Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Publicado
Towards the reduction of the effects of muscle fatigue on myoelectric control of upper limb prostheses
Hacia la reducción de los efectos de la fatiga muscular en el control mioeléctrico de prótesis de miembro superior
DOI:
https://doi.org/10.15446/dyna.v86n208.73401Palabras clave:
electromyography, myoelectric control, muscle fatigue, upper limb prostheses, pattern recognition (en)electromiografía, control mioeléctrico, prótesis de miembro superior, reconocimiento de patrones (es)
Descargas
This paper presents an investigation focused on the impact of muscle fatigue on a pattern recognition scheme for myoelectric control that uses three features sets and a Linear Discriminant Analysis classifier. Separability and repeatability between classes were used to evaluate the features changes while muscle fatigue was induced. Results show that while muscle fatigue is increasing over time, both separability and repeatability of the classes decrease. Finally two training schemes that use data acquired under fatigue, multiconditional training and selective classification, were evaluated using the Total Error Rate (TER). Results indicate that, when LDA classifier was trained whit no-fatigue, moderated fatigue and fatigue data, TER decreased to moderated and fatigue data, but increased to no-fatigue data. On the other hand, using three LDA classifiers to each of the condition, TER decreased to 9.26 % and 11 % in moderated fatigue and fatigue cases, while no-fatigue case was not affected.
Este artículo presenta una investigación centrada en el impacto de la fatiga muscular en un esquema de reconocimiento de patrones para el control mioeléctrico que utiliza tres conjuntos de características y un clasificador análisis discriminante lineal. Los cambios en las características mientras se inducía la fatiga muscular se evalúan mediante la separabilidad y la repetibilidad entre las clases. Los resultados muestran que mientras la fatiga muscular aumenta con el tiempo, tanto la separabilidad como la repetibilidad disminuyen. Finalmente se evaluaron, mediante tasa de error total (TER), dos esquemas de entrenamiento que usan datos adquiridos bajo fatiga: entrenamiento multicondición y clasificación selectiva. Los resultados indican que, utilizando entrenamiento multicondición, el TER disminuyó para fatiga moderada y fatiga, pero aumentó para no-fatiga. Por otro lado, al usar clasificación selectiva, TER disminuyó a 9.26% y 11% en casos fatiga moderada y fatiga, mientras que la condición no-fatiga no se vio afectada.
Referencias
R. Reiter, “Eine neu elecktrokunstand.,” Grenzgeb. Med, vol. 1, pp. 133–135, 1948.
S. C. Jacobson, D. F. Knutti, R. T. Johnson, and H. H. Sears, “Development of the Utah artificial arm,” Dev. Utah Artif. Arm, vol. 29, pp. 249–269, 1982.
P. Geethanjali, “Myoelectric control of prosthetic hands : state-of-the-art review,” Med. Devices Evid. Res., vol. 9, pp. 247–255, 2016.
D. P. Yang, J. D. Zhao, Y. K. Gu, X. Q. Wang, N. Li, L. Jiang, H. Liu, H. Huang, and D. W. Zhao, “An anthropomorphic robot hand developed based on underactuated mechanism and controlled by EMG signals.,” J Bionic Eng, vol. 6, no. 3, pp. 255–263, 2009.
Nianfeng Wang, Kunyi Lao, and Xianmin Zhang, “Desing and Myoelectric Control of an Anthropomorphic Prosthetic Hand,” J. Bionic Eng., vol. 14, pp. 47–59, 2017.
M. Winkler, “Towards multi-movement hand prostheses: combining adaptive classification with high precision socket,” in Proceedings of the 2nd European Conference on Technically Assisted Rehabilitation., 2009.
M. A. Oskoei and H. Hu, “Support vector machine-based classification scheme for myoelectric control applied to upper limb,” Ieee Trans Biomed Eng, vol. 8, no. 55, pp. 1956–1965, 2008.
D. Yang, “EMG pattern recognition and grasping force esti mation: Improvement to the myocontrol of multi-DOF prosthetic hands,” in Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis (MO), Piscataway (NJ), 2009, pp. 516–521.
G. Matrone, C. Cipriani, E. L. Secco, M. C. Carrozza, and G. Magenes, “Bio-inspired controller for a dexterous prosthetic hand based on principal components analysis.,” in Proc IEEE Eng Med Biol Soc., 2009, vol. 50, pp. 22–25.
