OmniIMU-Bench: Benchmarking Wrist IMU Representations Across Kinematic, Semantic and Clinical Tasks

Wrist-worn inertial measurement units (IMUs) provide a common sensing modality for tasks ranging from activity and gesture recognition to pose estimation, inertial odometry and clinical motor assessment. Existing wearable benchmarks evaluate performance across multiple tasks, but leave open how supervision for one task af- fects transfer to others and whether these tasks benefit from a shared representation. We present OmniIMU-Bench, a benchmark of ten tasks from eight datasets for studying IMU multi-task performance and cross-task transfer. We compare six self- supervised methods pretrained on unlabelled recorded and simulated wrist motion, evaluating classification, pose and velocity estimation, and IMU–language retrieval. We find that masked autoencoding achieves the best or joint-best frozen-probe performance on nine of ten tasks, and fine-tuning improves on training from scratch on nine of ten tasks, including full-body pose, odometry and retrieval. Across trans- fer and joint-training experiments, supervision that benefits one target can reduce performance on others, with gains in kinematic estimation accompanied by losses in language retrieval and joint-training benefits differing between paired tasks. These findings distinguish broad downstream usefulness from complementary task supervision. Code, splits, and a leaderboard will be released.