postprocessing strategy,” Front. Neurorobot., vol. 11, p. 51, 2017. DOI: 10.3389/fnbot.2017.00051R. Merletti and D. Farina, Surface Electromyography: Physiology, Engineering, and Applications. New Jersey: John Wiley & Sons, 2016.D. Stegeman and H. Hermens, “Standards for surface electromyography: The European project Surface EMG for non-invasive assessment of muscles (SENIAM),” Enschede Roessingh Res. Dev., pp. 108– 112, 2007.L. Logesparan, A. J. Casson, and E. Rodriguez-Villegas, “Assessing the impact of signal normalization: Preliminary results on epileptic seizure detection,” in Eng. Med. Biol. Soc., EMBC, 2011 Ann. Int. Conf. IEEE, pp. 1439–1442. doi: 10.1109/IEMBS.2011.6090356J. Wang, L. Tang, and J. E. Bronlund, “A pattern recognition system for myoelectric based prosthesis hand control,” in. Ind. Electron. Appl. (ICIEA), 2015 IEEE 10th Conf., pp. 830–834. doi: 10.1109/ICIEA.2015.7334225A. López-Delis, A. F. Ruiz-Olaya, T. Freire-Bastos, and D. Delisle-Rodríguez, “A comparison of myoelectric pattern recognition methods to control an upper limb active exoskeleton,” in Iberoamer. Congr. Pattern Recogn., 2013, pp. 100–107. Available: https://link.springer.com/content/pdf/10.1007%2F978-3-642-41827-3_13.pdfS. Negi, Y. Kumar, and V. M. Mishra, “Feature extraction and classification for EMG signals using linear discriminant analysis,” in Adv. Comput. Commun. Autom. (ICACCA) Int. Conf., 2016, pp. 1–6. doi: 10.1109/ICACCAF.2016.7748960A. López-Delis and A. F. Ruiz Olaya, “Métodos computacionales para el reconocimiento de patrones mioeléctricos en el control de exoesqueletos robóticos: una revisión,” INGE@ UAN-Tenden. Ing., vol. 3, no. 5, 2013. Available: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiwzrLBlMv qAhXFhOAKHZPXAcwQFjAAegQIBBAB&url=http%3A%2F%2Frevistas.uan.edu.co%2Findex.ph p%2Fingeuan%2Farticle%2Fdownload%2F262%2F204&usg=AOvVaw3PTVXBDATNfynSdIuzJGc TM. A. Oskoei and H. Hu, “Myoelectric control systems: A survey,” Biomed. Signal Process. Control, vol. 2, no. 4, pp. 275–294, 2007. Available: https://doi.org/10.1016/j.bspc.2007.07.009Z. Arief, I. A. Sulistijono, and R. A. Ardiansyah, “Comparison of five time series EMG features extractions using Myo Armband,” in Electron. Symp. (IES), 2015 Int., pp. 11–14. Available: https://ieeexplore.ieee.org/document/7380805M. Haris, “EMG signal based finger movement recognition for prosthetic hand control,” in Commun. Control Intell. Syst. (CCIS), 2015. Available: https://ieeexplore.ieee.org/document/7437907B. S. Zheng, M. Murugappan, S. Yaacob, and S. Murugappan, “Human emotional stress analysis through time domain electromyogram features,” in Indu. Electron. (ISIE), IEEE Int. Symp., 2013, pp. 172–177. Available: https://ieeexplore.ieee.org/document/6738989S. V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction. New Jersey John Wiley & Sons, 2008.L. Ljung, System Identification: Theory for the User, 2nd ed. Up. Saddle River, NJ: PTR Prentice-Hall, 1999A. Subasi, “Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines,” Comput. Biol. Med., vol. 42, no. 8, pp. 806–815, 2012. doi: 10.1016/j.compbiomed.2012.06.004K. Englehart, B. Hudgins, P. A. Parker, and M. Stevenson, “Classification of the myoelectric signal using time-frequency based representations,” Med. Eng. Phys., vol. 21, no. 6, pp. 431–438, 1999. Available: https://doi.org/10.1016/S1350-4533(99)00066-1G. Strang and T. Nguyen, Wavelets and Filter Banks. Wellesley: Cambridge Press, 1996T. Kamali, R. Boostani, and H. Parsaei, “A multi-classifier approach to MUAP classification for diagnosis of neuromuscular disorders,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 1, pp. 191– 200, 2014. doi: 10.1109/TNSRE.2013.2291322R. Kohavi, “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,” in Int. Joint Conf. Artificial Intell., 1995, pp. 1137–1143. Available: http://ai.stanford.edu/~ronnyk/accEst.pdfS. Haykin, Neural Networks and Learning Machines. New Jersey: Prentice Hall, 2009.A. Akhtar, L. J. Hargrove, and T. Bretl, “Prediction of distal arm joint angles from EMG and shoulder orientation for prosthesis control,” in Eng. Med. Biol. Soc. (EMBC), 2012 Annu. Int. Conf. IEEE, pp. 4160–4163. Available: doi: 10.1109/EMBC.2012.6346883R. S. Naoum, N. A. Abid, and Z. N. Al-Sultani, “An enhanced resilient backpropagation artificial neural network for intrusion detection system,” Int. J. Comput. Sci. Netw. Secur., vol. 12, no. 3, p. 11, 2012.S. Abe, Support Vector Machines for Pattern Classification, vol. 2. Berlín: Springer, 2005J. Yousefi and