# FormScout — Starter Kit & Resource Pack

Companion to `FormScout-FMS-Spec.md` and `FormScout-Build-Prompt.md`. Every link below was checked. Read §1 first — some items are time-sensitive and block the build if you leave them late.

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## 1. Do this NOW (before the hack window — some take hours to clear)

- [ ] **Request access to the gated Meta checkpoints today.** Both are gated on Hugging Face and approval isn't instant:
  - SAM 3 / SAM 3.1 — request on the SAM 3 repos (you need the latest code for the 3.1 checkpoints).
  - SAM 3D Body — `facebook/sam-3d-body-dinov3` and `facebook/sam-3d-body-vith` both require an access request, then an authenticated download. **Note:** data/checkpoints are blocked in sanctioned jurisdictions — shouldn't affect SK, but verify.
- [ ] **Put your HF token in the Space secrets** so the Space can pull the gated weights at build time.
- [ ] **Check licenses before you commit to a model** (this affects whether you can even submit):
  - Qwen3-VL-8B / Qwen3-VL-Embedding-8B / Qwen3.6 → **Apache-2.0** (clean).
  - SAM 3 / SAM 3.1 / SAM 3D Body → **SAM License** (not Apache; read the terms — there are use restrictions).
  - Ultralytics YOLO26 → historically **AGPL-3.0** (open-sourcing obligations; commercial license exists). Verify on the model/repo and make sure an AGPL dependency is OK for your submission. If it's a problem, RTMPose/ViTPose are alternatives.
  - pyskl / MMAction2 → Apache-2.0.
  - KIMORE / UI-PRMD → academic/research terms; check before redistributing anything derived.
- [ ] **Confirm the param-counting rule in the Discord AMA.** Specifically: (a) is it summed across the pipeline or per-model? (b) do **frozen** base models count? (c) does a LoRA adapter's base count? Your ~18B config is safe under the strict reading either way, but get it on record.

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## 2. Literature package

### 2.1 The framing that wins — "evaluate like an FMS reliability study"

The single most credible move in your writeup: evaluate FormScout the way the clinical literature evaluates human FMS raters. Treat the model as a *second rater* and report **weighted Cohen's κ** and **ICC** against the physio, the exact metrics the reliability papers use. That instantly makes your results legible to any sports-medicine reader and is far more honest than a vanity accuracy number.

| Resource | What it gives you | Link |
|---|---|---|
| Physiopedia — FMS | Clean overview of the 7 tests + 0–21 scoring | https://www.physio-pedia.com/Functional_Movement_Screen_(FMS) |
| FMS reliability study (JOSPT 2012) | The ICC/κ numbers and method you'll mirror in your eval | https://www.jospt.org/doi/10.2519/jospt.2012.3838 |
| FMS in elite youth soccer (PMC) | Per-test scores, asymmetries, clearing-test order | https://pmc.ncbi.nlm.nih.gov/articles/PMC5675373/ |
| Clinician's guide to FMS scoring | Per-test 3/2/1 criteria in plain language (rubric source) | https://meloqdevices.com/blogs/meloq-updates/functional-movement-screening |

> **Honesty anchor for the blog post:** the popular "≤14 → injury risk" cutoff has weak/mixed predictive validity. Sell standardization, asymmetry detection, and a repeatable baseline — not prediction.

### 2.2 Action Quality Assessment — surveys & living lists

| Resource | Why | Link |
|---|---|---|
| *A Decade of AQA* (survey, 2025, 200+ papers, PRISMA) | The map of the whole field; start here | https://arxiv.org/abs/2502.02817 · code: https://github.com/HaoYin116/Survey_of_AQA |
| *Comprehensive Survey of AQA: Method & Benchmark* (2024) | Taxonomy by modality (video / **skeleton** / multimodal) + unified benchmark | https://arxiv.org/abs/2412.11149 · page: https://zhoukanglei.github.io/AQA-Survey |
| Awesome-AQA (ZhouKanglei) | Curated, **has a Medical-Care/rehab section** — your closest analogues | https://github.com/ZhouKanglei/Awesome-AQA |
| Awesome-AQA (Lyman-Smoker) | Second list; catches papers the other misses (FLEX, ExAct, etc.) | https://github.com/Lyman-Smoker/Awesome-AQA |

### 2.3 Skeleton-based scoring — the methods your head will borrow from

| Paper | Relevance to FormScout | Link |
|---|---|---|
| ST-GCN (original) | The graph-over-skeleton + temporal-conv backbone | https://github.com/open-mmlab/mmaction2/blob/main/configs/skeleton/stgcn/README.md |
| AQA via Hierarchical **Pose-guided** Multi-Stage Contrastive Regression (TIP 2025) | Pose-guided + contrastive regression with few labels — close to your setup | https://arxiv.org/abs/2501.03674 |
| Attention-guided Movement **Quality** Assessment + skeletal augmentation (UI-PRMD/KIMORE) | Transformer MQA on clinician-scored rehab data; **augmentation recipe for tiny sets** | https://arxiv.org/pdf/2204.07840 |
| SSL-Rehab: self-supervised 3D skeleton + **LoRA** fine-tune (KIMORE/UI-PRMD) | Pretrain→LoRA recipe for small clinical datasets (uses your LoRA muscle) | https://www.sciencedirect.com/science/article/abs/pii/S1077314224003564 |
| Skeleton-based AQA w/ anomaly-aware DTW (Sensors 2025) | DTW alignment + anomaly scoring; cheap, label-light baseline | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12693942/ |

