Sistema de Monitoreo MONITOREO MUSCULAR POR MEDIO DE ELECTROMIOGRAFÍA CON GANANCIAS VARIABLES

Juan José Martínez Nolasco, David Manuel Carracedo González, Francisco Gutiérrez Vera, Daniel Cipriano Barradas Delfin

Resumen


Se presenta un sistema de monitoreo muscular capaz de cuantificar los movimientos de cualquier músculo esquelético superficial del cuerpo humano, funciona utilizando una técnica no invasiva de la electromiografía (EMG). El sistema de monitoreo funciona como una herramienta de ayuda accesible a los especialistas en rehabilitación para cuantificar de forma numérica los máximos rangos de movilidad de su paciente y mejorar así las sesiones de ejercicios de rehabilitación.

Palabra(s) Clave(s): adquisición, amplificación, electromiografía, músculos, portabilidad.


Texto completo:

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Referencias


T. D. Lalitharatne, Y. Hayashi, “A Study on Effects of Muscle Fatigue on EMG-Based Control for Human Upper-Limb Power-Assist”. Information and Automation for Sustainability (ICIAfS). 2012. 124 - 128.

H.I. Cakar, O. Toker, “A wireless surface electromyography system design for lumbar disc herniated patients”. Medical Measurements and Applications Proceedings (MeMeA). 2011. 35 - 38.

C. M. O'Connor, S. Langran, “Design of surface electrode array for electromyography in the genioglossus muscle”. Engineering in Medicine and Biology Society. 2004. 2259 – 2262.

A. Wege, A. Zimmermann, “Electromyography sensor based control for a hand exoskeleton”. Robotics and Biomimetics. 2007. 1470 – 1475.

F. Barroso, C. Santos, “Influence of the robotic exoskeleton Lokomat on the control of human gait: An electromyographic and kinematic analysis”. Bioengineering (ENBENG). 2013. 1 - 6.

B. D. Farnsworth, D.M. Talyor, “Wireless in vivo EMG sensor for intelligent prosthetic control”. Transducers 2009. 2009. 21 - 25.

M. V. Liarokapis, P. K. Artemiadis, “Task Discrimination from Myoelectric Activity: A Learning Scheme for EMG-Based Interfaces”. Rehabilitation Robotics (ICORR). 2013. 1 – 6.

O. Bida, D. Rancourt, “Electromyogram (EMG) Amplitude Estimation and Joint Torque Model Performance”. Bioengineering Conference. 2005. 229 – 230.

D. K. Kumar, N. D. Pah, “Wavelet Analysis of Surface Electromyography to determine Muscle Fatigue”. IEEE Transactions on neural systems and rehabilitation engineering. Vol. 11. No. 4. 2003. 400 – 406.

D. Staudenmann, A. Daffertshofer, “Independent Component Analysis of High-Density Electromyography in Muscle Force Estimation”. IEEE Transactions on Biomedical Engineering. Vol. 54. No. 4. 2007. 751 – 754.

H. Huang, R. Lipschutz “A Strategy for Identifying Locomotion Modes Using Surface Electromyography”. IEEE Transactions on Biomedical Engineering. Vol. 56. No. 1. 2009. 65 – 73.

H. Huang, P. Zhou, “An Analysis of EMG Electrode Configuration for Targeted Muscle Reinnervation Based Neural Machine Interface”. IEEE Transactions on Neural Systems and Rehabilitation Engineering. Vol. 16. No. 1. 2008. 37 – 45.

F.V. Tenore, A. Ramos, “Decoding of Individuated Finger Movements Using Surface Electromyography”. IEEE Transactions on Biomedical Engineering. Vol. 56. No. 5. 2009. 1427 – 1434.

Y. Ueyama, E. Miyashita, “Optimal Feedback Control for Predicting Dynamic Stiffness During Arm Movement”. IEEE Transactions on Industrial Electronics, Vol. 61. No. 2. 2014. 1044 – 1052.

Y. Su, M. H. Fisher, “Towards an EMG-Controlled Prosthetic Hand Using a 3-D Electromagnetic Positioning System”. IEEE Transactions on Instrumentation and Measurement. Vol. 56. No. 1. 2007. 178 – 186.

B. W. Lee, C. Lee, “Comparison of Conductive Fabric Electrode With Electromyography to Evaluate Knee Joint Movement”. IEEE Sensors Journal. Vol. 12. No. 2. 2012. 410 – 411.

Q. Zhang, M. Hayashibe, “Evoked Electromyography-Based Closed-Loop Torque Control in Functional Electrical Stimulation”. IEEE Transactions on Biomedical Engineering. Vol. 60. No. 8. 2013. 2299 – 2307.

Y. Muraoka, “Development of an EMG recording device from stimulation electrodes for functional electrical stimulation”. Frontiers Med. Biol. Engng. Vol. 11. No. 4. 2002. 323–333.

H. Li, G. Zhao, “Relationship of EMG/SMG features and muscle strength level: an exploratory study on tibialis anterior muscles during plantar-flexion among hemiplegia patients”. BioMedical Engineering OnLine. 2012.

X. Zhang, B. Wang, “Human Intention Extracted from Electromyography Signals for Tracking Motion of Meal Assistance Robot”.IEEE/ ICME International Conference on Complex Mediacal Engineering. 2007. 1384 – 1387.






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