1. Executive Summary
Product Name: FormFit AI
Vision: An AI-powered workout companion that uses computer vision and Gemini's multimodal capabilities to provide real-time form corrections, preventing injuries and maximizing workout effectiveness.
Problem:
- 60-80% of gym injuries are caused by poor form
- Personal trainers are expensive ($50-100/session)
- YouTube tutorials can't provide personalized feedback
- People don't know they're doing exercises wrong until they get injured
Solution: A mobile/web app that analyzes workout videos in real-time or post-recording, providing detailed form corrections, explanations of why form matters, and tracking improvement over time.
2. Success Metrics
Primary Metrics
- Accuracy Rate: >85% form assessment accuracy compared to certified trainers
- User Engagement: Users record 3+ exercises per week
- Safety Impact: 70% of users report feeling more confident in their form
Secondary Metrics
- Time to feedback: <5 seconds for video analysis
- User retention: 40% weekly active users after 1 month
- Exercise library coverage: 50+ exercises at launch
3. User Personas
Persona 1: "Beginner Brian"
- Age: 25-35
- Just started working out at home
- Watches YouTube tutorials but unsure if he's doing it right
- Worried about injury, wants to build confidence
- Need: Simple, encouraging feedback with explanations
Persona 2: "Intermediate Isabel"
- Age: 28-40
- Works out regularly, knows basic movements
- Wants to optimize performance and break plateaus
- Used to have a trainer but too expensive now
- Need: Detailed biomechanical feedback, progression tracking
Persona 3: "Rehab Robert"
- Age: 45-60
- Recovering from injury or managing chronic pain
- Doctor recommended specific exercises
- Needs to ensure perfect form to avoid re-injury
- Need: Medical-grade precision, safety warnings
4. Core Features
4.1 Video Analysis Engine (MVP)
User Flow:
- User selects exercise type (squat, pushup, deadlift, etc.)
- User records video (5-30 seconds) or uploads existing video
- AI analyzes movement frame-by-frame
- User receives annotated video with feedback overlay
Technical Implementation:
Input: Video file or camera stream
↓
Gemini 3's Vision API extracts:
- Body pose keypoints (joints, angles)
- Movement trajectory
- Range of motion
- Timing/tempo
↓
Gemini 3's Reasoning analyzes:
- Compares to ideal form database
- Identifies deviations
- Prioritizes corrections by injury risk
↓
Output: Annotated video + text feedback
Key Capabilities:
- Multi-angle support: Front, side, 45-degree views
- Rep counting: Automatic counting with quality scoring per rep
- Frame-by-frame breakdown: Scrub through video to see exact problem points
- Overlay annotations: Visual markers showing joint angles, alignment issues
4.2 Intelligent Feedback System
Feedback Hierarchy:
- Critical (Red): Injury risk issues - "Your lower back is rounding, risking disc herniation"
- Important (Yellow): Effectiveness issues - "Knees caving inward reduces quad activation"
- Optimization (Green): Performance tips - "Going deeper would increase glute engagement"
Explanation Depth:
- Quick Fix: "Keep your chest up"
- Why It Matters: "Rounding your back transfers load from legs to spine"
- Biomechanics: "The lumbar spine is designed for stability, not flexion under load..."
- Visual Comparison: Side-by-side of user's form vs. ideal form
4.3 Exercise Library
Launch Exercises (50+):
Compound Movements:
- Squats (back, front, goblet)
- Deadlifts (conventional, Romanian, sumo)
- Bench press (barbell, dumbbell)
- Overhead press
- Rows (bent-over, pendlay)
Bodyweight:
- Push-ups (standard, wide, diamond)
- Pull-ups/Chin-ups
- Dips
- Lunges
- Planks
Isolation:
- Bicep curls
- Tricep extensions
- Lateral raises
- Leg extensions
- Hamstring curls
Each Exercise Includes:
- Ideal form reference video
- Common mistakes database
- Muscle groups targeted
- Injury risk zones
- Progression/regression variations
4.4 Progress Tracking
Features:
- Form Score: 0-100 per exercise, tracked over time
- Improvement Graph: Visualize form getting better
- Streak Tracking: Consistency gamification
- Before/After: Compare first video vs. current
- Weak Point Identification: "Your squat depth has improved 15%, but knee alignment needs work"
4.5 Real-Time Mode (Post-MVP)
Live Camera Analysis:
- Start exercise
- AI provides audio cues during set
- "Chest up" / "Deeper" / "Good rep"
- Visual overlay on screen showing form in real-time
- Rep counter with quality indicator
5. Technical Architecture
5.1 System Overview
┌─────────────────┐
│ Mobile App │
│ (React Native) │
└────────┬────────┘
│
├─── Camera/Video Upload
│
┌────────▼────────────────────────┐
│ Backend API (Node.js/Python) │
│ │
│ ┌──────────────────────────┐ │
│ │ Video Processing │ │
│ │ - Frame extraction │ │
│ │ - Compression │ │
│ └──────────┬───────────────┘ │
│ │ │
│ ┌──────────▼───────────────┐ │
│ │ Gemini 3 Integration │ │
│ │ - Vision API │ │
│ │ - Pose estimation │ │
│ │ - Reasoning │ │
│ └──────────┬───────────────┘ │
│ │ │
│ ┌──────────▼───────────────┐ │
│ │ Form Analysis Engine │ │
│ │ - Angle calculations │ │
│ │ - Pattern matching │ │
│ │ - Risk assessment │ │
│ └──────────┬───────────────┘ │
│ │ │
│ ┌──────────▼───────────────┐ │
│ │ Feedback Generator │ │
│ │ - Prioritize issues │ │
│ │ - Generate explanations │ │
│ │ - Create annotations │ │
│ └──────────────────────────┘ │
└─────────────┬───────────────────┘
│
┌────────▼────────┐
│ Database │
│ - User data │
│ - Exercise DB │
│ - Progress │
└─────────────────┘
5.2 Gemini 3 Implementation
Prompt Engineering Strategy:
# Example prompt structure
prompt = f"""
You are an expert biomechanics coach analyzing exercise form.
