Chau T
Bloorview MacMillan Centre, 350 Rumsey Road, Toronto, Ontario, Canada M4G 1R8.
Gait Posture. 2001 Apr;13(2):102-20. doi: 10.1016/s0966-6362(00)00095-3.
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.
传统上，多变量步态数据的分析颇具挑战性。本综述的第一部分探讨了模糊、多变量统计和分形方法在步态数据分析中的应用。第二部分将这一重要综述扩展至人工神经网络和小波在步态数据分析中的应用。综述最后给出了选择替代步态数据分析方法的实用指南。研究发现，神经网络是过去10年中用于步态数据分析最普遍的非传统方法。多个步态信号相互作用的解释以及步态波形的定量比较被确定为需要进一步研究的重要数据分析主题。
