  The methodological contribution in this paper is motivated by biomechanical
studies where data characterizing human movement are waveform curves
representing joint measures such as flexion angles, velocity, acceleration, and
so on. In many cases the aim consists of detecting differences in gait patterns
when several independent samples of subjects walk or run under different
conditions (repeated measures). Classic kinematic studies often analyse
discrete summaries of the sample curves discarding important information and
providing biased results. As the sample data are obviously curves, a Functional
Data Analysis approach is proposed to solve the problem of testing the equality
of the mean curves of a functional variable observed on several independent
groups under different treatments or time periods. A novel approach for
Functional Analysis of Variance (FANOVA) for repeated measures that takes into
account the complete curves is introduced. By assuming a basis expansion for
each sample curve, two-way FANOVA problem is reduced to Multivariate ANOVA for
the multivariate response of basis coefficients. Then, two different approaches
for MANOVA with repeated measures are considered. Besides, an extensive
simulation study is developed to check their performance. Finally, two
applications with gait data are developed.