J. L. Pons, R. Ceres, E. Rocon, S. Levin, I. Markovitz, B. Saro, D. Reynaerts, W. Van Moorleghem, and L. Bueno, “Virtual reality training and EMG control of the MANUS hand prosthesis.,” Robotica, vol. 23, no. 3, pp. 311–317, 2005.
Scheme E. and Englehart K., “Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use,” J. Rehabil. Res. Dev., vol. 48, no. 6, pp. 643–660, 2011.
L. Hargrove, K. Englehart, and B. Hudgins, “The effect of electrode displacements on pattern recognition based myoelectric control.,” presented at the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006.
“A training strategy to reduce classification degradation due to electrode displacements in pattern recognition recognition based myoelectric control,” Biomed Signal Process Control, vol. 3, no. 2, pp. 175–80, 2008.
A. Radmand, E. Scheme, I. Member, K. Englehart, and I. S. Member, “A Characterization of the Effect of Limb Position on EMG Features to Guide the Development of Effective Prosthetic Control Schemes,” pp. 662–667, 2014.
T.D. Lalitharatne, Y. Hayashi, K. Teramoto, and K. Kiguchi, “A Study on effects of Muscle fatigue on EMG-based Control for Human Upper-Limb Power-Assist,” in Proc. of 2012 IEEE 6th International Conference on Information and Automation for Sustainability, 2012, pp. 124–128.
Alexander G. Barszap, Ida-Maria Skavhaug, and Sanjay S. Joshi, “Effects of muscle fatigue on the usability of a myoelectric human-computer interface,” Hum. Mov. Sci., vol. 49, pp. 225–238, 2016.
Gonzalez-Izal M., Malanda, A., Gorostiaga, E., and Izquierdo, M., “Electromyographic models to assess muscle fatigue,” J. Electromyogr. Kinesiol., vol. 22, pp. 501–512, 2012.
Park, E. and Meek, S., “Fatigue compensation of the electromyographic signal for prosthetic control and force estimation,” Ieee Trans. Biomed. Eng., vol. 40, pp. 1019–1023, 1993.
Song, J., Jung, J., Lee, S., and Bien, Z., “Robust EMG pattern recognition to muscular fatigue effect for powered wheelchair control.,” J. Intell. Fuzzy Syst., vol. 20, pp. 3–12, 2009.
E. Scheme and K. Englehart, “A flexible user interface for rapid prototyping a advanced real-time myoelectric control scheme,” presented at the MyoElectric Controls/Powered Prosthetics Symposium, Fredericton,NB,Canada, 2008.
L. H. Smith, T.A. Kuiken, L.J. Hargrove, B.A. Lock,, “Determining the optimal window length for pattern recognition-based myoelectric control: Balancing the competing effects of classification error and controller delay.,” Ieee Transneural Systrehabileng, vol. 19, no. 2, pp. 186–192, 2011.
B. Hudgins, P. A. Parker, and R. N. Scott, “A new strategy for multifunction myoelectric control.,” Ieee Trans. Biomed. Eng., vol. 40, no. 1, pp. 82–94, Jan. 1993.
D. Graupe and W. Cline, “Functional Separation EMG Signal via ARMA identification methods for prosthesis control purposes,” Ieee Trans Syst. Man Cybern., vol. 5, pp. 252–259, 1975.
P. C. Doershuk and D. E. Gustafson, “Upper extremity limb function discrimination using EMG signal analysis,” Ieee Trans Biomed Eng, vol. 30, pp. 18–28, 1983.
A. Phinyomark, P. Phupattaranond, and C. Limsakul, “Feature Reduction and Selection for EMG signal Classification,” Expert Syst. Appl., vol. 39, no. 8, pp. 7420–7431, 2012.
S. Lee and G. Sardis, “The control of a prosthetic arm by EMG pattern recognition.,” Ieee Trans Autom. Contr, vol. 29(4), pp. 290–302, 1984.
G. Sardis and T. Gootee, “EMG pattern analysis and classification for a prosthetic arm,” IEEE Trans Biomed Eng, vol. 29, no. 6, pp. 403–412, 1982.
Liu X,, Zhou R,, Yang L., and Li G., “Performance of various EMG features in identifying arm movements for control of multifunctional prostheses,” in Proceedings of IEEE Youth Conference on Information, Computing and Telecommunication, Beijin, China, 2009.
N.E. Bunderson and T.A. Kuiken., “Quantification of feature space changes with experience during electromyogram pattern recognition control.,” Ieee Trans. Neural Syst. Rehabil. Eng. Publ. IEEE Eng. Med. Biol. Soc., vol. 20, no. 3, pp. 239–246, 2012.
