| # Pozify Implementation Plan |
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| This plan breaks Pozify into implementation workstreams that can be developed independently while preserving the current JSON data contracts. |
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| ## Phase 1: Foundation And Contracts |
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| Goal: make the mocked pipeline production-shaped before replacing mock logic. |
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| Deliverables: |
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| - Stable JSON contracts in `src/pozify/contracts.py`. |
| - Artifact persistence under `runs/<run_id>/`. |
| - Contract validation and unit tests. |
| - Clear step boundaries for replacing mock implementations. |
| - Seed knowledge card structure. |
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| ## Phase 2: Video Intake And Quality Gate |
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| Goal: accept user videos, extract reliable metadata, and reject videos that cannot be analyzed safely. |
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| Deliverables: |
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| - OpenCV-backed video metadata extraction. |
| - Duration, FPS, brightness, blur, and basic frame-count checks. |
| - Pose-quality-aware analysis gate. |
| - User-facing quality warnings. |
| - Capture guidance for failed videos. |
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| ## Phase 3: Pose Extraction And Cleaning |
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| Goal: convert videos into a clean 33-point pose sequence. |
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| Deliverables: |
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| - MediaPipe Pose Landmarker integration. |
| - Per-frame landmark persistence. |
| - Pose quality scoring. |
| - Smoothing and interpolation. |
| - Coordinate normalization. |
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| ## Phase 4: Exercise Router |
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| Goal: classify the video as squat, push-up, shoulder press, or unknown. |
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| Deliverables: |
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| - Pose feature extraction for 30-frame windows. |
| - Baseline classifier. |
| - Fine-tuned small temporal classifier. |
| - Confidence-weighted aggregation. |
| - Manual fallback when confidence is low. |
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| ## Phase 5: Rep Segmentation |
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| Goal: split each exercise into clean reps using transparent state machines. |
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| Deliverables: |
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| - Squat rep counter. |
| - Push-up rep counter. |
| - Shoulder press rep counter. |
| - Partial-rep handling. |
| - Rep boundary visualization/debug output. |
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| ## Phase 6: Rep Analysis And Variation Detection |
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| Goal: compute meaningful metrics per rep and separate valid variations from issues. |
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| Deliverables: |
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| - Common rep metrics. |
| - Exercise-specific metrics. |
| - Variation detection rules. |
| - Aggregate trends such as fatigue and decreasing ROM. |
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| ## Phase 7: Frame-Level Issue Markers |
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| Goal: identify exact issue intervals and attach evidence to each issue. |
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| Deliverables: |
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| - Rule-based frame-level issue scores. |
| - Consecutive-frame thresholding. |
| - Issue interval grouping. |
| - Optional temporal issue classifier. |
| - Evidence payloads for each marker. |
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| ## Phase 8: Annotated Video And UI |
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| Goal: make the evidence visible and easy to inspect. |
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| Deliverables: |
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| - Skeleton overlay renderer. |
| - Red/amber highlights during issue intervals. |
| - Rep counter overlay. |
| - Issue thumbnails. |
| - Gradio report layout with summary, metrics, reps, issues, and JSON evidence tabs. |
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| ## Phase 9: Grounded Coach Summary |
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| Goal: generate concise coaching feedback from structured evidence, not model memory. |
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| Deliverables: |
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| - Exercise, variation, issue, goal, and safety knowledge cards. |
| - Deterministic retrieval. |
| - SLM prompt contract. |
| - Summary verifier. |
| - Conservative fallback summary. |
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| ## Phase 10: Dataset, Training, Evaluation, And Demo |
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| Goal: create a defensible small-model story and a reliable hackathon demo. |
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| Deliverables: |
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| - Dataset ingestion scripts. |
| - Custom data collection guide. |
| - Training pipeline for the exercise router. |
| - Evaluation metrics. |
| - Demo videos. |
| - README and deployment instructions. |
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