NGMI 3.0: AR-Powered Physical Challenge Verification
NGMI 3.0 represents a revolutionary proof-of-concept that will transform the platform from digital prediction markets to a physical-digital fusion system. This document outlines the core concepts, technical foundation, and implementation strategy for this ambitious evolution.
Core Innovation: The AR Verification Engine
The foundation of NGMI 3.0 is the AR Verification Engine – a sophisticated system that transforms smartphone sensors into reliable verification tools for physical activities.
Sensor Fusion Architecture
┌─────────────────────────────────────────────────┐
│ Raw Sensor Data │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────┐ │
│ │Accel. │ │Gyroscope│ │Camera │ │GPS │ │
│ └────┬────┘ └────┬────┘ └────┬────┘ └──┬──┘ │
└───────┼─────────────┼─────────────┼─────────┼───┘
│ │ │ │
▼ ▼ ▼ ▼
┌────────┴─────────────┴─────────────┴─────────┴───┐
│ │
│ Low-Level Sensor Fusion │
│ (Kalman filtering, complementary filters) │
│ │
└─────────────────────────┬──────────────────────┬─┘
│ │
▼ ▼
┌───────────────────────────┐ ┌─────────────────────┐
│ │ │ │
│ Motion Recognition │ │ Spatial Tracking │
│ (Activity patterns) │ │ (Position, paths) │
│ │ │ │
└─────────────┬─────────────┘ └──────────┬──────────┘
│ │
▼ ▼
┌────────────────────────────────────────────────┐
│ │
│ Verification Engine │
│ (Rule checking, anti-cheat logic) │
│ │
└────────────────────────┬───────────────────────┘
│
▼
┌─────────────────────────┐
│ │
│ Result Output │
│ (Confidence scoring) │
│ │
└─────────────────────────┘
Verification Methodology Classification
NGMI 3.0 POC will implement the first three levels of our five-level verification framework:
| Category | Verification Method | Example Challenges | Technical Requirements |
|---|---|---|---|
| Level 1 | Single sensor | Jump height, steps counted | Accelerometer only |
| Level 2 | Multi-sensor fusion | Running speed, orientation | Accelerometer + GPS/Gyroscope |
| Level 3 | Visual verification | Target hitting, posture holding | Camera + ML model |
| Level 4 (Future) | Comprehensive tracking | Form analysis, complex movements | Multiple sensors and ML |
| Level 5 (Future) | Environmental interaction | Object manipulation, spatial challenges | Advanced SLAM and object recognition |
Self Domain: Physical Challenges as Personal Goals
The Self Domain focuses on personal improvement and habit formation through verifiable physical challenges.
Key Features for POC
- Personal Goals System: Set physical targets with AR verification
- Progress Tracking: Visual representation of improvement over time
- Self-Prediction: Create personal commitment markets
- Challenge Templates: Pre-defined challenges with verification parameters
Self Challenge Types (Initial Implementation)
| Challenge Category | Description | Verification Method | Example Challenges |
|---|---|---|---|
| Jump Performance | Vertical jump height | Accelerometer with flight time calculation | Max height jump, consistency challenges |
| Step Counting | Step-based movement | Accelerometer pattern recognition | Daily step goals, pace challenges |
| Balance Tests | Stability challenges | Gyroscope-based stability metrics | Time-based balance holds, stability precision |
| Posture Holds | Basic form maintenance | Camera-based pose estimation | Plank holds, posture maintenance |
Anti-Cheating System
A fundamental requirement for verifiable physical challenges is a robust anti-cheating system. The POC will implement:
1. Multi-layered Defense
┌─────────────────────────────────────────────────────┐
│ │
│ Multi-layered Defense System │
│ │
└─────────────────────────────────────────────────────┘
│
┌───────────────┼───────────────┐
│ │ │
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ │ │ │ │ │
│ Technical │ │ Statistical │ │ Random │
│ Validation │ │ Analysis │ │ Elements │
│ │ │ │ │ │
└─────────────┘ └─────────────┘ └─────────────┘
2. Key Anti-Cheating Measures
- Sensor cross-validation: Comparing data across multiple sensors
- Pattern analysis: ML-based detection of unnatural movement patterns
- Randomization: Dynamic challenge parameters that prevent pre-recorded submissions
- Performance consistency: Comparing against user history and population norms
- Visual markers: Required visual elements that must appear during verification
AR Interface Design
NGMI 3.0 implements a minimal but effective AR interface focused on verification rather than visual spectacle.
Essential Visual Elements
- Challenge boundaries and target indicators
- Form guides and alignment markers
- Success/failure indicators
- Progress visualization
- Calibration guides
User Interaction Flow
- Environment Setup: Initial scanning and preparation
- Calibration Phase: Device and user positioning
- Challenge Execution: Performance with real-time feedback
- Verification Process: Multi-factor validation
- Results Display: Performance metrics and confidence score
Technical Requirements
The NGMI 3.0 POC targets modern smartphones with AR capabilities:
Device Requirements
- iOS 14+ with ARKit 4.0+ support
- Android 10+ with ARCore 1.23+ support
- Camera with 720p+ capability
- Accelerometer, gyroscope access
- 2GB+ RAM recommended
Implementation Stack
- AR Framework: Unity with AR Foundation (cross-platform)
- Computer Vision: MediaPipe (optimized for mobile)
- Sensor Fusion: Custom implementation with Kalman filtering
- Machine Learning: TensorFlow Lite for on-device processing
- Backend: Integration with NGMI 2.0 prediction markets
POC Development Timeline
The NGMI 3.0 POC will be developed in three phases:
Phase 1: Foundation (Q1-Q2 2027)
- Basic sensor data processing pipeline
- Initial challenge definition framework
- Simple verification for Level 1 challenges
- Core UI implementation
Phase 2: Enhancement (Q3 2027)
- Computer vision integration
- Level 2-3 challenge support
- Enhanced anti-cheating measures
- Self domain basic functionality
Phase 3: Integration & Testing (Q4 2027)
- Integration with NGMI 2.0 prediction markets
- Internal testing and optimization
- Preparation for limited alpha release
- Documentation and feedback systems
Alpha Program
A limited alpha program will begin in Q1 2028:
- 500-1,000 selected participants
- Focus on fitness enthusiasts and early adopters
- Core Self Domain challenges only
- Basic verification functionality
- Heavy focus on feedback collection
Success Criteria
The POC will be evaluated based on:
- Verification accuracy rates (target: >90% for Level 1, >80% for Level 2-3)
- False positive/negative percentages (target: less than 5% false positives, less than 10% false negatives)
- Device compatibility coverage (target: >95% of Tier 1 devices, >80% of Tier 2)
- User experience feedback measurements
- Technical performance metrics (battery impact, processing time)
Future Extensions
The successful POC will lead to further development in:
- Social Domain: Friend-to-friend challenges, social verification
- Advanced Challenge Types: Complex movements, form analysis
- Expanded Device Support: Broader device compatibility
- Enhanced Anti-Cheating: More sophisticated verification methods
NGMI 3.0 represents just the beginning of our journey into physical-digital fusion. The proof-of-concept will validate our core assumptions and technical approach, setting the stage for the more comprehensive NGMI 3.5 MVP.