| --- |
| library_name: braindecode |
| license: cc-by-nc-sa-4.0 |
| tags: |
| - emg |
| - semg |
| - hand-pose |
| - pose-estimation |
| - braindecode |
| - emg2pose |
| pipeline_tag: other |
| --- |
| |
| # NeuroPose — emg2pose regression baseline |
|
|
|  |
|
|
| *Figure 6 of Liu et al. (2021), CC BY.* |
|
|
| Meta's released `regression_neuropose.ckpt` from the |
| [emg2pose benchmark](https://github.com/facebookresearch/emg2pose), rehosted with |
| [braindecode](https://braindecode.org)'s own parameter names so it loads directly: |
|
|
| ```python |
| from braindecode.models import NeuroPose |
| |
| model = NeuroPose.from_pretrained("braindecode/NeuroPose-emg2pose") |
| ``` |
|
|
| 16-channel sEMG at 2 kHz in, 20 joint angles per sample out — `(batch, n_chans, n_times)` |
| to `(batch, n_times, n_outputs)`. |
|
|
| ## Provenance |
|
|
| These are **not** retrained weights. They are the authors' released checkpoint with only |
| the parameter names rewritten: upstream stores every block in a single `nn.Sequential`, |
| while braindecode splits them across `encoder` / `resnet` / `decoder`. Loading the |
| original `.ckpt` into `braindecode.models.NeuroPose` and running both implementations on |
| the same input gives bit-identical outputs (max absolute difference 0.0, 6,354,903 |
| parameters in both). |
|
|
| The Hub checkpoint uses Braindecode-native parameter names; upstream naming is handled |
| once during publication rather than by the runtime model. |
|
|
| ## Architecture |
|
|
| Liu et al.'s encoder / ResNet / decoder, in the configuration emg2pose used for its |
| published baseline: encoder widths 32/128/256 pooling (10,2) (8,2) (4,4) over |
| (time x electrodes), five residual blocks of three conv groups, decoder 128/32/1 |
| upsampling (10,4) (8,4) (4,2). |
|
|
| ## License and attribution |
|
|
| Weights are Meta's, released under **CC BY-NC-SA 4.0** — non-commercial, share-alike. |
| That license carries over here unchanged. The UmeTrack hand model used for the forward |
| kinematics behind the labels is CC BY-NC 4.0. |
|
|
| - Salter et al. (2024), *emg2pose: A Large and Diverse Benchmark for Surface |
| Electromyographic Hand Pose Estimation*, NeurIPS Datasets and Benchmarks. |
| [arXiv:2412.02725](https://arxiv.org/abs/2412.02725) |
| - Liu, Zhang, Gowda (2021), *NeuroPose: 3D Hand Pose Tracking using EMG Wearables*, |
| The Web Conference 2021. [doi:10.1145/3442381.3449890](https://doi.org/10.1145/3442381.3449890) |
|
|
