AnyMo pre-trains a graph encoder on physics-simulated multi-placement IMU data and aligns full-body motion tokens with LLMs to enable zero-shot activity recognition, retrieval, and captioning across unseen datasets and setups.
A systematic study of unsupervised domain adaptation for robust human-activity recognition.Proc
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GenHAR generalizes cross-domain human activity recognition by 9.97% accuracy and 6.4x lower FLOPs via tokenized sensor data, frequency channel correlations, selective masking, and efficient attention, with deployment detecting 2.15 billion activities.
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