Multi-complexity ensemble measures for gait time series analysis: application to diagnostics, monitoring and biometrics.
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.
Department of Physics, West Virginia University, Morgantown, WV, 26506, USA, gavrishchaka@gmail.com.
Valeriy
V
Gavrishchaka
Olga
O
Senyukova
Kristina
K
Davis
D000465:Algorithms; D001699:Biometry; D019277:Entropy; D005684:Gait / Q000502:physiology*; D020233:Gait Disorders, Neurologic / Q000175:diagnosis / Q000503:physiopathology*; D006801:Humans; D008954:Models, Biological; D013997:Time Factors