Exercise: {exercise_name}
Video Analysis: I will provide frames from a {exercise_name} video.
Your task:
1. Identify body keypoints and track them across frames
2. Calculate joint angles (hip, knee, ankle, etc.)
3. Compare to ideal {exercise_name} form parameters:
{ideal_form_params}
4. Identify deviations and rank by:
- Injury risk (highest priority)
- Effectiveness loss
- Optimization opportunities
5. For each issue found:
- Specify the problem clearly
- Explain the consequence (injury risk or performance)
- Provide a specific correction
- Estimate severity (Critical/Important/Minor)
6. Generate rep count and quality score (0-100) for each rep
Output format: JSON with structure:
{{
"overall_score": 75,
"rep_count": 10,
"rep_scores": [80, 78, 75, ...],
"issues": [
{{
"severity": "critical",
"timestamp": "0:03",
"problem": "Lower back rounding",
"consequence": "Risk of disc herniation",
"correction": "Engage core, neutral spine",
"affected_joints": ["lumbar_spine"]
}}
],
"strengths": ["Good depth", "Controlled tempo"],
"next_focus": "Work on maintaining neutral spine"
}}
"""
Multimodal Input:
- Video frames (extracted at 10 fps for analysis)
- Previous session data for comparison
- User profile (height, experience level, injury history)
5.3 Tech Stack
Frontend:
- React Native (iOS + Android)
- Expo for rapid development
- React Native Vision Camera for video capture
- Canvas API for video annotations
Backend:
- Node.js with Express OR Python with FastAPI
- Google Cloud Functions for serverless scaling
- FFmpeg for video processing
AI/ML:
- Primary: Google Gemini 3 API (multimodal)
- Supplementary: MediaPipe for pose estimation preprocessing (optional)
- OpenCV for frame extraction and preprocessing
Database:
- PostgreSQL for user data, progress tracking
- Firebase Storage for video files
- Redis for caching analysis results
Infrastructure:
- Google Cloud Platform (synergy with Gemini)
- Cloud Storage for videos
- Cloud Run for containerized backend
6. User Experience
6.1 User Flow - First Time User
1. Onboarding
↓
"Welcome! Let's analyze your first exercise"
↓
2. Exercise Selection
↓
Browse library → Select "Squat"
↓
3. Setup Guidance
↓
"Position camera 6 feet away, showing full body from the side"
↓
4. Record Exercise
↓
Record 5-10 reps
↓
5. Analysis (5 seconds)
↓
"Analyzing your form..."
