Air-Writing Translater: A Novel Unsupervised Domain Adaptation Method for Inertia-Trajectory Translation of In-air Handwriting

JOURNAL OF XXX CLASS FILES, VOL. 1, NO. 1, JUNE 2019 1 Air-Writing Translater: A Novel Unsupervised Domain Adaptation Method for Inertia-Trajectory Translation of In-air Handwriting Songbin Xu, Y ang Xue, Xin Zhang, Lianwen Jin 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. T o 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 Air-Writing Translater model to learn the bidirectional 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. I NTRODUCTION I NAIR handwriting refers to a novel way of human-computer interaction (HCI), which freely writes meaningful characters in 3D space and then converts them into user-to-computer commands. Compared with general motion gestures, in-air handwriting is more complicated and provides more abundant expressions. As modern MEMS(Micro-Electro- Mechanical System) inertial sensors become smaller and more energy efficient, they have been universally employed in portable and wearable devices such as smartphones and wristbands. Unlike optical devices, inertial sensors do not suffer from illumination interference and obstruction. Therefore, inertial sensor based in-air handwriting has widely attracted researchers' attention [1]-[4]. Most of the existing work is mainly focused on in-air handwriting recognition (IAHR) [5]-[8]. But in the research of IAHR, there are usually two problems. Firstly, the inertial signal is full of abstractness and lack of readability, because it is a series of temporal sequences representing motion shifting, as illustrated in Fig.1(a).