Document Detail

Artificial neural network prediction using accelerometers to control upper limb FES during reaching and grasping following stroke.
MedLine Citation:
PMID:  17945746     Owner:  NLM     Status:  MEDLINE    
This work investigates arm acceleration as a control signal for functional electrical stimulation (FES) of the upper limb during reaching and grasping. We segment the reach and grasp motion into phases and present an artificial neural network (ANN) approach that estimates the phase of the reaching cycle from accelerometer signals. We then select the stimulator command that maximizes successful triggering without unnecessary risk to the patient's safety. Our results suggest that the algorithm successfully generalizes between sessions and patients but is less successful at generalizing between different motions.
Phil Tresadern; Sibylle Thies; Laurence Kenney; David Howard; John Y Goulermas
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Publication Detail:
Type:  Evaluation Studies; Journal Article    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  1     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2006  
Date Detail:
Created Date:  2007-10-23     Completed Date:  2008-03-13     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2916-9     Citation Subset:  IM    
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MeSH Terms
Aged, 80 and over
Arm / physiopathology
Biomedical Engineering
Hemiplegia / physiopathology,  rehabilitation,  therapy
Man-Machine Systems
Movement / physiology
Neural Networks (Computer)*
Stroke / physiopathology,  rehabilitation,  therapy*
Transcutaneous Electric Nerve Stimulation*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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