Transferring Physical Motion Between Domains for Neural Inertial Tracking
This work addresses the challenge of domain shifts in inertial tracking for mobile agents, which is incremental as it builds on existing domain adaptation methods for sensory sequences.
The paper tackles the problem of domain adaptation for neural inertial tracking by proposing a framework that transfers physical motion knowledge from a labeled source domain to unlabeled target domains, enabling accurate trajectory reconstruction from raw IMU measurements without paired data.
Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. However, they are affected greatly by changes in sensor placement/orientation or motion dynamics, and it is infeasible to collect labelled data from every domain. To overcome the challenges of domain adaptation on long sensory sequences, we propose a novel framework that extracts domain-invariant features of raw sequences from arbitrary domains, and transforms to new domains without any paired data. Through the experiments, we demonstrate that it is able to efficiently and effectively convert the raw sequence from a new unlabelled target domain into an accurate inertial trajectory, benefiting from the physical motion knowledge transferred from the labelled source domain. We also conduct real-world experiments to show our framework can reconstruct physically meaningful trajectories from raw IMU measurements obtained with a standard mobile phone in various attachments.