| --- |
| license: mit |
| tags: [freezing-of-gait, parkinsons, accelerometer, self-supervised, mae, time-series, time-series-classification] |
| pipeline_tag: other |
| --- |
| |
| # FORGE — FOG Representation via Generative Encoding |
|
|
| Self-supervised spectral-temporal encoders for **Freezing of Gait (FOG)** detection from a |
| single lower-back accelerometer. Pretrained by masked autoencoding on ~21M unlabeled home |
| windows; evaluated zero-shot on the external FogAtHome cohort. |
|
|
| **Headline:** the MC probe reaches clinical-grade agreement with expert video annotation — |
| **ICC(%TF) = 0.909 [0.87, 0.94]**, zero-shot, one IMU. |
|
|
| ## Released weights |
|
|
| ### Pretrained FORGE encoders (the backbones) |
| | Context | Window (frames) | File | Params | |
| |---|---|---|---| |
| | LC | 1000 | `encoders/lc.ckpt` | 14,147,072 | |
| | MC | 500 | `encoders/mc.ckpt` | 14,147,072 | |
| | SC | 200 | `encoders/sc.ckpt` | 12,918,272 | |
|
|
| ### Downstream classification checkpoints (128-patient DeFOG, 3-fold CV) |
| | File | Context | Phase | Fold | |
| |---|---|---|---| |
| | `classification/lc_probe_fold0.ckpt` | lc | probe | 0 | |
| | `classification/lc_probe_fold1.ckpt` | lc | probe | 1 | |
| | `classification/lc_probe_fold2.ckpt` | lc | probe | 2 | |
| | `classification/mc_probe_fold0.ckpt` | mc | probe | 0 | |
| | `classification/mc_probe_fold1.ckpt` | mc | probe | 1 | |
| | `classification/mc_probe_fold2.ckpt` | mc | probe | 2 | |
| | `classification/sc_probe_fold0.ckpt` | sc | probe | 0 | |
| | `classification/sc_probe_fold1.ckpt` | sc | probe | 1 | |
| | `classification/sc_probe_fold2.ckpt` | sc | probe | 2 | |
| | `classification/lc_finetune_fold0.ckpt` | lc | finetune | 0 | |
| | `classification/lc_finetune_fold1.ckpt` | lc | finetune | 1 | |
| | `classification/lc_finetune_fold2.ckpt` | lc | finetune | 2 | |
| | `classification/mc_finetune_fold0.ckpt` | mc | finetune | 0 | |
| | `classification/mc_finetune_fold1.ckpt` | mc | finetune | 1 | |
| | `classification/mc_finetune_fold2.ckpt` | mc | finetune | 2 | |
| | `classification/sc_finetune_fold0.ckpt` | sc | finetune | 0 | |
| | `classification/sc_finetune_fold1.ckpt` | sc | finetune | 1 | |
| | `classification/sc_finetune_fold2.ckpt` | sc | finetune | 2 | |
| | `classification/lc_supervised_fold0.ckpt` | lc | supervised | 0 | |
| | `classification/lc_supervised_fold1.ckpt` | lc | supervised | 1 | |
| | `classification/lc_supervised_fold2.ckpt` | lc | supervised | 2 | |
| | `classification/mc_supervised_fold0.ckpt` | mc | supervised | 0 | |
| | `classification/mc_supervised_fold1.ckpt` | mc | supervised | 1 | |
| | `classification/mc_supervised_fold2.ckpt` | mc | supervised | 2 | |
| | `classification/sc_supervised_fold0.ckpt` | sc | supervised | 0 | |
| | `classification/sc_supervised_fold1.ckpt` | sc | supervised | 1 | |
| | `classification/sc_supervised_fold2.ckpt` | sc | supervised | 2 | |
|
|
| ## Usage |
| Checkpoints are slimmed PyTorch Lightning checkpoints (weights + config; optimizer state |
| stripped). Each keeps the `state_dict` and the `hyper_parameters["config"]` Pydantic config |
| used to rebuild the model — the same fields the evaluation pipeline reads. |
|
|
| ```python |
| import torch |
| ckpt = torch.load("encoders/mc.ckpt", map_location="cpu", weights_only=False) |
| state_dict = ckpt["state_dict"] # encoder weights |
| config = ckpt["hyper_parameters"]["config"] # Config object to rebuild the model |
| meta = ckpt.get("forge_meta") # name / context / phase / fold |
| ``` |
|
|
| Reproduce every paper number with the companion repo's `reproduce-evaluations` skill (see |
| `manifest.yaml`, shipped in this repo). |
| Code: [github.com/Lior-Nis/forge](https://github.com/Lior-Nis/forge). |
| Data: [Liornis/fog-dataset](https://huggingface.co/datasets/Liornis/fog-dataset). |
|
|
| License: **MIT**. |
|
|
