Mechatronic device for the analysis and mitigation of involuntary movements in people with Parkinson's disease

Authors

  • Gabriel Eduardo Rivera Cárdenas Universidad UTE
  • Vladimir Bonilla Universidad UTE
  • Moya Marcelo Universidad UTE
  • Guillermo Mosquera Universidad UTE
  • Anatoly Vitalyevich Litvin Universidad Técnica Estatal del Don

DOI:

https://doi.org/10.29019/enfoqueute.v10n1.452

Keywords:

Parkinson disease; electromyographical signals; artificial Intelligence; exoskeleton

Abstract

Considering that the Parkinson disease is a neurodegenerative, progressive and incurable pathology, and with the purpose of improving ill people life quality, the design and construction of a mechatronic device was proposed to help mitigating the involuntary movements produced by the disease. This device allows the analysis of the involuntary movements of pronosupination generated in the upper limbs using electromyographic signals produced by the muscles of the forearm and an algorithm based on artificial neural networks. To materialize the device, fast prototyping like 3D printing and the V model-based mechatronics methodology were considered. As a result of this investigation, a mechatronics device in the shape of an exoskeleton controlled by an embedded system which analyses, processes electromyographic signals and using neural networks allows tremor and involuntary movements classification produced by each patient. The system operation results are: for tremor prediction is 96.88% of success, and for the involuntary movement prediction is 100 % of success.

Metrics

Downloads

Download data is not yet available.

