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library_name: braindecode
license: cc-by-nc-sa-4.0
tags: [emg, semg, hand-pose, pose-estimation, braindecode, emg2pose]
pipeline_tag: other
---
# emg2pose — emg2pose Regression baseline
Meta's released `regression_emg2pose.ckpt` from the
[emg2pose benchmark](https://github.com/facebookresearch/emg2pose), rehosted with
[braindecode](https://braindecode.org)'s parameter names:
```python
from braindecode.models import VEMG2Pose
model = VEMG2Pose.from_pretrained("braindecode/EMG2Pose-emg2pose")
```
16-channel sEMG at 2 kHz in, 20 joint angles per sample out. The encoder uses **valid**
convolutions and consumes a left context of **1790** samples, so windows must be longer
than that; the paper trains on 11,790 (10,000 + 1790).
## Configuration
`decoder="mlp"`, `parameterization="position"`. The stateless MLP decoder reads the pose out directly at each step, still conditioned on the previous pose. Trained without a ground-truth initial pose.
## Provenance
Not retrained — the authors' checkpoint with parameter names rewritten. Loaded into
`braindecode.models.VEMG2Pose` and run against the reference `emg2pose.pose_modules`
implementation on the same input, the outputs are **bit-identical** (max absolute
difference 0.0), with all 68 tensors mapped.
## License
Weights are Meta's, under **CC BY-NC-SA 4.0** (non-commercial, share-alike), carried
over unchanged. The UmeTrack hand model behind the labels is CC BY-NC 4.0.
- Salter et al. (2024), *emg2pose*, NeurIPS Datasets and Benchmarks.
[arXiv:2412.02725](https://arxiv.org/abs/2412.02725)
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