You can use diff or gradient.
Decide what minimum rate of change is acceptable:
And then to find the point of interest:
index = find( abs(diff(x)) > tolerance )
However, this is going to find ALL points that exceed your tolerance. I don't know what your data is, but if you say it accelerates, then every point after the turning point is going to be returned. Also, unless there is a theoretical reason behind your 'small changes', you might need to detect the tolerance. That's always more fiddly.
Another way to go about this is to detect a 'baseline' and remove it from your data. It might not be relevant to you... I've used this to analyse qPCR experiments for gene research. We had sigmoid-like curves where the initial part of the curve was linear. I had to detect that linear section, find a regression line through it, and subtract that line from the data. It might not be relevant to you. The theory behind this is to remove from the data any constant change that is proven to exist and must be corrected.
How many turning points do you expect in each vector, or is this unknown? What does the data look like?