  We propose a new metric to measure domain divergence and a new domain
adaptation method for time-series classification. The metric belongs to the
class of probability distributions-based metrics, is transductive, and does not
assume the presence of source data samples. The 2-stage method utilizes an
improved autoregressive, RNN-based architecture with deep/non-linear
transformation. We assess our metric and the performance of our model in the
context of sEMG/EMG-based gesture recognition under inter-session and
inter-subject domain shifts.
