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silas-therapy/
small-functional-movement-screening

Name: small-functional-movement-screening
Availability: InStock

apache-2.0

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FormScout

FMS (Functional Movement Screen) scoring pipeline — a screening aid that scores movement videos 0–3 per test with a written rationale and annotated overlay.

⚠️ Screening aid — not a diagnosis. Pain or clearing tests require a clinician.

Running locally

1. Clone and install

bash

git clone https://huggingface.co/silas-therapy/small-functional-movement-screening
cd small-functional-movement-screening
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

2. Start the VLM judge (optional but recommended)

The judge uses Qwen3-VL-8B-Instruct via llama.cpp. Without it the app falls back to the deterministic rubric score — fully functional, no GPU needed.

bash

# Install llama.cpp once
brew install llama.cpp

# Download the model (one-time, ~6 GB)
python3 -c "
from huggingface_hub import hf_hub_download
for f in ['Qwen3VL-8B-Instruct-Q4_K_M.gguf', 'mmproj-Qwen3VL-8B-Instruct-F16.gguf']:
    hf_hub_download('Qwen/Qwen3-VL-8B-Instruct-GGUF', f, local_dir='checkpoints/qwen3-vl')
"

# Start the server (keep this terminal open)
./scripts/serve_judge.sh

To use a fine-tuned GGUF instead of the default:

FORMSCOUT_JUDGE_GGUF=/path/to/finetuned.gguf ./scripts/serve_judge.sh

3. Launch the Gradio app

bash

python3 app.py
# → http://127.0.0.1:7860

Upload a video, select the FMS test from the dropdown, and click Analyze.

4. Headless pipeline (no Gradio)

python3 -m formscout.run sample.mp4

5. Tests

pytest tests/ -v

6. Upload to Hugging Face

bash

# Pushes source to both model repo and Space, opens a PR on each
./scripts/hf_upload.sh

# Or with a custom commit message
./scripts/hf_upload.sh "feat: my change"

Architecture

Typed specialist agents orchestrated by a deterministic Director:

Ingest → Pose2D → [Body3D optional] → Biomechanics → Rubric Score → [Judge] → Report

Agent	Model	Status
Pose2D	YOLO26l-Pose (0.026B) + MediaPipe fallback	✅
Body3D	SAM 3D Body DINOv3 (0.84B)	gated, off by default
Judge + Classifier	Qwen3-VL-8B-Instruct via llama.cpp (8B)	✅
Scoring Head	ST-GCN (0.03B)	Phase 3
Retrieval	Qwen3-VL-Embedding-8B (8B)	Phase 3

See CLAUDE.md for full architecture and invariants.

Feature flags (`formscout/config.py`)

Flag	Default	Meaning
`ENABLE_JUDGE`	`True`	VLM judge via llama-server; rubric fallback when server is down
`ENABLE_3D`	`False`	SAM 3D Body — off until integrated
`ENABLE_STGCN`	`False`	Phase 3
`ENABLE_RAG`	`False`	Phase 3

Model budget

~18B params total (under 32B cap). See MODEL_BUDGET.md.

License

Apache-2.0. Built for the Build Small Hackathon (Backyard AI track).

silas-therapy/small-functional-movement-screening

FormScout

Running locally

1. Clone and install

2. Start the VLM judge (optional but recommended)

3. Launch the Gradio app

4. Headless pipeline (no Gradio)

5. Tests

6. Upload to Hugging Face

Architecture

Feature flags (formscout/config.py)

Model budget

License

silas-therapy/
small-functional-movement-screening

Feature flags (`formscout/config.py`)