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## 3. Models & tooling (verified)

| Component | Repo / card | Params | License | Gated? |
|---|---|---:|---|---|
| YOLO26-Pose | https://docs.ultralytics.com/tasks/pose | <0.1B | AGPL-3.0* | no |
| SAM 3.1 | https://github.com/facebookresearch/sam3 | ~0.85B | SAM License | **yes** |
| SAM 3D Body | https://github.com/facebookresearch/sam-3d-body · https://huggingface.co/facebook/sam-3d-body-dinov3 | sub-1B† | SAM License | **yes** |
| ST-GCN++ / PoseConv3D | https://github.com/kennymckormick/pyskl | ~0.01–0.05B | Apache-2.0 | no |
| Qwen3-VL-8B-Instruct | https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct | 8B | Apache-2.0 | no |
| Qwen3-VL-Embedding-8B | https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B (GGUF: dam2452/...-GGUF) | 8B | Apache-2.0 | no |
| Qwen3.6-27B (alt brain) | https://huggingface.co/unsloth/Qwen3.6-27B-GGUF | 27B | Apache-2.0 | no |

\* verify the current YOLO26 license. † two variants (`dinov3`, `vith`); confirm exact count on the card — budget impact is small either way. SAM 3 itself is 848M.

**Useful extras:** SAM 3D Body uses a Momentum Human Rig (MHR) that separates skeleton from soft-tissue shape — convenient for clean joint-angle extraction. The repo ships a notebook combining SAM 3D Body + SAM 3D Objects in one frame of reference. SAM 3D Body demo: https://www.aidemos.meta.com/segment-anything/editor/convert-body-to-3d

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## 4. Datasets for transfer / pretraining

You have a couple of labeled clips. Pretrain on clinician-scored movement-quality data first, then few-shot fine-tune. These are the most transferable to FMS (ranked by relevance):

| Dataset | Why it's the closest analogue | Link |
|---|---|---|
| **KIMORE** | Clinician **scores** of low-back-pain rehab exercises (trunk control, multi-plane) — same "score movement quality" task as FMS; partially overlaps Deep Squat / Rotary Stability / TSPU mechanics | https://www.researchgate.net/publication/333791841 (search "KIMORE dataset") |
| **UI-PRMD** | 10 rehab movements, correct vs. incorrect executions; standard MQA benchmark, pairs with KIMORE | search "UI-PRMD University of Idaho Physical Rehabilitation Movements" |
| **Fitness-AQA** | Real gym **squat/deadlift form errors** — directly relevant to Deep Squat compensations | https://github.com/ParitoshParmar/MTL-AQA (links Fitness-AQA) |
| **FLEX** | Large multi-modal fitness AQA dataset | via Lyman-Smoker/Awesome-AQA |
| **MTL-AQA / AQA-7 / FineFS** | General sports AQA for backbone pretraining (diving, skating) | https://github.com/ParitoshParmar/MTL-AQA |

**FMS-specific public video data is scarce** — don't expect a drop-in set. Your physio's clips are the gold; everything above is for pretraining the temporal backbone so it learns movement structure before it ever sees an FMS label.

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## 5. Build & deploy tooling

| Need | Link |
|---|---|
| Gradio docs (v6) | https://www.gradio.app/docs |
| `gradio.Server` — custom frontend + Gradio backend (Off-Brand badge) | https://www.gradio.app/guides/server-mode · blog: https://huggingface.co/blog/introducing-gradio-server |
| Gradio AI coding-assistant skill | `gradio skills add --claude` (PyPI: https://pypi.org/project/gradio/) |
| Gradio changelog (confirm `gr.Walkthrough`, `gr.Navbar`, `gr.Video.playback_position`) | https://www.gradio.app/changelog |
| HF Spaces ZeroGPU (`@spaces.GPU`) | https://huggingface.co/docs/hub/spaces-zerogpu |
| llama.cpp | https://github.com/ggml-org/llama.cpp |
| pyskl (ST-GCN++/PoseConv3D, custom-video tutorial incl. diving48) | https://github.com/kennymckormick/pyskl |
| MMAction2 (broader video understanding) | https://github.com/open-mmlab/mmaction2 |
| Hackathon's own trailheads (ML Intern, Gradio guides) | https://github.com/huggingface/ml-intern |

> **Hackathon-specific gotcha already seen in the org:** another team's Space hit `libcudart.so.12` errors and had to swap llama.cpp for transformers + `spaces.GPU`. Plan for it — isolate the llama.cpp build (CPU-only or pinned-CUDA) and keep a transformers fallback. For the scoring head, a small hand-rolled ST-GCN may deploy more cleanly on a Space than the full MMAction2/pyskl stack — prototype with pyskl, ship lean.