References

Bonilla, V., Litvin, A. B., Lukyanov, E. A., & Starodubtseva, Л. B. (2018). Synthesis of the electromiographic control device. Control, computer engineering, informatics. Medical instrumentation, 3(28), 132-139.
Bonilla, V., Lukyanov , E. A., Litvin, A. V., & Deplov, D. A. (2015). Identification of the elbow motion kinematic parameters by means of artificial neural networks technology. Vestnik of Don State Technical University, 15(1), 39-47. doi:https://vestnik.donstu.ru/jour/article/view/228
Bonilla, V., Mosquera, G., Mideros, D., & Litvin, A. (2017). Definición de los parámetros del movimiento del codo mediante el análisis de las señales electromiográficas superficiales del bíceps. Quito: INCISCOS 2017.
Bonilla, V., Moya, M., Evgeny, A. V., Lukyanov, A., & Marín, L. (2018). Modeling and simulation of the Mitsubishi RV-2JA Robot controlled by electromyographic signals (Vol. 9). Quito: Enfoque UTE.
Cudeiro Mazaira, F. J. (2015). Reeducación Funcional en la enfermedad de Parkinson. Barcelona, España: Elsevier.
Estrada Bellman, I., & Martínez Rodríguez, H. R. (Septiembre-Diciembre de 2011). Diagnóstico y tratamiento de la enfermedad de Parkinson. Avances, 8, 16-22.
Francescon, P., Kilby, W., Noll, J., Masi, L., & Sata, N. (2017). Monte Carlo simulated corrections for beam commissioning measurements with circular and MLC shaped fields on the CyberKnife M6 System: a study including diode, microchamber, point scintillator, and synthetic microdiamond detectors, (Vol. 62). Physics in Medicine & Biology.
Friedenthal, S., Moore, A., & Steiner, R. (2012). A Practical Guide to SysML The Systems Modeling Language (2nd ed.). Waltham, Massachusetts, United States : Elsevier.
Gilmore, G., & Jog, M. (2017). Future Perspectives: Assessment Tools and Rehabilitation in the New Age. (H. Chien, & O. Barsottini, Edits.) Movement Disorders Rehabilitation: Springer.
Jankovic, J., & Tolosa, E. (2007). Enfermedad de Parkinson y trastornos del movimiento (5ta Edición ed.). Philadelphia, Pennsylvania, United States: Lippincott Williams & Wilkins.
Linazasoro Cristóbal, G., López del Val, L. J., López García, E., Martínez Martínez, L., & Santos Lasaosa, S. (2012). Parkinson y Discinecias. Madrid, España: Editorial médica panamericana.
López del Val , L. J., & Linazasoro, G. (2012). Párquinson y Discinecias: Abordaje diagnóstico y terapéutico. Madrid, España: Panamericana.
Montoya, M., Muñoz , J., & Henao, O. (2015). Surface EMG based muscle fatigue detection using a low-cost wearable sensor and amplitude-frequency analysis. Actas de Ingeniería, 1, 29-33.
Phinyomark, A., Sirinee , T., Huosheng, H., Pornchai , P., & Limsakul, C. (2012). The Usefulness of Mean and Median Frequencies in Electromyography Analysis. (G. R. Naik, Ed.) doi:http://dx.doi.org/10.5772/50639
Ponce Cruz, P. (2010). Inteligencia Artificial con aplicaciones a la ingeniería (1era Edición ed.). México: Alfaomega.
Rivera, G., & Bonilla, V. (2018). Diseño y construcción de un exo-esqueleto para la mitigación de temblores involuntarios de pronosupinación en personas con parkinson. Universidad Tecnológica Equinoccial, Quito, Ecuador.
Rivera, G., Bonilla, V., & Moya, M. (2019). Exoskeleton prototype to mitigate pronosupination tremors in people with Parkinson's disease. 2018 International Conference on Information Systems and Computer Science (INCISCOS), 16-22. doi: http://doi.ieeecomputersociety.org/10.1109/INCISCOS.2018.00010
Rocon E, G. J.-L. (10 de Octubre de 2012). Biomechanical Loading as an Alternative Treatment for Tremor: A Review of Two Approaches. Tremor and Other Hyperkinet Movements. Obtenido de http://tremorjournal.org/article/view/77
Rocon, E., Ruíz, A. F., Belda-Lois, J. M., Moreno, J. C., Pons, J. L., Raya, R., & Ceres, R. (Abril de 2008). Diseño, Desarrollo y Validación de Dispositivo Robótico para Supresión del Temblor Patológico. Revista Iberoamericana de Automática e Informática, 5(2).
Roland, V. (octubre de 2015). MyoBridge. Obtenido de Github: https://github.com/vroland/MyoBridge/wiki
Romo, H. A., Realpe, J. C., & Jojoa, P. E. (2007). Análisis de Señales EMG Superficiales y su Aplicación en Prótesis de Mano. Avances en Sistemas e Informática, 4(1), 127-136.
Roncon, E., & Pons, J. (2011). Exoeskeletons in Rehabilitation Robotics: Tremor Suppression (Vol. 69). Berlin, Alemania: Springer.
Shah, V. V., Goyal, S., & Palanthandalam-Maadapusi, H. J. (2017). A Possible Explanation of How High-Frequency Deep Brain Stimulation. IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society, vol. 25, pp. 2498-2508.
Tepavac, D., & Schwirtlich, L. (Marzo de 1997). Detection and prediction of FES-induced fatigue. Journal of Electromyography and Kinesiology, 7, 39-50.
Thalmic-Labs. (16 de 01 de 2017). Obtenido de MYO Web site: https://www.myo.com/
Tomaszewski, M. (2015). Obtenido de GitHub Inc.: http://www.mathworks.com/matlabcentral/fileexchange/55817-myo-sdk-matlab-mex-wrapper
VDI-RICHTLINIEN. (Junio de 2004). Design methodology for Mechatronic Systems. 118.
Yang, Y., Bei-sha , T., & Ji-feng , G. (2016). Parkinson’s Disease and Cognitive Impairment. Hindawi Publishing Corporation, 1-8. Obtenido de http://dx.doi.org/10.1155/2016/6734678

Published

2019-03-29

How to Cite

Rivera Cárdenas, G. E., Bonilla, V., Marcelo, M., Mosquera, G., & Litvin, A. V. (2019). Mechatronic device for the analysis and mitigation of involuntary movements in people with Parkinson’s disease. Enfoque UTE, 10(1), pp. 153 - 172. https://doi.org/10.29019/enfoqueute.v10n1.452

Issue

Section

Automation and Control, Mechatronics, Electromechanics, Automotive