Document Detail


Off-the-shelf mobile handset environments for deploying accelerometer based gait and activity analysis algorithms.
MedLine Citation:
PMID:  19964383     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Over the last decade, there has been substantial research interest in the application of accelerometry data for many forms of automated gait and activity analysis algorithms. This paper introduces a summary of new "of-the-shelf" mobile phone handset platforms containing embedded accelerometers which support the development of custom software to implement real time analysis of the accelerometer data. An overview of the main software programming environments which support the development of such software, including Java ME based JSR 256 API, C++ based Motion Sensor API and the Python based "aXYZ" module, is provided. Finally, a sample application is introduced and its performance evaluated in order to illustrate how a standard mobile phone can be used to detect gait activity using such a non-intrusive and easily accepted sensing platform.
Authors:
Martin Hynes; Han Wang; Liam Kilmartin
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Publication Detail:
Type:  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:  2009     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2009  
Date Detail:
Created Date:  2009-12-07     Completed Date:  2010-03-17     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:  5187-90     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Acceleration*
Algorithms*
Cellular Phone*
Gait / physiology*
Humans
Motor Activity / physiology*

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


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