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## 6. Two artifacts you probably haven't made yet

### 6.1 Data & capture protocol (highest-leverage non-code work)

With a tiny dataset, controlling *how* clips are captured beats any model tweak. Give the physio a one-pager:

- **Camera:** one fixed position, tripod, ~3 m back, lens at hip height, landscape, 1080p/30fps+. Same setup every session — this is what makes 3D consistent and the longitudinal baseline meaningful.
- **Framing:** whole body in frame for the whole rep, including the dowel. Plain-ish background, even lighting, no backlight.
- **One athlete in frame** at scoring time (or note who to track). For bilateral tests, capture **both sides** and label each.
- **Label schema (CSV):** `clip_id, athlete_id, date, test_name, side(L/R/NA), score(0–3), pain(bool), compensation_notes(free text), camera_view, consent_on_file(bool)`.
- **One rep per clip** to start (simplest). If sessions are continuous, you'll need temporal segmentation first — flag it to the build agent at Phase 1.

### 6.2 Evaluation plan

Define "good" before you train, given so few labels:

- **Primary:** Spearman ρ between predicted and physio scores (the AQA-standard metric), plus **exact-match** and **±1 accuracy** per test.
- **Clinical credibility:** **weighted Cohen's κ** and **ICC** of model-vs-physio, reported alongside the human inter-rater numbers from the JOSPT study — i.e. "how does FormScout compare to a second human rater?"
- **Asymmetry:** detection rate of L/R asymmetries the physio flagged (this is one of the FMS's most defensible outputs).
- **Validation:** leave-one-clip-out CV (you can't afford a held-out test split). Keep ≥1 clip the judge never sees for the demo.
- **Calibration:** report when the system says "low confidence / physio review" and show it's right to do so. A well-calibrated, humble tool reads as more trustworthy than a confident one.

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## 7. Ethics, consent & data handling (EU / Slovakia)

You're filming identifiable athletes, possibly **minors** on a youth team. This is biometric personal data under GDPR — treat it as first-class, and say so in your submission (judges and physios both reward it):

- **Consent:** written consent from each athlete (and a parent/guardian for anyone under 18) before any footage is used. No consent → not in the dataset, not in the demo.
- **Data minimization & retention:** keep only what you need; don't persist raw clips on the Space beyond what's approved; document a retention/deletion policy. Prefer storing derived skeletons over raw video where possible.
- **Demo footage:** use a consenting adult (you, a teammate) for the public demo video rather than a minor athlete, even if you trained on team data privately.
- **Framing:** screening aid, not a medical device; pain/clearing tests always defer to the clinician; human-in-the-loop by design.

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## 8. The transfer-learning recipe (ties it together)

1. **Backbone pretrain** — ST-GCN++ on a general skeleton-action set (NTU/Kinetics skeletons via pyskl) so it learns motion structure.
2. **Domain adapt** — continue on **KIMORE + UI-PRMD** (clinician-scored movement quality) so it learns *quality*, not just *what action*.
3. **Few-shot fine-tune** — **LoRA** on the physio's FMS clips with heavy augmentation (temporal jitter, **L↔R mirror** to double bilateral data, 3D camera-angle perturbation, joint noise). The SSL-Rehab paper (§2.3) is your blueprint and it's exactly your LoRA wheelhouse.
4. **Don't over-train the head** — let deterministic biomechanics carry the demo; the learned head and RAG are the refinement and the badges, not the foundation.

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## 9. Demo & submission storyboard (the "make it sing" 30%)

The submission needs a demo video + social post; "Show, Don't Tell" is a literal rule. A tight 60–90s cut:

1. **0–10s** — the problem: physio eyeballing a squat, scribbling a score. "Same player, two raters, two scores."
2. **10–35s** — upload the clip to FormScout → skeleton overlay → 0–3 with the *deciding angle drawn on the frame* (`playback_position` jump). The "aha" shot.
3. **35–55s** — the scorecard: composite 0–21, the L/R asymmetry strip, a "low confidence — physio review" flag on a borderline case (honesty sells).
4. **55–75s** — the physio reacting / using it on a real player (the Backyard AI "they actually used it" proof).
5. **End card** — "Runs on a laptop. ~18B params. Screening aid, not a diagnosis." Link the Space, the published head, the agent trace, the blog.

Social post: lead with the overlay GIF + the asymmetry-detection angle; tag Gradio/HF; one line of honest framing.

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*Built to give FormScout the best shot. The two things most teams underinvest in — the capture protocol (§6.1) and the honest, clinical-style evaluation (§6.2, §2.1) — are exactly where this project can out-class flashier entries. Good luck. 🏀*