Decodificación de Movimientos Individuales de los Dedos y Agarre a Partir de Señales Mioeléctricas de Baja Densidad

John J. Villarejo Mayor, Regina Mamede Costa, Anselmo Frizera Neto, Teodiano Freire Bastos

Resumen

Uno de los principales retos en el diseño de prótesis de mano es poder establecer un control intuitivo que reduzca el esfuerzo del usuario durante su entrenamiento. Este trabajo presenta un esquema para identificar tareas de motricidad fina de la mano, agrupadas en movimientos de los dedos individuales y gestos para el agarre de objetos el cual se ha validado con sujetos amputados. Se han comparado diferentes métodos de selección de características y clasificadores para el reconocimiento de patrones mioeléctricos, utilizando cuatro electrodos superficiales. Las características de las señales en el dominio del tiempo y la frecuencia se han combinado con métodos no lineales basados en análisis de fractales, mostrando una diferencia significativa en comparación con los métodos expuestos en la literatura para clasificar tareas de fuerza. Los resultados con amputados mostraron una exactitud de hasta 99,4% en los movimientos individuales de los dedos, superior a la obtenida con los gestos de agarre, de hasta 93,3%. El sistema ha obtenido una tasa de acierto promedio de 86,3% utilizando máquinas de soporte vectorial (SVM), seguido muy de cerca por K-vecinos más cercanos (KNN) con 83,4%. Sin embargo, KNN ha obtenido un mejor rendimiento global, debido a que es más rápido que SVM, lo que representa una ventaja para aplicaciones en tiempo real. El método aquí propuesto ofrece una mayor funcionalidad en el control de prótesis de mano, lo que mejoraría su aceptación por parte de los amputados.

Palabras clave

Señales electromiográficas; prótesis de miembro superior; reconocimiento de patrones; tareas de destreza de la mano

Texto completo:

PDF

Referencias

Al-Timemy, A., Bugmann, G., Escudero, J., Outram, N., 2013. Classification of finger movements for the dexterous hand prosthesis control with surface electromyography. IEEE Journal of Biomedical and Health Informatics 17(3), 608–618. DOI:10.1109/JBHI.2013.2249590

Arjunan, S., Kumar, D., 2010. Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors. Journal of Neuroengineering and Rehabilitation 7(1), 53. DOI: 10.1186/1743-0003-7-53

Burck, J., Bigelow, J., Harshbarger, S., 2011. Revolutionizing prosthetics: systems engineering challenges and opportunities. Johns Hopkins APL Tech Dig 30(3), 186–197.

Castro, M., Arjunan, S., Kumar, D., 2015. Selection of suitable hand gestures for reliable myoelectric human computer interface. BioMedical Engineering OnLine 14(1), 1–11. DOI: 10.1186/s12938-015-0025-5

Ceres, R., Pons, J., Calderón, L., Moreno, J., 2008. La robótica en la discapacidad. Desarrollo de la prótesis diestra de extremidad inferior manus-hand. Revista Iberoamericana de Automática E Informática Industrial RIAI 5(2), 60–68. DOI: 10.1016/S1697-7912(08)70145-6

Chowdhury, R., Reaz, M., Ali, M., Bakar, A., Chellappan, K., Chang, T., 2013. Surface electromyography signal processing and classification techniques. Sensors 13(9), 12431–66. DOI: 10.3390/s130912431

Cipriani, C., Antfolk, C., Controzzi, M., Lundborg, G., Rosen, B., Carrozza, M., Sebelius, F., 2011. Online myoelectric control of a dexterous hand prosthesis by transradial amputees. IEEE Transactions on Neural Systems and Rehabilitation Engineering 19(3), 260–270. DOI: 10.1109/TNSRE.2011.2108667

Englehart, K., Hudgins, B., Parker, P., 2001. A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering 48(3), 302–311. DOI: 10.1109/10.914793

Guo, S., Pang, M., Gao, B., Hirata, H., Ishihara, H., 2015. Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement. Sensors 15(4), 9022–38. DOI: 10.3390/s150409022

Hermens, H. J., Freriks, B., Disselhorst-Klug, C., Rau, G., 2000. Development of recommendations for SEMG sensors and sensor placement procedures. Journal of Electromyography and Kinesiology 10(5), 361–374. DOI: 10.1016/S1050-6411(00)00027-4

Hu, K., Ivanov, P., Chen, Z., Carpena, P., Stanley, H., 2001. Effect of trends on detrended fluctuation analysis. Physical Review. E 64, 11114. DOI: 10.1103/PhysRevE.64.011114

Hudgins, B., Parker, P., Scott, R., 1993. A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering 40(1), 82–94. DOI: 10.1109/10.204774

Japkowicz, N., Shah, M., 2014. Evaluation learning algorithms: a classification perspective. Cambridge University Press. New York, NY, USA.

Kanitz, G., Antfolk, C., Cipriani, C., Sebelius, F., Carrozza, M., 2011. Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm. Annual International Conference of the IEEE Engineering in Medicine and Biology Society 33, 1608–11. DOI: 10.1109/IEMBS.2011.6090465

Khushaba, R., Kodagoda, S., Takruri, M., Dissanayake, G., 2012. Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Systems with Applications 39(12), 10731–10738. DOI: 10.1016/j.eswa.2012.02.192

Kumar, D., Arjunan, S., Singh, V., 2013. Towards identification of finger flexions using single channel surface electromyography - able bodied and amputee subjects. Journal of Neuroengineering and Rehabilitation 10(1), 50. DOI: 10.1186/1743-0003-10-50

Light, C., Chappell, P., Hudgins, B., Engelhart, K., 2002. Intelligent multifunction myoelectric control of hand prostheses. Journal of Medical Engineering & Technology 26(4), 139–146. DOI: 10.1080/03091900210142459

Losier, Y., Clawson, A., Wilson, A., Scheme, E., Englehart, K., Kyberd, P., Hudgins, B., 2011. An overview of the UNB hand system. Proceedings of the 2011 MyoElectric Controls/Powered Prosthetics Symposium Fredericton, 2–5.

