Arquitectura de un sistema de medición de bioparámetros integrando señales inerciales-magnéticas y electromiográficas

Authors

  • Mauro Callejas-Cuervo Universidad Pedagógica y Tecnológica de Colombia orcid http://orcid.org/0000-0001-9894-8737
  • Manuel A. Vélez-Guerrero Universidad Pedagógica y Tecnológica de Colombia
  • Wilson Javier Pérez Holguín Universidad Pedagógica y Tecnológica de Colombia

DOI:

https://doi.org/10.33571/rpolitec.v14n27a9

Keywords:

Rehabilitación, miembros superiores, captura de movimiento, electromiografía, fusión de datos

Abstract

Este trabajo presenta una arquitectura para la medición e integración de bioparámetros basado en unidades de procesamiento de movimiento inercial-magnético (MPUs) y electromiografía (EMG). Derivado de la arquitectura propuesta, se logró desarrollar un dispositivo llamado Imocap, el cual reúne y utiliza las mejores características de la tecnología MPU + EMG para realizar una medición completa en el segmento de brazo y antebrazo en el cuerpo humano. Se presenta en primer lugar la revisión bibliográfica de los métodos y herramientas para la captura del movimiento biomecánico, seguido de las técnicas y aplicaciones de la recolección de bioparámetros. Finalmente, se muestra la arquitectura y la descripción del sistema Imocap, algunas aplicaciones y discusión. Como trabajo futuro, Imocap tiene como objetivo proporcionar la información necesaria en un sistema de control electrónico para una plataforma de rehabilitación basada en exoesqueletos robóticos.

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Author Biographies

Mauro Callejas-Cuervo, Universidad Pedagógica y Tecnológica de Colombia

Ingeniero de Sistemas

Manuel A. Vélez-Guerrero, Universidad Pedagógica y Tecnológica de Colombia

Ingeniero Electrónico

Wilson Javier Pérez Holguín, Universidad Pedagógica y Tecnológica de Colombia

3PhD en ingeniería

References

V. Feigin, G., et al. Global burden of stroke and risk factors in 188 countries, during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013, The Lancet Neurology, vol. 15, 913-924, 2016.

Ministerio de Salud y Protección Social de Colombia. Sala Situacional de Personas con Discapacidad (PCD), 2016.

D. Hill, C. Holloway, D. Morgado Ramirez, P. Smitham y Y. Pappas. What are User Perspectives of Exoskeleton Technology? A Literature Review, Intl. Journal of Tech. Assessment in Health Care, vol. 33, 160-167, 2017.

A. Stephenson y J. Stephens. An exploration of physiotherapists’ experiences of robotic therapy in upper limb rehabilitation within a stroke rehabilitation centre, Disability and Rehabilitation: Assistive Technology, vol. 13, 245-252, 2018.

D. Chakarov, I. Veneva, M. Tsveov y T. Tiankov. New Exoskeleton Arm Concept Design and Actuation for Haptic Interaction With Virtual Objects, Journal of Theo. and App. Mech., vol. 44, 3-14, 2015.

Z. Tang, K. Zhang, S. Sun, Z. Gao, L. Zhang, y Z. Yang. An Upper-Limb Power-Assist Exoskeleton Using Proportional Myoelectric Control, Sensors, 6677–6694, 2015.

Z. Lu, X. Chen, X. Zhang, K.-Y. Tong, y P. Zhou. Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition, Int. J. Neural Syst., vol. 27, 2017.

F. Xiao, Y. Wang, Y. Gao, Y. Zhu, y J. Zhao. Continuous estimation of joint angle from electromyography using multiple time-delayed features and random forests, Biomed. Signal Process. Control, vol. 39, 303–311, 2018.

C. Lambelet, M. Lyu, D. Woolley, R. Gassert, y N. Wenderoth. The eWrist: A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation, en International Conference on Rehabilitation Robotics, 726–733, 2017.

M. Tiboni, et al. ERRSE: Elbow robotic rehabilitation system with an EMG-based force control, 26th International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2017, vol. 49, 892–900, 2018.

J. R. Koller, C. David Remy, y D. P. Ferris. Comparing neural control and mechanically intrinsic control of powered ankle exoskeletons, en 2017 International Conference on Rehabilitation Robotics, 294–299, 2017.

B. Hwang y D. Jeon. Estimation of the User’s Muscular Torque for an Over-ground Gait Rehabilitation Robot Using Torque and Insole Pressure Sensors, Int. J. Control. Autom. Syst., vol. 16, 275–283, 2018.

A. Sarasola-Sanz, et al. A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients, en 2017 International Conference on Rehabilitation Robotics, 895–900, 2017.

A. Qingsong, Y. Zhang, W. Qi, Q. Liu, y K. Chen. Research on lower limb motion recognition based on fusion of sEMG and accelerometer signals, Symmetry (Basel), vol. 9, 2017.

