MyoMechanix: Biomechanically-Grounded Compositional Skilled Activity Understanding and Coaching
Read the original on arXiv Machine Learning →MyoMechanix is a multimodal dataset and framework for action quality assessment that incorporates muscle activity and other physiological signals alongside visual data. It contains over 7,500 samples of 20 weight‑loaded actions from 38 subjects, with synchronized RGB video, 3D pose, sEMG, and additional signals. The accompanying Fitness Knowledge Graph structures expert annotations into relationships among actions, phases, key steps, errors, and corrective feedback, enabling compositional scoring and interpretable assessment through the CUBIST engine. The project also introduces MyoMechanix‑AQA, MyoMechanix‑VideoQA, and a novel MyoMechanix‑Video2EMG task, demonstrating that multimodal sensing and structured representations improve performance, interpretability, and error attribution.
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