Improvement of the reflective learning in engineering education using MATLAB for problems solving

Vicéns, José L.; Parra, Blas Zamora; Ojados, Dolores

doi:10.1002/cae.21748

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…In that sense, symbolic analysis is preferred since it provides analytic expressions which allow a deeper insight into the circuit behavior [28]. Additionally, some other software tools for systems analysis are preferred in both industry and education, as is the case of MATLAB [11,14,22,29]. An example of an EDA tool developed for the symbolic analysis of electric and electronic circuits is EI-SCAM.…”

Section: Introductionmentioning

confidence: 99%

EI‐SCAM as a teaching tool in an undergraduate course in analog and high‐frequency circuits

Sánchez-Gaspariano

Vivaldo‐de‐la‐Cruz

Muñoz‐Pacheco

et al. 2022

Comp Applic In Engineering

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EI-SCAM is a very useful tool capable of carrying out symbolic, semi-symbolic, and numerical solutions for the electrical variables of a circuit under analysis. The goal of the research reported herein is to assess the effectiveness of the program as a teaching tool for circuit analysis of Automotive System Engineering students. To this aim, EI-SCAM has been used for at least 5 years, from 2017 to 2021, in the undergraduate course Interfaces, Communications,

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Section: Introductionmentioning

confidence: 99%

EI‐SCAM as a teaching tool in an undergraduate course in analog and high‐frequency circuits

Sánchez-Gaspariano

Vivaldo‐de‐la‐Cruz

Muñoz‐Pacheco

et al. 2022

Comp Applic In Engineering

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The Neuromusculoskeletal Modeling Pipeline: MATLAB-based Model Personalization and Treatment Optimization Functionality for OpenSim

Hammond,

Williams,

Vega

et al. 2024

Preprint

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Neuromusculoskeletal injuries including osteoarthritis, stroke, spinal cord injury, and traumatic brain injury affect roughly 19% of the U.S. adult population. Standardized interventions have produced suboptimal functional outcomes due to the unique treatment needs of each patient. Strides have been made to utilize computational models to develop personalized treatments, but researchers and clinicians have yet to cross the "valley of death" between fundamental research and clinical usefulness. This article introduces the Neuromusculoskeletal Modeling (NMSM) Pipeline, two MATLAB-based toolsets that add Model Personalization and Treatment Optimization functionality to OpenSim. The two toolsets facilitate computational design of individualized treatments for neuromusculoskeletal impairments through the use of personalized neuromusculoskeletal models and predictive simulation. The Model Personalization toolset contains four tools for personalizing 1) joint structure models, 2) muscle-tendon models, 3) neural control models, and 4) foot-ground contact models. The Treatment Optimization toolset contains three tools for predicting and optimizing a patient's functional outcome for different treatment options using a patient's personalized neuromusculoskeletal model with direct collocation optimal control methods. Support for user-defined cost functions and model modification functions facilitate simulation of a vast number of possible treatments. An NMSM Pipeline use case is presented for an individual post-stroke with impaired walking function, where the goal was to predict how the subject's neural control could be changed to improve walking speed without increasing metabolic cost. First the Model Personalization toolset was used to develop a personalized neuromusculoskeletal model of the subject starting from a generic OpenSim full-body model and experimental walking data (video motion capture, ground reaction, and electromyography) collected from the subject at his self-selected speed. Next the Treatment Optimization toolset was used with the personalized model to predict how the subject could recruit existing muscle synergies more effectively to reduce muscle activation disparities between the paretic and non-paretic legs. The software predicted that the subject could increase his walking speed by 60% without increasing his metabolic cost per unit time by modifying existing muscle synergy recruitment. This hypothetical treatment demonstrates how NMSM Pipeline tools could allow researchers working collaboratively with clinicians to develop personalized neuromusculoskeletal models of individual patients and to perform predictive simulations for the purpose of designing personalized treatments that maximize a patient's post-treatment functional outcome.

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Introduction to Computing: Interdisciplinary Course Design

Subburaj

Urban

2018

2018 IEEE Frontiers in Education Conference (FIE)

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Improvement of the reflective learning in engineering education using MATLAB for problems solving

Cited by 4 publications

References 19 publications

EI‐SCAM as a teaching tool in an undergraduate course in analog and high‐frequency circuits

EI‐SCAM as a teaching tool in an undergraduate course in analog and high‐frequency circuits

The Neuromusculoskeletal Modeling Pipeline: MATLAB-based Model Personalization and Treatment Optimization Functionality for OpenSim

Introduction to Computing: Interdisciplinary Course Design

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