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Prof. Karina Isabel Espinosa Espejel
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Academic Career Length
4Years (2023-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
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Journal Article
YRYukio Rosales-Luengas
Yukio Rosales-LuengasKEKarina Isabel Espinosa Espejel
Karina Isabel Espinosa Espejel
Jesus Ricardo Lopez-GutierrezSSSalaza Sergio
Salaza Sergio
Rogelio LozanoThis paper presents the development of an instrumented exoskeleton with baropodometry, electromyography, and torque sensors. The six degrees of freedom (Dof) exoskeleton has a human intention detection system based on a classifier of electromyographic signals coming from four sensors placed in the muscles of the lower extremity together with baropodometric signals from four resistive load sensors placed at the front and rear parts of both feet. In addition, the exoskeleton is instrumented with four flexible actuators coupled with torque sensors. The main objective of the paper was the development of a lower limb therapy exoskeleton, articulated at hip and knees to allow the performance of three types of motion depending on the detected user’s intention: sitting to standing, standing to sitting, and standing to walking. In addition, the paper presents the development of a dynamical model and the implementation of a feedback control in the exoskeleton.
Journal Article
KEKarina Isabel Espinosa Espejel
Karina Isabel Espinosa EspejelYRYukio Rosales-Luengas
Yukio Rosales-LuengasSSSalaza Sergio
Salaza SergioJesus Ricardo Lopez-GutierrezRogelio LozanoThis article presents the design of a control algorithm based on Artificial Neural Networks (ANNs) applied to a lower-limb exoskeleton, which is aimed to carry out walking trajectories during lower-limb rehabilitation. The interaction between the patient and the exoskeleton leads to model uncertainties and external disturbances that are always present. For this reason, the proposed control considers that the non-linear part of the model is unknown and is perturbed by external disturbances, which are estimated by an active disturbance rejection control via Artificial Neural Networks. To validate the proposed approach, a numerical simulation and an experimental implementation of the ANN-Controller are developed.
About Me
Karina I. Espinosa-Espejel was born in Mexico State, Mexico, on November 5, 1995. She received her B.S. degree in Bionics Engineering from the National Polytechnic Institute (IPN), Mexico City, Mexico, in 2018, and her M.Sc. degree in Autonomous Systems from the Advanced Studies Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Mexico, in 2021.
Espinosa has done research stays at the Laboratory of Instrumentation and Signal Processing, Mexico, with the topic of electrogastrographic signal processing; at the National Rehabilitation Institute (INR), Mexico, with the topic of brain–computer interfaces; and at the University of Compiègne, France, with the topic of disturbance observers for perturbed systems.
She is currently a Ph.D. student at the National Laboratory of Autonomous Vehicles and Exoskeletons (LANAVEX) at CINVESTAV-IPN. Her research includes rehabilitation robotic systems, artificial intelligence, and control systems.