↓
6. Results
↓
Form Score: 72/100
↓
Video playback with annotations
↓
7. Feedback
↓
"Great depth! Focus on: keeping knees aligned with toes"
↓
8. Action
↓
[Try Again] [Save Progress] [Learn More]
6.2 Key Screens
Home Screen:
- Quick-start popular exercises
- "Record New Exercise" CTA
- Recent exercises with scores
- Weekly progress chart
- Streak counter
Exercise Library:
- Search and filter
- Categories: Compound, Isolation, Bodyweight
- Muscle group filter
- Difficulty level
Recording Screen:
- Live camera preview
- Countdown timer
- Form checklist reminder
- Camera angle guidance overlay
Analysis Screen:
- Video player with annotations
- Overall form score (prominent)
- Issue cards (expandable)
- Rep-by-rep breakdown
- Action buttons: "Try Again", "Save", "Share"
Progress Dashboard:
- Exercise history
- Form improvement graphs
- Achievements/badges
- Weak points to focus on
7. Competitive Advantage
Why This Wins the Hackathon:
Technical Execution (40%): ✅ Deep integration with Gemini 3's multimodal capabilities ✅ Complex video analysis + reasoning pipeline ✅ Clean, production-ready code architecture ✅ Functional demo with real-time feedback
Innovation/Wow Factor (30%): ✅ Novel application of LLM reasoning to biomechanics ✅ Combines computer vision + expert reasoning in unique way ✅ "Wow" moment when users see annotated video with precise feedback ✅ Real-time mode is technically impressive
Potential Impact (20%): ✅ Huge market: 184M gym memberships worldwide ✅ Clear ROI: Prevent injuries, replace expensive trainers ✅ Accessibility: Makes expert coaching available to everyone ✅ Scalable to physical therapy, sports training
Presentation (10%): ✅ Clear before/after demo ✅ Relatable problem statement ✅ Strong architectural documentation ✅ Obvious Gemini 3 value-add
What Makes This Different:
vs. Existing Form Apps:
- Most use basic pose estimation without reasoning
- We use Gemini 3 to understand biomechanics and explain why
- Context-aware: considers user history, injury risk, experience level
vs. Personal Trainers:
- Available 24/7
- Fraction of the cost
- Consistent quality
- Objective measurements
8. MVP Scope (Hackathon Demo)
Must-Have (Build This):
- Exercise Selection: 5 core exercises (squat, pushup, deadlift, plank, lunge)
- Video Upload: Record or upload 10-30 second video
- Gemini 3 Analysis:
- Extract poses from video frames
- Analyze form against ideal parameters
- Generate detailed feedback with explanations
- Results Display:
- Annotated video with markers
- Form score
- Top 3 corrections prioritized
- "Why this matters" explanations
- Simple Progress Tracking: Save results, show improvement over time
Nice-to-Have (If Time Permits):
- Rep counting
- Multiple camera angles
- Real-time audio cues
- Exercise library with 20+ exercises
- Social sharing
Demo Script (3 minutes):
Minute 1: Problem
- "Show hands - who's injured themselves working out?"
- Show injury statistics
- "Personal trainers cost $75/hour. What if AI could coach you?"
Minute 2: Solution
- Live demo: Record a squat with intentional form error
- Show Gemini 3 analyzing the video
- Display annotated feedback: "See how it caught the knee valgus and explained the injury risk?"
- Show the explanation depth: quick fix → biomechanics
Minute 3: Impact + Tech
- Show progress tracking: "Here's improvement over 3 sessions"
- Architecture diagram: "Gemini 3's multimodal capabilities are key - vision for pose + reasoning for context"
- Market opportunity: "184M gym members, $96B fitness industry"
- Call to action: "FormFit AI - Expert coaching for everyone"
9. Development Timeline (Hackathon - 48 hours)
Hour 0-8: Setup & Core Infrastructure
- Project setup, repo structure
- Gemini 3 API integration
- Basic video upload/processing pipeline
- Database schema
Hour 8-20: Analysis Engine
- Frame extraction from video
- Gemini 3 prompt engineering for pose analysis
- Form evaluation logic
- JSON response parsing
Hour 20-32: Frontend & UX
- Exercise selection UI
- Video recording/upload
- Results display with annotations
- Form score visualization
Hour 32-42: Polish & Demo Prep
- Test with all 5 exercises
- Refine feedback quality
- Create demo video with intentional errors
- Progress tracking feature
- Bug fixes
Hour 42-48: Documentation & Presentation
- Architecture diagram
- README with Gemini 3 integration details
- Demo script practice
- Pitch deck (5 slides)
10. Risk Mitigation
| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| Gemini 3 API latency too slow | Medium | High | Pre-process videos, async analysis, show loading states |
| Form analysis accuracy insufficient | Medium | Critical | Curate high-quality training examples, focus on 5 exercises done well |
| Video quality too poor | Low | Medium | Guide users on camera placement, validate video quality before analysis |
| Scope creep | High | Medium | Strict MVP definition, defer features ruthlessly |
| Gemini 3 API rate limits | Low | High | Implement caching, request queuing, have backup demo videos |
11. Future Roadmap (Post-Hackathon)
Phase 1 (Months 1-3):
- Expand to 100+ exercises
- Add real-time mode
- Trainer certification program (validate AI accuracy)
Phase 2 (Months 4-6):
- Physical therapy integration
- Custom workout plans based on form analysis
- Integration with fitness trackers
Phase 3 (Months 7-12):
- B2B partnerships (gyms, PT clinics)
- Sports-specific training (golf swing, baseball pitch, etc.)
- AR overlay for real-time corrections
12. Success Criteria for Hackathon
✅ Working demo that analyzes form and provides feedback ✅ Clear Gemini 3 integration with documented prompts ✅ Impressive "wow" moment in presentation ✅ Clean code with architecture diagram ✅ Real-world applicability demonstrated
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