  As a new way of human-computer interaction, inertial sensor based in-air
handwriting can provide a natural and unconstrained interaction to express more
complex and richer information in 3D space. However, most of the existing
in-air handwriting work is mainly focused on handwritten character recognition,
which makes these work suffer from poor readability of inertial signal and lack
of labeled samples. To address these two problems, we use unsupervised domain
adaptation method to reconstruct the trajectory of inertial signal and generate
inertial samples using online handwritten trajectories. In this paper, we
propose an AirWriting Translater model to learn the bi-directional translation
between trajectory domain and inertial domain in the absence of paired inertial
and trajectory samples. Through semantic-level adversarial training and latent
classification loss, the proposed model learns to extract domain-invariant
content between inertial signal and trajectory, while preserving semantic
consistency during the translation across the two domains. We carefully design
the architecture, so that the proposed framework can accept inputs of arbitrary
length and translate between different sampling rates. We also conduct
experiments on two public datasets: 6DMG (in-air handwriting dataset) and CT
(handwritten trajectory dataset), the results on the two datasets demonstrate
that the proposed network successes in both Inertia-to Trajectory and
Trajectory-to-Inertia translation tasks.