Matrone, G., Cipriani, C., Carrozza, M., Magenes, G., 2012. Real-time myoelectric control of a multi-fingered hand prosthesis using principal components analysis. Journal of NeuroEngineering and Rehabilitation 9(1), 40. DOI: 10.1186/1743-0003-9-40

Naik, G., Kumar, D., Arjunan, S., 2010. Pattern classification of myoelectrical signal during different maximum voluntary contractions: a study using BSS techniques. Measurement Science Review 10(1), 1–6. DOI: 10.2478/v10048-010-0001-y

Oskoei, M., Hu, H., 2008. Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb. IEEE Transactions on Biomedical Engineering 55(8), 1956–1965. DOI: 10.1109/TBME.2008.919734

Peerdeman, B., Boere, D., Witteveen, H., Huis R., Hermens, H., Stramigioli, S., Misra, S., 2011. Myoelectric forearm prostheses: state of the art from a user-centered perspective. The Journal of Rehabilitation Research and Development 48(6), 719. DOI: 10.1682/JRRD.2010.08.0161

Peleg, D., Braiman, E., Yom-Tov, E., Inbar, G., 2002. Classification of finger activation for use in a robotic prosthesis arm. IEEE Transactions on Neural Systems and Rehabilitation Engineering 10(4), 290–293. DOI: 10.1109/TNSRE.2002.806831

Phinyomark, A., Phukpattaranont, P., Limsakul, C., 2012a. Fractal analysis features for weak and single-channel upper-limb EMG signals. Expert Systems with Applications 39(12), 11156–11163. DOI: 10.1016/j.eswa.2012.03.039

Phinyomark, A., Phukpattaranont, P., Limsakul, C., 2012b. Feature reduction and selection for EMG signal classification. Expert Systems with Applications 39(8), 7420–7431. DOI: 10.1016/j.eswa.2012.01.102

Pons, J., Ceres, R., Rocon, E., Levin, S., Markovitz, I., Saro, B., Bueno, L., 2005. Virtual reality training and EMG control of the MANUS hand prosthesis. Robotica 23(3), 311–317. DOI: 10.1017/S026357470400133X

Sensinger, J., Lock, B., Kuiken, T., 2009. Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms. IEEE Transactions on Neural Systems and Rehabilitation Engineering 17(3), 270–278. DOI: 10.1109/TNSRE.2009.2023282

Tenore, F., Ramos, A., Fahmy, A., Acharya, S., Etienne-Cummings, R., Thakor, N., 2009. Decoding of individuated finger movements using surface electromyography. IEEE Transactions on Biomedical Engineering 56(5), 1427–1434. DOI: 10.1109/TBME.2008.2005485

Theodoridis, S., Koutroumbas, K., 2008. Pattern Recognition. Academic press.

Tsenov, G., Zeghbib, A., Palis, F., Shoylev, N., Mladenov, V., 2006. Neural networks for online classification of hand and finger movements using surface EMG signals. 8th Seminar on Neural Network Applications in Electrical Engineering (NEUREL), 167–171. DOI: 10.1109/NEUREL.2006.341203

Villarejo, J., Costa, R., Bastos, T., Frizera, A., 2014. Identification of low level semg signals for individual finger prosthesis. Biosignals and Biorobotics Conference. Biosignals and Robotics for Better and Safer Living (BRC), 5th ISSNIP-IEEE. DOI: 10.1109/BRC.2014.6880991

Villarejo, J., Frizera, A., Bastos, T., Sarmiento, J., 2013. Pattern recognition of hand movements with low density sEMG for prosthesis control purposes. IEEE International Conference on Rehabilitation Robotics 1–6. DOI: 10.1109/ICORR.2013.6650361

Villarejo, J., Mamede, R., Bastos, T., 2014. Movement Identification using weak sEMG signals of low density for upper limb control. En: Andrade, A., Barbosa, A., Cardoso, A., Lamounier, E. Tecnologias, técnicas e tendências em engenharia biomédica. Canal6 Edi, p. 280–300.

Yang, D., Zhao, J., Gu, Y., Wang, X., Li, N., Jiang, L., Zhao, D., 2009. An anthropomorphic robot hand developed based on underactuated mechanism and controlled by EMG signals. Journal of Bionic Engineering 6(3), 255–263. DOI: 10.1016/S1672-6529(08)60119-5

Zecca, M., Micera, S., Carrozza, M., Dario, P., 2002. Control of multifunctional prosthetic hands by processing the electromyographic signal. Critical Reviews in Biomedical Engineering 30(4–6), 459–485. DOI: 10.1615/CritRevBiomedEng.v30.i456.80

Abstract Views

2339
Metrics Loading ...

Metrics powered by PLOS ALM


 

Citado por (artículos incluidos en Crossref)

This journal is a Crossref Cited-by Linking member. This list shows the references that citing the article automatically, if there are. For more information about the system please visit Crossref site

1. Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton
Mario Ortiz, Laura Ferrero, Eduardo Iáñez, José M. Azorín, José L. Contreras-Vidal
Frontiers in Bioengineering and Biotechnology  vol: 8  año: 2020  
doi: 10.3389/fbioe.2020.00735



Creative Commons License

Esta revista se publica bajo una Licencia Creative Commons Attribution-NonCommercial-CompartirIgual 4.0 International (CC BY-NC-SA 4.0)

Universitat Politècnica de València     https://doi.org/10.4995/riai

e-ISSN: 1697-7920     ISSN: 1697-7912