O. Mazumder, A. S. Kundu, P. K. Lenka, y S. Bhaumik. Multi-channel Fusion Based Adaptive Gait Trajectory Generation Using Wearable Sensors, J. Intell. Robot. Syst. Theory Appl., vol. 86, 335–351, 2017.

V. Joukov, J. F.-S. Lin, y D. Kulic. Generalized Hebbian algorithm for wearable sensor rotation estimation, en 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2248–2253, 2017.

S. J. Kim, Y. Kim, H. Lee, P. Ghasemlou, y J. Kim. Development of an MR-compatible hand exoskeleton that is capable of providing interactive robotic rehabilitation during fMRI imaging, Med. Biol. Eng. Comput., vol. 56, 261–272, 2018.

S. Ferrante, et al. Neural and physiological measures to classify user’s intention and control exoskeletons for rehabilitation or assistance: The experience @NearLab, 26th International Conference on Robotics, vol. 49, 735–745, 2018.

A. M. Tobar, et al. Decoding of ankle flexion and extension from cortical current sources estimated from non-invasive brain activity recording methods, Front. Neurosci., vol. 11, 2018.

H. Zeng-Guang, Z. Xin-Gang, C. Long, W. Qi-ning, y W. Wei-qun. Recent Advances in Rehabilitation Robots and Intelligent Assistance Systems, vol. 15, 2016.

A. Singla, S. Dhand, A. Dhawad and G. Virk. Toward Human-Powered Lower Limb Exoskeletons: A Review, Harmony Search and Nature Inspired Optimization Algorithms, 783-795, 2019.

J. Bin Huang, K. Y. Young, y C. H. Ko. Effective control for an upper-body exoskeleton robot using ANFIS, 2016 IEEE Int. Conf. Syst. Sci. Eng, 2–5, 2016.

A. M. Khan, F. Khan, y C. Han. Estimation of desired motion intention using extreme learning machine for upper limb assist exoskeleton, IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, 919–923, 2016.

Yupeng, K. Sang Hoon, P. Hyung-Soon, W. Yi-Ning y Z. Li-Qun. Developing a multi-joint upper limb exoskeleton robot for diagnosis, therapy, and outcome evaluation in neurorehabilitation, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 21, 490–499, 2013.

U. Keller, H. van Hedel, V. Klamroth-Marganska y R. Riener. ChARMin: The First Actuated Exoskeleton Robot for Pediatric Arm Rehabilitation, IEEE/ASME Trans. Mechatronics, vol. 21, 2201–2213, 2016.

P. N. Kooren, et al. Design and control of the Active A-Gear: A wearable 5-DOF arm exoskeleton for adults with Duchenne muscular dystrophy, Proc. IEEE RAS EMBS Int. Conf. Biomed. Robot. Biomechatronics, vol. 2016, 637–642, 2016.

J. Meuleman, E. van Asseldonk, G. van Oort, H. Rietman y H. van der Kooij. LOPES II Design and Evaluation of an Admittance Controlled Gait Training Robot with Shadow-Leg Approach, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, 352-363, 2016.

G. Zeilig, et al. Lokomat walking results in increased metabolic markers in individuals with high spinal cord injury, Virtual Rehabil. Proc. (ICVR), 119–120, 2015.

M. Mallwitz, N. Will, J. Teiwes, y E. A. Kirchner. The Capio Active Upper Body Exoskeleton and its Application For Teleoperation, Proc. 13th Symp. Adv. Sp. Technol. Robot. Autom., vol. 13, 1–8, 2015.

A. Gardner, J. Potgieter y F. Noble. A review of commercially available exoskeletons' capabilities, 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 2017.

A. Ruiz-Olaya, M. Callejas-Cuervo and C. Lara-Herrera. Wearable low-cost inertial sensor-based electrogoniometer for measuring joint range of motion, DYNA 84 (201), 180-185, 2017.

M. Callejas-Cuervo, R. Gutierrez y A. Hernandez. Joint amplitude MEMS based measurement platform for low cost and high accessibility telerehabilitation: Elbow case study, Journal of Bodywork and Movement Therapies, vol. 21, 574-581, 2017.

A. Ruiz-Olaya, M. Callejas-Cuervo y A. Perez,. EMG-based pattern recognition with kinematics information for hand gesture recognition, 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA), 2015.

Published

2018-12-08

How to Cite

Callejas-Cuervo, M., Vélez-Guerrero, M. A., & Pérez Holguín, W. J. (2018). Arquitectura de un sistema de medición de bioparámetros integrando señales inerciales-magnéticas y electromiográficas. Revista Politécnica, 14(27), 93–102. https://doi.org/10.33571/rpolitec.v14n27a9

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