IMUDINO: Self-distilled Foundation Model for Inertial Sensing

Anonymous (ICLR 2027 double-blind submission)

Summary

Adapts DINO self-distillation to IMU via motion transformations that simulate variations in motion pattern and sensor setup; frequency-aware positional encoding lets one plain transformer ingest any window length / sampling rate. Positioned as the first inertial FM competitive with SOTA supervised methods across HAR, health, motion capture and robot odometry; global and dense features transfer across users, setups, tasks and embodiments. "Code and checkpoints will be released" (abstract).