Document Detail


Multi-complexity Ensemble Measures for Gait Time Series Analysis: Application to Diagnostics, Monitoring and Biometrics.
MedLine Citation:
PMID:  25381104     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Previously, we have proposed to use complementary complexity measures discovered by boosting-like ensemble learning for the enhancement of quantitative indicators dealing with necessarily short physiological time series. We have confirmed robustness of such multi-complexity measures for heart rate variability analysis with the emphasis on detection of emerging and intermittent cardiac abnormalities. Recently, we presented preliminary results suggesting that such ensemble-based approach could be also effective in discovering universal meta-indicators for early detection and convenient monitoring of neurological abnormalities using gait time series. Here, we argue and demonstrate that these multi-complexity ensemble measures for gait time series analysis could have significantly wider application scope ranging from diagnostics and early detection of physiological regime change to gait-based biometrics applications.
Authors:
Valeriy Gavrishchaka; Olga Senyukova; Kristina Davis
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Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Advances in experimental medicine and biology     Volume:  823     ISSN:  0065-2598     ISO Abbreviation:  Adv. Exp. Med. Biol.     Publication Date:  2015  
Date Detail:
Created Date:  2014-11-8     Completed Date:  -     Revised Date:  2014-11-9    
Medline Journal Info:
Nlm Unique ID:  0121103     Medline TA:  Adv Exp Med Biol     Country:  -    
Other Details:
Languages:  ENG     Pagination:  107-126     Citation Subset:  -    
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