Your task: Draft a full, production‑grade design for a web platform called “FlexiPT”, a virtual physical‑therapy service that lets licensed providers remotely assign rehab exercises, while patients perform those exercises in front of their laptops or tablets and receive real‑time feedback on form. 1. Goals & Value Proposition Reduce missed PT sessions and improve adherence to home‑exercise programs. Provide quantitative, objective movement data to clinicians. Lower costs for payers by decreasing in‑person visit counts. Offer patients an engaging, coach‑like experience that boosts motivation. 2. User Roles Admin / Superuser Provider (Licensed PT, PTA, or Athletic Trainer) Patient 3. Core Functional Requirements 3.1 Exercise Library Store or reference clinically vetted exercise videos (MP4, Vimeo, YouTube, or HIPAA‑secure CDN). Metadata for each exercise: Name, category, body region, difficulty, therapeutic goal, sets, reps, hold time, contraindications. Bulk import from CSV and single‑upload via drag‑and‑drop. 3.2 Provider Dashboard Create and manage patient profiles; assign one‑off exercises or templated rehab programs. Calendar view of scheduled sessions and completion status. Real‑time analytics panel: adherence %, average form score, range‑of‑motion trends, pain‑score history. Secure messaging and document upload (progress notes, PDFs). 3.3 Patient Portal Exercise queue: each item shows thumbnail, description, and a “Start Session” button that opens the video link. Embedded webcam capture canvas with pose overlay and repetition counter. Real‑time audio or on‑screen cues: “Lift your elbow higher,” “Keep your knee behind your toes,” etc. After each set, prompt for self‑reported pain/fatigue (0‑10 scale). Progress page: streaks, badges, trend graphs, provider feedback. 3.4 Computer‑Vision & Feedback Pipeline Pose‑estimation microservice (Python + PyTorch or TensorFlow): Ingest live video stream, run MediaPipe BlazePose or custom model, return 2D/3D keypoints. Form‑scoring engine: Rule‑based thresholds plus optional ML model trained on labeled good/bad reps. Outputs: rep count, range‑of‑motion %, posture deviation angle, pass/fail per rep. WebSocket channel for <150 ms latency feedback to the browser. 3.5 Data & Analytics Store raw keypoints, summary metrics, and session logs in encrypted S3 (or GCS) buckets. Daily job aggregates stats into patient_progress table for fast dashboard queries. Predictive model flags dropout risk or abnormal recovery patterns for provider review. 3.6 Notifications & Engagement Email, SMS, or push notifications for upcoming sessions, streak milestones, or provider messages. Gamification: achievements, leaderboards (opt‑in for group rehab cohorts). 3.7 Compliance & Security HIPAA and GDPR alignment: audit logs, role‑based access control, encryption at rest (AES‑256) and in transit (TLS 1.3). MFA for providers; optional SSO with OAuth2 / SAML for enterprise clinic accounts. FDA Class II SaMD readiness checklist (intended use = therapeutic guidance). 4. Non‑Functional Requirements Scalability: target 10 k concurrent video streams with auto‑scaling Kubernetes pods. Browser Support: latest Chrome, Edge, Safari, Firefox; graceful degradation if webcam unavailable. Accessibility: WCAG 2.1 AA compliance, captions on videos, high‑contrast mode, keyboard navigation. 5. Suggested Tech Stack Layer Recommendation Rationale Frontend Next.js 14 + React + TypeScript with Tailwind & shadcn/ui SSR for SEO, component reuse Realtime WebRTC for video + WebSockets for feedback Low‑latency streams Backend API Node.js (NestJS) or Python (FastAPI) Typed, scalable, async‑friendly Database PostgreSQL via Prisma ORM Relational, strong ACID guarantees CV Microservice Python FastAPI + PyTorch GPU‑friendly, decoupled Queue & Jobs RabbitMQ or Kafka Event‑driven architecture Auth Auth0 or Keycloak HIPAA‑ready identity management CI/CD GitHub Actions -> Docker -> Kubernetes Automated testing & deployment Cloud AWS (EKS, RDS, S3, SES, CloudFront) Managed services, HIPAA BAA 6. Data Model (simplified ERD) User (id, role, name, email, hashed_password, provider_id FK) Exercise (id, title, video_url, category, muscle_group, difficulty, metadata_json) Program (id, provider_id, patient_id, name, created_at) ProgramExercise (id, program_id, exercise_id, sets, reps, order_index) SessionLog (id, patient_id, program_exercise_id, timestamp, reps_completed, average_form_score, pain_rating) KeypointBlob (id, session_log_id, s3_uri) 7. API Route Examples (REST) bash Copy Edit POST /api/v1/patients/{id}/programs # create program GET /api/v1/patients/{id}/progress # progress summary POST /api/v1/sessions/{id}/complete # upload form metrics WS /api/v1/stream/{session_id} # live keypoints → feedback 8. UI/UX Deliverables Figma wireframes for: Provider dashboard (list, calendar, analytics). Patient exercise session screen with split video / webcam view. Component spec sheet (buttons, cards, graphs, modals). Color palette and typography tokens. 9. Future‑Roadmap “Nice‑to‑Haves” Mobile apps (React Native) with camera‑based feedback. Wearable integration (Apple HealthKit, Fitbit, Garmin). Voice assistant for hands‑free controls. AI‑generated exercise plans based on diagnosis codes (ICD‑10) and outcome goals. 10. Deliverables You Must Produce 20‑page system design doc (markdown). ERD diagram (draw.io JSON + PNG). Figma link for all screens. Docker‑compose file that spins up: PostgreSQL, backend API, frontend, CV microservice. Seed script that inserts 25 example exercises and 3 demo programs. README with local dev, staging, and production deploy instructions. When you finish, output each artifact inline or as separate files (use triple‑backticks for code, markdown tables for docs, and detailed checklists). If any assumption is unclear, briefly state your assumption and proceed. - Initial Deployment
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