Inertial Measurement Unit (IMU)

AnyMo: Geometry-Aware Setup-Agnostic Framework for Wearable IMU Human Motion Understanding

AnyMo is a geometry-aware framework that addresses the setup-dependence problem in wearable IMU-based human motion modeling by using physics-grounded simulation over dense body-surface placements to generate synthetic training signals. It pre-trains a graph encoder from synthetic placement views and masked partial observations, then tokenizes multi-position IMU data into full-body motion tokens aligned with an LLM for motion-language understanding. Evaluated across zero-shot activity recognition (14 unseen datasets), cross-modal retrieval, and motion captioning, AnyMo improves average Accuracy/F1 by ~11.7%/11.6%, zero-shot retrieval MRR by 15.9–28.6%, and captioning BERT-F1 by 18.8%. The work positions itself as a generalist model for wearable motion understanding transferable across devices and sensing configurations.