Kinect normalize depth

Question

I have some Kinect data of somebody standing (reasonably) still and performing sets of punches. I am given it in the format of an x,y,z co-ordinate for each joint of which they are 20, so I have 60 data points per frame.

I'm trying to perform a classification task on the punches however I'm having some problems normalising my data. As you can see from the graph there are sections with much higher 'amplitude' than the others, my belief is that this is due to how close that person was to the kinect sensor when the readings were taken. (The graph is actually the first principal coefficient obtained by PCA for each frame, multiple sequences of the same punch are strung together in this graph)

Looking back at the data files it looks like those that are 'out' have a z co-ordinate (depth from sensor) of ~2.7 where as the others tent to hover around 3.3-3.6.

How can I perform a normalization with the depth values to make them closer to each other for each sequence? I've already tried differentiation to get the velocity, although it helps to normalise the output actually ends up too similar and makes it very hard to classify.

Edit: I should mention I am already using a normalization method by subtracting the hip position from each joint in an attempt to make the co-ordinates relative.

Answer 1

The Kinect can output some strange values when the person that is tracked is standing near the edges of the view of the Kinect. I would either completly ignore these data or just replace the data with an average of the previous 2 and next 2.

For example: 1,2,1,12,1,2,3

Replace 12 with (2 + 1 + 1 + 2) / 4 = 1.5

You can basically do this with the whole array of values you have, this way you have a more normalised line/graph.

You can also use the clippedEdges value to determine if one or more joints is outside the view.