Advances in the mental control of a robotic hand

Main Article Content

Iván Fernando Vargas Ochoa
Richard Camilo Bravo Angarita
César Augusto Peña Cortes

Article Details

Iván Fernando Vargas Ochoa, Universidad de Pamplona

Facultad de Ingenierías y Arquitectura

Richard Camilo Bravo Angarita, Universidad de Pamplona

Facultad de Ingenierías y Arquitectura

César Augusto Peña Cortes, Universidad de Pamplona

Facultad de Ingenierías y Arquitectura

Section
Research Articles

Abstract

Introduction: The present article is the product of the research "Advances in the mental control of a robotic hand", developed at the University of Pamplona in the year 2019.


Problem: Currently one of the main problems presented by robotic hand prostheses is the way in which the user indicates the movements to be performed. Given this, the best results have been obtained using invasive systems.


Objective: The main objective of the system is to allow a person to control the movements and / or gestures of a robotic hand using their thoughts, in such a way that the control is as natural and precise as possible.


Methodology: Use is made of a non-invasive, low-cost brain-computer interface (BCI) for the generation of control system references.


Results: The performance of the system is directly subject to the user's ability to recreate actions or movements in their mind; the more defined your thinking, the better the control response.


Conclusion: Mind control represents a new challenge for users, but as it is used, it becomes a more natural and precise control method, offering great control possibilities to people who make daily use of robotic hand prostheses.


Originality: Through this research, an alternative is formulated for the control of hand prostheses, which does not require invasive systems and has the advantage of being low cost.


Limitations: Frustration, stress and external noise are factors that directly affect the performance of the system.

[1] A. Tecuatl Pancoatl, “Prótesis inteligentes,” Benemérita Universidad Autónoma de Puebla, pp. 12, 2014. [Online]. Available: https://edoc.pub/ensayo-protesis-inteligentes-pdf-free.html

[2] M. E. Rodríguez-García, G. Dorantes-Méndez, and M. O. M. Gutiérrez, “Desarrollo de una prótesis para desarticulado de muñeca controlada por señales de electromiografía,” Rev. Mex. Ing. Biomed., vol. 38, no. 3, 2017. [Online]. doi: http://dx.doi.org/10.17488/rmib.38.3.8

[3] J. Brazeiro, S. Petraccia, M. Valdés, “Mano Controlada por Señales Musculares,” Universidad de la República, pp. 161, Sep. 2015. [Online]. Available: https://iie.fing.edu.uy/publicaciones/2015/BPV15/BPV15.pdf

[4] R. Agrawal, “Predictive Analysis Of Breast Cancer Using Machine Learning Techniques”, Ing. Solidar., vol. 15, no. 29, 2019. https://doi.org/10.16925/2357-6014.2019.03.01

[5] D. Ramírez Jiménez and J. D. Quintero-Ospina, “Classification of pathologies present in the spinal column through learning machinery techniques”, Ing. Solidar., vol. 15, no. 27, 2019. https://doi.org/10.16925/2357-6014.2019.01.05

[6] J. M. López, G. Martí, S. T. Puente, F. A. Candelas, A. Úbeda, F. Torres, “Implementación y evaluación de un esquema de control mioeléctrico ON/OFF utilizando hardware de bajo coste,” Área de Ingeniería de Sistemas y Automática, Universidad de Extremadura, pp. 94-99, 2018. [Online]. Available: http://rua.ua.es/dspace/handle/10045/80509

[7] C. A. Quinayás-Burgos, C. A. Gaviria-López, “Sistema de identificación de intención de movimiento para el control mioeléctrico de una prótesis de mano robótica,” Ing. y Univ., vol. 19, no. 1, 2015. [Online]. http://dx.doi.org/10.11144/Javeriana.iyu19-1.siim

[8] D. Bansal and R. Mahajan, “EEG-Based Brain-Computer Interfacing (BCI),” Elsevier Inc., pp. 21 - 71, 2019. [Online]. doi: https://doi.org/10.1016/B978-0-12-814687-3.00002-8

[9] G. Rodríguez-Bermúdez, P. García Laencina, D. Brizion, and J. Roca, “Adquisición, procesamiento y clasificación de señales EEG para diseño de sistemas BCI basados en imaginación de movimiento,” Rev. VI Jornadas Introd. a la Investig. la UPCT, vol. 6, pp. 10–12, 2013.

[10] B. J. Edelman, J. Meng, D. Suma, C. Zurn, E. Nagarajan, BS Baxter, CC Cline, y B. El, “Noninvasive neuroimaging enhances continuous neural tracking for robotic device control,” Science Robotics, vol. 4, no. 31, 2019. [Online]. doi: https://doi.org/ 10.1126 / scirobotics.aaw6844

[11] J. Minguez, “Tecnología de Interfaz Cerebro - Computador,” Dep. Inform. e Ing. Sist.
Zaragoza, pp. 1–12, 2012. [Online]. Available: https://webdiis.unizar.es/~jminguez/Sesion001_UJI.pdf

[12] M. Balcells and V. Cisteré, “Imagen de archivo Historia de la Electroencefalografía en España : introducción y evolución,” Neurosci. Hist., vol. 2, no. 1, pp. 38–42, 2014. [Online]. Available: http://nah.sen.es/vmfiles/abstract/NAHV2N1201438_42ES.pdf

