Hi all, I wonder if anyone is familiar with using dynamic time warping (DTW) and might have some insights into how to implement it. I am specifically looking at using it in place of time normalization - for example, for a data set consisting of 10 subjects' knee joint angles during gait - each subject's curve would typically be time normalized to 100% of stance, after which a mean at each point would be taken to represent the group. This works fairly well most of the time, especially in gait, but in some applications, peaks and transitions can be offset between subjects so that the mean dilutes these peaks. DTW "warps" each curve, rather than simply stretching or compressing them, so that the peaks are mostly lined up. This seems like a good thing to adopt in biomechanics applications, but I have yet to see any ready made implementations that I could easily adapt to specific data sets. Matlab has a dtw function which appears to work well on two curves, but I'm not sure how to convert it to apply to more than two. In addition, comments on pros and cons of dtw for biomechanics applications would also be welcome, as well as comments on how well it would be accepted in publications, particularly as whole waveform analysis (e.g. functional data analysis or statistical parametric mapping) becomes more common.