NDLI: Prediction of muscle activity during loaded movements of the upper limb

Content Provider	Springer Nature : BioMed Central
Author	Tibold, Robert Fuglevand, Andrew J
Abstract	Background 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.
Related Links	https://jneuroengrehab.biomedcentral.com/counter/pdf/10.1186/1743-0003-12-6.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
ISSN	17430003
DOI	10.1186/1743-0003-12-6
Journal	Journal of NeuroEngineering and Rehabilitation
Issue Number	1
Volume Number	12
Language	English
Publisher	BioMed Central
Publisher Date	2015-01-15
Access Restriction	Open
Subject Keyword	Neurosciences Neurology Rehabilitation Medicine Biomedical Engineering and Bioengineering Functional electrical stimulation Electromyography Artificial neural networks Kinematics Grip force Upper limb
Content Type	Text
Resource Type	Article
Subject	Health Informatics Rehabilitation
Journal Impact Factor	5.2/2023
5-Year Journal Impact Factor	5.6/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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