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Abstract
Multivariate gait data have traditionally been challenging to analyze. Part 1 of this
review explored applications of fuzzy, multivariate statistical and fractal methods
to gait data analysis. Part 2 extends this critical review to the applications of
artificial neural networks and wavelets to gait data analysis. The review concludes
with a practical guide to the selection of alternative gait data analysis methods.
Neural networks are found to be the most prevalent non-traditional methodology for
gait data analysis in the last 10 years. Interpretation of multiple gait signal interactions
and quantitative comparisons of gait waveforms are identified as important data analysis
topics in need of further research.