| --- |
| library_name: braindecode |
| license: cc-by-nc-sa-4.0 |
| tags: [emg, semg, hand-pose, pose-estimation, braindecode, emg2pose] |
| pipeline_tag: other |
| --- |
| |
| # vemg2pose — emg2pose Regression baseline |
|
|
| Meta's released `regression_vemg2pose.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/VEMG2Pose-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="lstm"`, `parameterization="hybrid"`. The recurrent decoder emits a position *and* a velocity per joint, taking the position for the first `num_position_steps` (500 samples, 250 ms) and integrating the velocity thereafter. Trained without a ground-truth initial pose, so `forward(x)` starts from zeros. |
|
|
| ## 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) |
|
|