A. Hamilton-Wright, “Characterizing EMG data using machine-learning tools,” Comput. Biol. Med., vol. 51, pp. 1–13, 2014. doi: 10.1016/j.compbiomed.2014.04.018L. R. Quitadamo et al., “Support vector machines to detect physiological patterns for EEG and EMGbased human: Computer interaction; A review,” J. Neural Eng., vol. 14, no. 1, p. 11001, 2017. doi: 10.1088/1741-2552/14/1/011001A. Alkan and M. Günay, “Identification of EMG signals using discriminant analysis and SVM classifier,” Expert Syst. Appl., vol. 39, no. 1, pp. 44–47, 2012. doi: 10.1016/j.eswa.2011.06.043P. Konar and P. Chattopadhyay, “Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs),” Appl. Soft Comput., vol. 11, no. 6, pp. 4203–4211, 2011. doi: 10.1016/j.asoc.2011.03.014G. L. Prajapati and A. Patle, “On performing classification using SVM with radial basis and polynomial kernel functions,” in 2010 3rd Int. Conf. Emerging Trends Eng. Technol., 2010, pp. 512–515. Available: https://ieeexplore.ieee.org/document/5698379P. A. Kaplanis, C. S. Pattichis, D. Zazula, and others, “Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders,” Med. Biol. Eng. Comput., vol. 48, no. 8, pp. 773–781, 2010. doi: 10.1007/s11517-010-0629-7G. R. Naik, D. K. Kumar, and others, “Twin SVM for gesture classification using the surface electromyogram,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 2, pp. 301–308, 2009. Available: https://ieeexplore.ieee.org/document/5353702X. Li, Q. Huang, J. Zhu, W. Sun, and H. She, “A novel proportional and simultaneous control method for prosthetic hand,” J. Mech. Med. Biol., vol. 17, no. 08, p. 1750120, 2017. doi: 10.1142/S0219519417501202H. Shim, H. An, S. Lee, E. H. Lee, H. Min, and S. Lee, “EMG pattern classification by split and merge deep belief network,” Symmetry, vol. 8, no. 12, p. 148, 2016. doi: 10.3390/sym8120148A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “Feature reduction and selection for EMG signal classification,” Expert Syst. Appl., vol. 39, no. 8, pp. 7420–7431, 2012. doi: 10.1016/j.eswa.2012.01.102info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbMedicina físicaMedicine physicalArticulacionesJointsBiomecánicaBiomechanicsIntencionalidad de movimientoSeñales de electromiografíaReconocimiento de patronesTécnicas de aprendizaje automáticoRedes neuronales artificialesMovement intentElectromyography signalsPattern recognitionMachine learning techniquesartificial neural networksTEXTClassification of the Angular Position During Wrist Flexion-extension Based on EMG Signals.pdf.txtClassification of the Angular Position During Wrist Flexion-extension Based on EMG Signals.pdf.txtExtracted texttext/plain55012https://repositorio.escuelaing.edu.co/bitstream/001/3247/4/Classification%20of%20the%20Angular%20Position%20During%20Wrist%20Flexion-extension%20Based%20on%20EMG%20Signals.pdf.txt1b453ecc3fdb028ddc32753acfc85651MD54metadata only accessTHUMBNAILPortada Classification of the Angular Position During Wrist Flexion-extension Based on EMG Signals.PNGPortada Classification of the Angular Position During Wrist Flexion-extension Based on EMG Signals.PNGimage/png57507https://repositorio.escuelaing.edu.co/bitstream/001/3247/3/Portada%20Classification%20of%20the%20Angular%20Position%20During%20Wrist%20Flexion-extension%20Based%20on%20EMG%20Signals.PNG55e4a3af7b9ba81da199f3fd12931cc7MD53open accessClassification of the Angular Position During Wrist Flexion-extension Based on EMG Signals.pdf.jpgClassification of the Angular Position During Wrist Flexion-extension Based on EMG Signals.pdf.jpgGenerated Thumbnailimage/jpeg9892https://repositorio.escuelaing.edu.co/bitstream/001/3247/5/Classification%20of%20the%20Angular%20Position%20During%20Wrist%20Flexion-extension%20Based%20on%20EMG%20Signals.pdf.jpg469df959f1bffcfc89c1d6ca1be2d33dMD55metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/3247/2/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD52open accessORIGINALClassification of the Angular Position During Wrist Flexion-extension Based on EMG Signals.pdfClassification of the Angular Position During Wrist Flexion-extension Based on EMG Signals.pdfapplication/pdf646072https://repositorio.escuelaing.edu.co/bitstream/001/3247/1/Classification%20of%20the%20Angular%20Position%20During%20Wrist%20Flexion-extension%20Based%20on%20EMG%20Signals.pdf63c8471efedf8a3d3d875fa4d94a9d50MD51metadata only access001/3247oai:repositorio.escuelaing.edu.co:001/32472024-09-06 03:02:31.334metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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 |