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Open AccessArticle

Evaluating Muscle Activation Models for Elbow Motion Estimation

1
Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada
2
Canadian Surgical Technologies and Advanced Robotics, Lawson Health Research Institute, London, ON N6A 5A5, Canada
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(4), 1004; https://doi.org/10.3390/s18041004
Received: 17 January 2018 / Revised: 12 March 2018 / Accepted: 22 March 2018 / Published: 28 March 2018
(This article belongs to the Special Issue Smart Sensors for Mechatronic and Robotic Systems)
Adoption of wearable assistive technologies relies heavily on improvement of existing control system models. Knowing which models to use and how to improve them is difficult to determine due to the number of proposed solutions with relatively little broad comparisons. One type of these models, muscle activation models, describes the nonlinear relationship between neural inputs and mechanical activation of the muscle. Many muscle activation models can be found in the literature, but no comparison is available to guide the community on limitations and improvements. In this research, an EMG-driven elbow motion model is developed for the purpose of evaluating muscle activation models. Seven muscle activation models are used in an optimization procedure to determine which model has the best performance. Root mean square errors in muscle torque estimation range from 1.67–2.19 Nm on average over varying input trajectories. The computational resource demand was also measured during the optimization procedure, as it is an important aspect for determining if a model is feasible for use in a particular wearable assistive device. This study provides insight into the ability of these models to estimate elbow motion and the trade-off between estimation accuracy and computational demand. View Full-Text
Keywords: electromyography; elbow model; muscle activation model; estimation accuracy; computational resources electromyography; elbow model; muscle activation model; estimation accuracy; computational resources
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MDPI and ACS Style

Desplenter, T.; Trejos, A.L. Evaluating Muscle Activation Models for Elbow Motion Estimation. Sensors 2018, 18, 1004. https://doi.org/10.3390/s18041004

AMA Style

Desplenter T, Trejos AL. Evaluating Muscle Activation Models for Elbow Motion Estimation. Sensors. 2018; 18(4):1004. https://doi.org/10.3390/s18041004

Chicago/Turabian Style

Desplenter, Tyler; Trejos, Ana L. 2018. "Evaluating Muscle Activation Models for Elbow Motion Estimation" Sensors 18, no. 4: 1004. https://doi.org/10.3390/s18041004

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