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.
CITATION STYLE
Gavrishchaka, V., Senyukova, O., & Davis, K. (2015). Multi-complexity ensemble measures for gait time series analysis: Application to diagnostics, monitoring and biometrics. Advances in Experimental Medicine and Biology, 823, 107–126. https://doi.org/10.1007/978-3-319-10984-8_6
Mendeley helps you to discover research relevant for your work.