[13] L. A. Moreno-Cueva, C. A. Peña Cortés, H. G. Gonzáles-Sepúlveda, “Integración de un sistema de neuroseñales para detectar expresiones en el análisis de material multimedia,” Revista Facultad de Ingeniería, UPTC, vol. 24, no, 38. [Online]. doi: https://doi.org/10.19053/01211129.3156

[14] R. Raj, S. Deb, and P. Bhattacharya, “Brain Computer Interfaced Single Key Omni Directional Pointing and Command System: A Screen Pointing Interface for Differently-abled Person,” Procedia Comput. Sci., vol. 133, pp. 161–168, 2018. [Online]. doi: https://doi.org/10.1016/j.procs.2018.07.020

[15] A. Asif, M. Majid, and S. M. Anwar, “Human stress classification using EEG signals in response to music tracks,” Comput. Biol. Med., vol. 107, no. February, 2019. [Online]. doi: https://doi.org/10.1016/j.compbiomed.2019.02.015

[16] J. H. Yu and K. B. Sim, “Classification of color imagination using Emotiv EPOC and event-related potential in electroencephalogram,” Optik (Stuttg). vol. 127, no. 20, 2016. [Online]. doi: https://doi.org/10.1016/j.ijleo.2016.07.074

[17] F. Gómez, J. Leonardo, “Análisis de señales EEG para detección de eventos oculares, musculares y cognitivos,” Biblioteca ETSI Industriales, pp. 8–9, Sep. 2016. [Online]. Available:http://oa.upm.es/44379/1/TFM_LEONARDO_JOSE_GOMEZ_FIGUEROA.pdf

[18] L. Bi, A. Feleke, and C. Guan, “A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration,” Biomed. Signal Process. Control, vol. 51, pp. 113–127, 2019.
https://doi.org/10.1016/j.bspc.2019.02.011

[19] M. Tavakoli, C. Benussi, and J. L. Lourenco, “Single channel surface EMG control of advanced prosthetic hands: A simple, low cost and efficient approach,” Expert Syst.
Appl., vol. 79, pp. 322–332, 2017. [Online]. doi: https://doi.org/10.1016/j.eswa.2017.03.012

[20] C. Cipriani et al., "Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 19, no. 3, 2011. [Online]. doi: https://doi.org/10.1109/TNSRE.2011.2108667

[21] M. Jochumsen, A. Waris, and E. N. Kamavuako, “The effect of arm position on classification of hand gestures with intramuscular EMG,” Biomed. Signal Process. Control, vol. 43, 2018. [Online]. doi: https://doi.org/10.1016/j.bspc.2018.02.013

[22] M. A. A. Kasim et al., “User-Friendly LabVIEW GUI for Prosthetic Hand Control Using Emotiv EEG Headset,” Procedia Comput. Sci., vol. 105, no. December, 2017. [Online]. doi: https://doi.org/10.1016/j.procs.2017.01.222

[23] R. Alazrai, H. Alwanni, and M. I. Daoud, “EEG-based BCI system for decoding finger movements within the same hand,” Neurosci. Lett., vol. 698, no. December, 2019. [Online]. doi: https://doi.org/10.1016/j.neulet.2018.12.045

[24] M. D. del Castillo, J. I. Serrano, J. Ibáñez, and L. J. Barrios, “Metodología para la Creación de una Interfaz Cerebro-Computador Aplicada a la Identificación de la Intención de Movimiento,” RIAI - Rev. Iberoam. Autom. e Inform. Ind., vol. 8, no. 2, 2011. [Online]. doi: https://doi.org/10.1016/S1697-7912(11)70030-9

[25] G. D. I. Gib, J. Giraldo-leiva, and M. A. Becerra, “Brain Computer Interface Based On Eeg Signals For Motion Control Of Hand Prosthesis Using Anfis,” pp. 59–64, 2013. [Online]. Available: https://dialnet.unirioja.es/servlet/articulo?codigo=4776223

[26] C. D. Virgilio G., J. H. Sossa A., J. M. Antelis, and L. E. Falcón, “Spiking Neural Networks applied to the classification of motor tasks in EEG signals,” Neural Networks, vol. 122, 2020. [Online]. doi: https://doi.org/10.1016/j.neunet.2019.09.037

[27] G. Lange, C. Y. Low, K. Johar, F. A. Hanapiah, and F. Kamaruzaman, “Classification of Electroencephalogram Data from Hand Grasp and Release Movements for BCI Controlled Prosthesis,” Procedia Technol., vol. 26, pp. 374–381, 2016.

[28] J. E. Vargas Soto, M. A. Traslosheros, J. M. Ramos, J. E. Orozco Ramirez, “Sinergia Mecatrónica,” Asociación Mexicana de Mecatrónica A.C, pp. 517, May. 2019. [Online]. Available: http://www.mecamex.net/Libros/2019-Libro-SinergiaMecatronica.pdf

[29] T. Talamillo García, “Manual básico para enfermeros en electroencefalografía,” Enfermería Docente, pp. 29–33. [Online]. Available: http://www.sspa.juntadeandalucia.es/servicioandaluzdesalud/huvvsites/default/files/r evistas/ED-094-07.pdf

[30] M. E. Hussein and G. Brooker, “3D Printed Myoelectric Prosthetic Arm,” pp. 87, Oct. 2014. [Online]. Available: https://www.academia.edu/39599212/3D_Printed_Myoelectric_Prosthetic_Arm_3D
_Printed_Myoelectric_Prosthetic_Arm_i