Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 16, Issue 5, Pages 1259-1270Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2008.924326
Keywords
Combined uncertainty measures; continuous wavelets; fuzziness measures; gait analysis; generalized maximum uncertainty principle; temporal feature extraction
Funding
- American Horse Foundation
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Uncertainty measures model different types of uncertainty that are inherent in complex information systems. Measures that model either fuzzy or probabilistic uncertainty types have been explored in the literature. This paper shows that a combination of fuzzy and probabilistic uncertainty types, combined with the generalized maximum uncertainty principle, can be applied to time-series sequence classification and analysis. We present a novel algorithm that selects a wavelet from a wavelet library such that it best represents a time-series sequence, in a maximum uncertainty sense. Transformation coefficients are combined together in feature vectors that capture sequence temporal trends. A neural network is trained and tested using extracted gait sequence temporal features. Re-suits have shown that models that combine together fuzzy and probabilistic uncertainty types better classify time-series gait sequences.
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