Text2IMU: Advancing Human Activity Recognition by Text-Driven IMU Data Synthesis

Lars Ole Haeusler, Lena Uhlenberg, Oliver Amft (University of Freiburg / Hahn-Schickard)

Summary

Freiburg (Amft lab) introduces Text2IMU: text prompts → motion-synthesis model → multiple body surface morphologies → virtual wearable accelerometer + gyroscope at configurable placements, then train HAR solely on synthetic data (no measured target-domain IMU required). OpenReview abstract reports average HAR accuracy 79.2% on correctly synthesised activities across three public benchmarks — roughly double IMUGPT-class baselines in their comparison. Precursor to the OJEMB 2026 diversity study (text2imu-diversity-sources-ojemb-2026, best synthetic-only bal.