The advent of commodity 3D sensor technol- ogy has, amongst other things, enabled the efficient and effective assessment of human movements. Machine learning approaches do not rely manual definitions of gold standards for each new movement. However, to train models for the automated assessments of a new movement they still need a lot of data that map recorded movements to expert judg- ments. As camera technology changes, this training needs to be repeated if a new camera does not agree with the old one. The present paper presents an inexpensive method to check the agreement of cameras, which, in turn, would allow for a safe reuse of trained models regardless of the cameras. We apply the method to the Kinect, Astra Mini, and Real Sense cameras. The results show that these cameras do not agree and that the models cannot be reused without an unacceptable decay in accuracy. However, the suggested method works independent of movements and cameras and could potentially save effort when integrating new cameras in an existing assessment environment.