Liornis/forge-fog

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.

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. Data: Liornis/fog-dataset.

License: MIT.