Knee functional state classification using surface electromyographic and goniometric signals by means artificial neural networks
Published
Mar 16, 2015
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Abstract
In this article a methodology for a medical diagnostic decision support system to assess knee injuries is proposed. Such methodology takes into account that these types of injuries are common and arise due to different causes. Therefore, the physician’s diagnostic and treatment may lead to expensive and invasive tests depending on his medical criteria. This system uses a surface Electromyographic (sEMG) and goniometric signals that are processed with signal analysis methods in time-frequency space through a spectrogram and a wavelet transform. Artificial neural networks are used as a learning technique by having a multilayer perceptron. EMG signals were measured in four external and internal muscles associated to the joint through flexion and extension assessments. These tests also registered the goniometric measures of the sagittal plane. This system shows above 80% of effectiveness as a performance measure that makes it an objective measure leading to help the physician in his diagnosis.
Keywords
Knee injury, sEMG, ANN, goniometry, transformed waveletLesión de rodilla, EMGS, RNA, goniometría, transformada wavelet.
References
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[2] S. L. Woo et al., “Biomechanics of knee ligaments: injury, healing, and repair”, Journal Biomechanics, vol 39, pp. 1-20, 2006.
[3] F. Salinas Durán, et al., Rehabilitación en salud, 2da ed. Medellín: Universidad de Antioquia, 2008.
[4] R. D. P. Morales, D. A. Morales y V. H. Grisales, “Caracterización de señales electromiográficas para la discriminación de seis movimientos de la mano,” Scientia Et Technica, pp. 278-283, 2009.
[5] A. Fuglsang-Frederiksen, “The utility of interference pattern analysis,” Muscle nerve, pp. 18-36, 2000.
[6] J. L. Dideriksen et al., “Comparison between the degree of motor unit short-term synchronization and recurrence quantification analysis of the surface EMG in two human muscles”, Clinical neurophysiology, pp. 2086-2092, 2009.
[7] A. L. Bryant, R. U. Newton, and J. Steele, “Successful feed-forward strategies following ACL injury and reconstruction,” Journal Electromyography Kinesiology, pp. 988-997, 2009.
[8] M. C. Panesso, M. C. Trillos y I. T. Guzmán, Biomecánica clínica de la rodilla. Bogotá: Editorial Universidad del Rosario, 2009.
[9] A. Subasi, “Classification of EMG signals using combined features and soft computing techniques”, Applied soft computing, vol. 2012, no. 8, pp. 2188-2198, ago. 2012.
[10] L. Kok-Meng and J. Guo, “Kinematic and dynamic analysis of an anatomically based knee joint”, Journal Biomechanics, vol. 43, pp. 1231-1236, 2010.
[11] J. Romkes, C. Rudman, and R. Brunner, “Changes in gait and EMG when walking with the Masai Barefoot Technique”, Clinical Biomechanics, vol 21, pp. 75-81, 2006.
[12] V. C. Dionisio et al., “Kinematic, kinetic and EMG patterns during downward squatting”, Journal Electromyography Kinesiology, vol. 18, pp. 134-143, 2008.
[13] G. Rasool, K. Iqbal, and G. A. White, “Myoelectric activity detection during a Sit-to- Stand movement using threshold methods”, Computers Mathematics Applications, vol. 64, pp. 1473-1483, 2012.
[14] J. D. Enderle and J. D. Bronzino, Introduction to biomedical engineering. Burlington MA: Academic Press, 2011.
[15] J. D. Bronzino, The biomedical engineering handbook. Londres: CRC Press LLC, 2000.
[16] Y.C. Du et al., “Portable hand motion classifier for multi-channel surface electromyography recognition using grey relation analysis”, Expert Systems Applications, pp. 4283-4291, 2010.
[17] W. E. Prentice, Técnicas de rehabilitación en medicina deportiva. Badalona, España: Editorial Paidotribo, 2001.
[18] A. Phinyomark, “EMG feature evaluation for improving myoelectric pattern recognition robustness”, Expert Systems with Applications: An International Journal Archive, vol. 40, no. 12, pp. 4832-4840, sep. 2013.
[19] P. Konrad, Theabc of EMG. A practical introduction to kinesiological electromyography. Scottsdale: Noraxon, 2005.
[20] S. Thongpanja et al., “Mean and median frequency of EMG sign alto determine muscle force based on time-dependent power spectrum”, Electronics Electrical Engineering, vol 19, pp. 51-56, 2013.
[21] J. L. Rodríguez Sotelo et al., “Comparative study of techniques to features extraction in signals of electrocardiography”, Tesis de maestría, Universidad Nacional de Colombia, Manizales, 2004.
[22] D. G. Sánchez Marín y J. I. Marín Hurtado, “Segmentación y realce de señales de voz usando la transformada Wavelet y DSPs”, Tesis, Universidad del Quindío, Armenia, Colombia, 2004.
[23] A. Subasi, “Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders”, Computers Biology Medicine, vol. 43, pp. 576-586, 2013.
[24] T. M. Apóstol, Análisis matemático. Barcelona: Editorial Reverté, 1976.
[25] A. F. Quiceno Manrique et al., “Análisis tiempo-frecuencia por métodos no paramétricos orientado a la detección de patologías en bioseñales”, Tesis de maestría, Universidad Nacional de Colombia, Bogotá, 2009.
[26] M. M. Ardestani, “Human lower extremity joint moment prediction: A wavelet neural network approach”, Expert Systems Applications, vol. 41, pp. 4422-4433, jul. 2014.
[27] M. Rojas-Martínez et al., “Identification of isometric contractions based on high density EMG maps”, Journal Electromyography Kinesiology, vol. 23 pp. 33-42, 2013.
[28] M. Bienfait, Bases fisiológicas de la terapia manual y de la osteopatía. Barcelona: Paidotribo, 2006.
[29] V. Ruonala et al., “EMG signal morphology and kinematic parameters in essential tremor and Parkinson’s disease patients”, Journal Electromyography Kinesiology, vol. 24, pp. 300-306, 2014.
[30] J. L. Rodríguez Sotelo et al., “Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering”, Computer Methods Programs Biomedicine, pp. 250-261, 2012.
How to Cite
Herrera-González, M., Martínez-Hernández, G. A., Rodríguez-Sotelo, J. L., & Avilés-Sánchez, O. F. (2015). Knee functional state classification using surface electromyographic and goniometric signals by means artificial neural networks. Ingeniería Y Universidad, 19(1), 51–66. https://doi.org/10.11144/Javeriana.iyu19-1.kfsc
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Articles