Hi Ajay, @AjayTalati
looking at this some more, I wonder whether the guarantees are unusually strong in practice -

if … have enough capacity and is trained to reach optimum

seems like a pretty strong assumption (it would seem the WGAN equivalent would be not far away from “the discriminator has learnt a test function reproducing the real Wasserstein distance” and “the generator has been trained so that the Wasserstein distance is 0” - at which point you automatically have success).
The code for the article accompanying the ichi2014 dataset you linked seems to be downloadable from this uni-siegen page 3. Maybe it is useful as a baseline. I’m not sure that I quite understand the spectrogram conversion from accelerometer data - this seems quite different from RF data.
Best regards
Thomas