Cómo citar
IEEE
ACM
ACS
APA
ABNT
Chicago
Harvard
MLA
Turabian
Vancouver
Descargar cita
CrossRef Cited-by
1. Vaheh Nazari, Yong-Ping Zheng. (2025). A Highly Efficient HMI Algorithm for Controlling a Multi-Degree-of-Freedom Prosthetic Hand Using Sonomyography. Sensors, 25(13), p.3968. https://doi.org/10.3390/s25133968.
2. Darkhan Zholtayev, Amna Riaz Khawaja, Kassymbek Ozhikenov, Aiman Ozhikenova, Ussen Shylmyrza, Beibit Abdikenov. (2025). Fast-Calibration-Based Control for an Upper-Limb Prosthesis. IEEE Access, 13, p.202438. https://doi.org/10.1109/ACCESS.2025.3634486.
3. Bingbin Wang, Ernest N. Kamavuako. (2021). Correlation between the stability of feature distribution and classification performance in sEMG signals. 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART). , p.1. https://doi.org/10.1109/BioSMART54244.2021.9677831.
4. Bingbin Wang, Jinglin Li, Levi Hargrove, Ernest Nlandu Kamavuako. (2024). Unravelling Influence Factors in Pattern Recognition Myoelectric Control Systems: The Impact of Limb Positions and Electrode Shifts. Sensors, 24(15), p.4840. https://doi.org/10.3390/s24154840.
5. Guangfei Wu, Wenqing Gu, Yi Luo, Xin Zhang, Lei Li, Jingming Hou, Haoyue Deng, Wensheng Hou, Lin Chen, Xing Wang. (2025). Study on Prosthetic Hand Proprioception Feedback Based on Hybrid Vibro-Electrotactile Stimulation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 33, p.2967. https://doi.org/10.1109/TNSRE.2025.3593354.
6. Bingbin Wang, Ernest N. Kamavuako. (2024). Weighted Average Confidence Score-based Ensemble Classification to Mitigate the Effect of Time on Myoelectric Control. 2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON). , p.1204. https://doi.org/10.1109/MELECON56669.2024.10608735.
7. Masahiro KOMATSU, Rinya TOYAMA, Feng WANG. (2024). A Study to Improve Operating Accuracy of Electric Prosthetic Hand Using Tactile Feature Sensing System. Journal of the Japan Society of Applied Electromagnetics and Mechanics, 32(2), p.369. https://doi.org/10.14243/jsaem.32.369.
8. Felipe Ramirez Cortes, María Gaitán-Padilla, Marcelo Eduardo Vieira Segatto, Maria J. Pontes, Carlos A. Cifuentes, Camilo Arturo Rodríguez Díaz, Israel Gannot, Katy Roodenko. (2025). Accuracy comparison for prosthesis activation using a commercial sEMG sensor and an FMG sensor based on FBG and 3D-printing. Optical Fibers and Sensors for Medical Diagnostics, Treatment, and Environmental Applications XXV. , p.52. https://doi.org/10.1117/12.3043577.
9. Bingbin Wang, Jinglin Li, Ernest N. Kamavuako. (2026). Fusion of surface electromyogram signals with IMU sensors and dynamic training to mitigate the effect of limb position. Biomedical Signal Processing and Control, 113, p.109144. https://doi.org/10.1016/j.bspc.2025.109144.
10. Bingbin Wang, Levi Hargrove, Xinqi Bao, Ernest N. Kamavuako. (2022). Surface EMG Statistical and Performance Analysis of Targeted-Muscle-Reinnervated (TMR) Transhumeral Prosthesis Users in Home and Laboratory Settings. Sensors, 22(24), p.9849. https://doi.org/10.3390/s22249849.
Dimensions
PlumX
Visitas a la página del resumen del artículo
Descargas
Licencia
Derechos de autor 2019 DYNA
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
El autor o autores de un artículo aceptado para publicación en cualquiera de las revistas editadas por la facultad de Minas cederán la totalidad de los derechos patrimoniales a la Universidad Nacional de Colombia de manera gratuita, dentro de los cuáles se incluyen: el derecho a editar, publicar, reproducir y distribuir tanto en medios impresos como digitales, además de incluir en artículo en índices internacionales y/o bases de datos, de igual manera, se faculta a la editorial para utilizar las imágenes, tablas y/o cualquier material gráfico presentado en el artículo para el diseño de carátulas o posters de la misma revista.
