A Novel Unsupervised domain adaptation method for inertia-Trajectory translation of in-air handwriting

Highlights：

• We propose a novel Air Writing Translator for inertia trajectory transla tion of in air handwriti ng. To our best knowledge, this is the f i rst attempt to address unsupervised inertia trajectory translation as a domain adap tation task in IAHR.

• We propose a triple loss function to train the network: adversarial loss, classification loss and reconstruction loss. The combination of adversarial loss and latent classification loss in latent space aims to guide the semantic consistency of feature level.

• Coupled with reconstruction loss, the triple loss ensures that the model can learn the style information of the target

• We propose a unique design of Conv and GRU (gated recurrent unit) to handle input time series of arbitrary length.

• In addition, this design makes it possible to translate between inertial signals and trajectory signals of different sampling rat

• Air-Writing Translator is trained in an unsupervised manner, with no need for pair-wise data.

• On one hand, this training method ensures that our model can learn to make latent representations of the two domains closer from the perspective of probability distribution, rather than simply matching samples.

• On the other hand, it solves the problem of lack of paired data in practical applications.