Decoding of Grasp and Individuated Finger Movements Based on Low-Density Myoelectric Signals

[ES] 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.[EN] Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning to use an artificial hand. This work presents the development of a myoelectric pattern recognition system for myoelectric weak signals able to discriminate dexterous hand movements using a reduced number of electrodes. The system was evaluated in six forearm amputees and the results were compared with the performance of able-bodied subjects. Different methods were analyzed to classify individual fingers flexion, hand gestures and different grasps using four electrodes and considering the low level of muscle contraction in these tasks. Multiple features of sEMG signals were also analyzed considering traditional magnitude-based features and fractal analysis. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). For amputees, results showed accuracy up to 99.4% for individual finger movements, higher than the achieved by grasp movements, up to 93.3%. Best performance was achieved using support vector machine (SVM), followed very closely by K-nearest neighbors (KNN). However, KNN produces a better global performance because it is faster than SVM, which implies an advantage for real-time applications. The results show that the method here proposed is suitable for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.Este trabajo ha sido patrocinado por CAPES y FAPES/Brasil (Proyecto Número 007/2014: Use of Robotics and Assistive Technology for Children and Adults with Disabilities).Villarejo Mayor, JJ.; Mamede Costa, R.; Frizera Neto, A.; Freire Bastos, T. (2017). Decodificación de Movimientos Individuales de los Dedos y Agarre a Partir de Señales Mioeléctricas de Baja Densidad. Revista Iberoamericana de Automática e Informática industrial. 14(2):184-192. https://doi.org/10.1016/j.riai.2017.02.001OJS184192142Al-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.2013.2249590Arjunan, 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: 10.1186/1743-0003-7-53Burck, 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/s12938-015-0025-5Ceres, 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)70145-6Chowdhury, 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/s130912431Cipriani, 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.2011.2108667Englehart, 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: 10.1109/10.914793Guo, 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/s150409022Hermens, 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)00027-4Hu, 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.64.011114Hudgins, B., Parker, P., Scott, R., 1993. A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering 40(1), 82-94. DOI: 10.1109/10.204774Japkowicz, 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.2011.6090465Khushaba, 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.2012.02.192Kumar, 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: 10.1186/1743-0003-10-50Light, 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: 10.1080/03091900210142459Losier, 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: 10.1186/1743-0003-9-40Naik, 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-yOskoei, 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.2008.919734Peerdeman, 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.2010.08.0161Peleg, 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.2002.806831Phinyomark, 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.2012.03.039Phinyomark, 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.2012.01.102Pons, 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/S026357470400133XSensinger, 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.2009.2023282Tenore, 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.2008.2005485Theodoridis, 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.2006.341203Villarejo, 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.2014.6880991Villarejo, 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.2013.6650361Villarejo, 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)60119-5Zecca, 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.i456.8