Mobility Deficit Identification and Compensation through an Artificial Neural Network and Adaptive Controller Design during Gait

Authors

  • Silvia L. Chaparro-Cárdenas Universidad de Investigación y Desarrollo - UDI https://orcid.org/0000-0002-2589-259X
  • Eduardo Castillo-Castañeda Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional https://orcid.org/0000-0002-3307-6947
  • Alejandro A. Lozano-Guzmán Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional

Keywords:

Adaptive controller, Artificial neural network, Knee orthosis, Mobility deficit compensation

Abstract

This article presents a progressive compensation strategy for gait recovery in patients with different degrees of limited knee mobility, based on angular analysis and muscle electrical activity, and artificial intelligence. Ten subjects were tested during gait on a flat surface simulating 4 conditions of limited knee mobility with an active knee brace. Data on the amplitude of the electrical signal from 3 leg muscles were analyzed: rectus femoris, tibialis anterior, and gastrocnemius. In addition to the electromyography sensors, an angular position sensor was placed on the knee joint. An artificial neural network was trained to identify the type of limitation of each patient in their muscle activity. A knee orthosis with a linear actuator was designed to compensate for the loss of force during knee flexion-extension movement, according with limiting condition. The actuator trajectory is controlled through a model reference adaptive controller with a fuzzy logic-based adaptation mechanism. The simulation demonstrates the efficiency of this strategy, despite the high-amplitude disturbances in the system.

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Author Biographies

Silvia L. Chaparro-Cárdenas, Universidad de Investigación y Desarrollo - UDI

Silvia L. Chaparro-Cárdenas received the electronic engineer degree with the with a specialty in process control from the Fundación Universitaria de San Gil UNISANGIL, Colombia, in 2013. The master degree and the Ph.D. degree in advanced technology, with a speciality in mechatronics, from the Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada (CICATA), Instituto Politécnico Nacional, Mexico, in 2016 and 2021, respectively. Her research interests include fuzzy systems, hybrid systems, robotic rehabilitation devices, neural networks, artificial intelligence, signal processing and intelligent control. She has published articles in in data analysis, control systems and artificial intelligence. She is currently full-time professor at the Universidad de Investigación y Desarrollo UDI, in Colombia.

Eduardo Castillo-Castañeda, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional

Eduardo Castillo-Castañeda, a Mechanical Electrical Engineer, graduated from Universidad Nacional Autonoma de Mexico in 1987. In 1994 he received his Ph.D. in Automatic Control from the Institute National Polytechnique de Grenoble, France. In 2015, he received the Training Certificate "Leaders in Innovation" by the Royal Academy of Engineering and the University of Oxford, in the United Kingdom. Currently, he is member of the Technical Committee for Robotics and Mechatronics of IFToMM, the International Federation for the Promotion of Mechanism and Mechanics Science. Since 2007, he is full time professor at the Instituto Politécnico Nacional, in Mexico.

Alejandro A. Lozano-Guzmán, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional

Alejandro A. Lozano-Guzmán received the B.S. in Mechanical and Electrical Engineering degree at the Mexico National University, 1975 and the M.S degree in Mechanical Engineering, at the same University in 1977. He obtained the Ph.D. in Mechanical Engineering, University of New Castle Upon Tyne, England in 1982. He is a Professor at CICATA Querétaro, in México. His research focusses on mechanical systems analysis, particularly mechanical vibrations and condition monitoring systems. He has published articles in mechanical systems analysis and is a member of the National Research System.

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Published

2024-11-14

How to Cite

Chaparro Cárdenas, S. L., Castillo-Castañeda, E. ., & Lozano-Guzmán, A. A. (2024). Mobility Deficit Identification and Compensation through an Artificial Neural Network and Adaptive Controller Design during Gait. IEEE Latin America Transactions, 22(12), 1063–1072. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/8957

Issue

Vol. 22 No. 12 (2024): Ordinary Issue

Section

Electronics