*7.2.3 Time-series data processing*

Domain adaptation can also enhance the performance of processing many other time-series datasets such as healthcare time-series datasets [51], in which the authors present a variational recurrent adversarial method for domain adaptation. The main idea is to learn domain-invariant temporal latent representations of multivariate time-series data. Another real-world task that involves time-series data is to build diver assistant systems. In [52], an auxiliary domain classifier is also adopted to enhance the performance of recurrent neural networks for driving maneuvers anticipation. And the core idea in this paper is also to learn sharing features from different datasets by the domain classifier. An interesting work related to inertial information processing is introduced in [53], in which a novel framework called MotionTransformer is proposed for extracting domain-invariant features of raw sequences.
