A review of analytical techniques for gait data. Part 2: neural network and wavelet methods
Identifieur interne : 001C62 ( Main/Merge ); précédent : 001C61; suivant : 001C63A review of analytical techniques for gait data. Part 2: neural network and wavelet methods
Auteurs : Tom ChauSource :
- Gait & Posture [ 0966-6362 ] ; 2000.
English descriptors
- KwdEn :
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.
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DOI: 10.1016/S0966-6362(00)00095-3
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ISTEX:FEA2ACBD3C7AC498542CEE1C2725C97901758366Le document en format XML
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<term>FD, Fractal dynamics</term>
<term>Gait analysis</term>
<term>MCA, Multiple correspondence analysis</term>
<term>NN, Neural networks</term>
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<front><div type="abstract" xml:lang="en">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.</div>
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