There are many options for interpreting and presenting motion analysis data. To aid the interpretation of reduced data – discrete values such as peaks – it is useful to present time-normalised mean curves to enable qualitative interpretation of the data. It should always be remembered that the average curve smoothes the peaks of the individual trials as a result of inter-trial temporal differences. The most appropriate way of presenting these data will depend on your project, and the variability between trials (Figure 3.3). If all trials are very similar, a single mean curve may be most appropriate.
However, an indication of the variability between trials is usually helpful, and a mean ± 1 standard deviation curves will often be more informative. In other cases, it may be better to plot all of the individual curves on the same graph, to give the reader all of the information about inter-trial variability. Mean values for the key variables are reported alongside these plots.
Depending on the research question asked, other types of analysis may be appropriate. For example, much current research is concerned with the coordination between segments and how coordination variability may affect injury risk.
The common thread in current analyses of human movement data is the extraction of discrete variables from continuous time-series data. The discrete variables are used to answer your research questions statistically. To maintain the statistical power of your study, it is preferable to identify a few key variables that will contribute the most to your hypotheses and present other associated variables, continuous and discrete, descriptively to aid in the interpretation of your statistical results.