Prediction of muscle activity during loaded movements of the upper limb
AffiliationDepartments of Physiology and Neuroscience, University of Arizona
KeywordsFunctional electrical stimulation
Artificial neural networks
MetadataShow full item record
CitationTibold and Fuglevand Journal of NeuroEngineering and Rehabilitation 2015, 12:6 http://www.jneuroengrehab.com/content/12/1/6
Rights© 2015 Tibold and Fuglevand; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0),
Collection InformationThis item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at firstname.lastname@example.org.
AbstractBACKGROUND: Accurate prediction of electromyographic (EMG) signals associated with a variety of motor behaviors could, in theory, serve as activity templates needed to evoke movements in paralyzed individuals using functional electrical stimulation. Such predictions should encompass complex multi-joint movements and include interactions with objects in the environment. METHODS: Here we tested the ability of different artificial neural networks (ANNs) to predict EMG activities of 12 arm muscles while human subjects made free movements of the arm or grasped and moved objects of different weights and dimensions. Inputs to the trained ANNs included hand position, hand orientation, and thumb grip force. RESULTS: The ability of ANNs to predict EMG was equally as good for tasks involving interactions with external loads as for unloaded movements. The ANN that yielded the best predictions was a feed-forward network consisting of a single hidden layer of 30 neural elements. For this network, the average coefficient of determination (R2 value) between predicted and actual EMG signals across all nine subjects and 12 muscles during movements that involved episodes of moving objects was 0.43. CONCLUSION: This reasonable accuracy suggests that ANNs could be used to provide an initial estimate of the complex patterns of muscle stimulation needed to produce a wide array of movements, including those involving object interaction, in paralyzed individuals.
PubMed Central IDPMC4326445
VersionFinal published version
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