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README.md

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 UniMTS is the first unified pre-training procedure for motion time series that generalizes across diverse device latent factors (positions and orientations) and activities. Specifically, we employ a contrastive learning framework that aligns motion time series with text descriptions enriched by large language models. This helps the model learn the semantics of time series to generalize across activities. Given the absence of large-scale motion time series data, we derive and synthesize time series from existing motion skeleton data with all-joint coverage. Spatio-temporal graph networks are utilized to capture the relationships across joints for generalization across different device locations. We further design rotation-invariant augmentation to make the model agnostic to changes in device mounting orientations. UniMTS shows exceptional generalizability across 18 motion time series classification benchmark datasets, outperforming the best baselines by 340% in the zero-shot setting, 16.3% in the few-shot setting, and 9.2% in the full-shot setting.
 
-🤗 We have released the model weights at Hugging Face: https://huggingface.co/xiyuanz/UniMTS/checkpoint/UniMTS.pth
+🤗 We have released the model weights at Hugging Face: https://huggingface.co/xiyuanz/UniMTS
 
 Github repo: https://github.com/xiyuanzh/UniMTS