💊 PillReminder - Offline-First Medication Management

A multimodal AI system to digitize medication instructions with 100% offline reliability, mitigating clinical errors in low-resource settings.

🏆 Winner: Best Poster Presenter, NIRAMAYA Conference (2026)

🌟 Key Features

🔒 Edge-First AI Architecture

• 100% Offline Processing - Works perfectly without internet connection
• Instant Results - <1 second processing time
• Privacy-First - All data stays on your device by default
• Zero Cost - No API charges for normal operation

🤖 Advanced AI Stack

• Dual-Layer Offline Pipeline - Combines ML Kit for text recognition with a custom computer vision validation module.
• Interpretable Computer Vision - Hand-engineered marker detection using dual-axis spatial projection histograms, adaptive ROI localization (silver foil segmentation), and valley-splitting peak detection.
• Gemini 2.0 Flash - Optional cloud enhancement for complex cases
• TensorFlow.js - 1024-dimensional visual embeddings for medication matching
• Fuzzy Matching - Spell-corrects OCR errors against medication database

📱 Smart Features

• Multi-image scanning (front + back of packaging)
• Handwritten dosage detection (circles, loops, marks)
• Grid layout detection (2x5, 3x7, etc.)
• Visual medication matching
• Nepali/English support
• Medication reminders & scheduling

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🌐 Multilingual Support

• English - Full support
• Nepali - Full support (Devanagari script)
• Dosage pattern detection for both languages
• Context-aware timing extraction ("खाना पछि" / "after food")

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🔐 Privacy & Security

• ✅ Offline-first: No data sent to cloud by default
• ✅ Local storage: All medications stored on-device
• ✅ No tracking: No analytics or telemetry
• ✅ Optional cloud: Explicit user consent required
• ✅ Medical data: Complies with privacy best practices

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🏗️ Architecture

Dual-Layer Offline Processing Flow

📸 Scan Blister Pack Image
   ↓
🔍 Layer 1: ML Kit OCR (Extract Text / Brand / Instructions)
   ↓
👁️ Layer 2: Custom CV Validation (Spatial Histograms for Handwritten Marks)
   ↓
🧠 Offline Parser + Fuzzy Matching (Reconcile OCR & CV outputs)
   ↓
✅ Return Results (<1 second total)
   ↓
💾 Save to Local Database (WatermelonDB)

Optional Cloud Enhancement

// For complex cases, users can manually enhance:
import { enhanceWithCloudAI } from '@/services/ai/ocr';

const enhanced = await enhanceWithCloudAI(imageUri, currentData);
console.log(`Confidence improved by ${(enhanced.confidence - currentData.confidence) * 100}%`);

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🛠️ Technology Stack

Core Framework

• React Native - Cross-platform mobile framework
• Expo - Development tooling and SDK
• TypeScript - Type-safe JavaScript
• Expo Router - File-based navigation

AI/ML

• @google/generative-ai - Gemini 2.0 Flash (optional cloud)
• @tensorflow/tfjs - Machine learning in JavaScript
• @tensorflow/tfjs-react-native - TensorFlow for React Native
• @react-native-ml-kit/text-recognition - Offline OCR

State & Data

• @nozbe/watermelondb - Offline-first reactive database
• zustand - Lightweight state management
• @react-native-async-storage/async-storage - Persistent storage

UI/UX

• nativewind - Tailwind CSS for React Native
• react-native-reanimated - Smooth animations
• expo-notifications - Local medication reminders
• expo-haptics - Tactile feedback

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🚀 Getting Started

Prerequisites

• Node.js 18+ and npm
• Expo CLI
• iOS Simulator or Android Emulator (or physical device)

Installation

1. Clone and install dependencies

   cd PillReminder
   npm install

2. Start the development server

   npx expo start

3. Run on device

   • Press i for iOS simulator
   • Press a for Android emulator
   • Scan QR code with Expo Go app on physical device

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📝 Environment Variables

Create a .env file in the root directory:

# Optional: Only needed if enabling cloud enhancement
EXPO_PUBLIC_GEMINI_API_KEY=your_api_key_here

Note: App works fully without this key (offline-only mode).

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🎯 AI Configuration

Default Settings

// services/ai/config.ts
const DEFAULT_CONFIG = {
    processingMode: 'offline-only',    // No cloud by default
    logPerformance: true,               // Track processing times
    trackOfflineAccuracy: true,         // Collect data for model training
};

Enable Cloud Enhancement

import { setAIConfig } from '@/services/ai/config';

await setAIConfig({
    processingMode: 'allow-cloud-enhancement'
});

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🧪 Testing Offline Mode

Verify 100% Offline Reliability

1. Enable airplane mode on your test device
2. Open PillReminder app
3. Scan a medication image
4. Verify instant results without errors

Expected: Processing completes in <1 second with no network errors.

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📂 Project Structure

PillReminder/
├── app/                    # Expo Router screens
│   ├── camera.tsx         # Medication scanning
│   ├── modal.tsx          # Add/edit medication
│   ├── alarm.tsx          # Medication reminders
│   └── ...
├── services/
│   ├── ai/
│   │   ├── ocr.ts         # Main AI processing (offline-first)
│   │   ├── config.ts      # AI preferences & metrics
│   │   ├── vision-matcher.ts  # 1024-dim embeddings
│   │   ├── marker-detector.ts # Handwriting CV
│   │   ├── offline-parser.ts  # Text parsing
│   │   └── medication-db.ts   # Fuzzy matching
│   ├── db/                # WatermelonDB models
│   └── scheduler.ts       # Notification scheduling
├── components/            # Reusable UI components
└── lib/                   # Utilities & state

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📊 Performance Metrics

Metric	Target	Actual
Processing Time (Offline)	<1s	~800ms ⚡
Processing Time (Online)	<5s	~3-4s
Offline Accuracy (Simple)	>80%	~85%
Offline Accuracy (Complex)	>70%	~75%
Cloud Accuracy	>90%	~95%

Metrics tracked automatically via config.ts

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🔬 Future Enhancements

Model Fine-Tuning

The app tracks offline processing metrics for future improvements:

// Automatically logged
interface ProcessingMetrics {
    timestamp: number;
    processingTime: number;
    confidence: number;
    mode: 'offline' | 'online';
    medicationName: string;
}

Roadmap:

1. Collect 1000+ medication scans with user corrections
2. Train specialized OCR model for medication packaging
3. Fine-tune handwriting detection for local practices
4. Deploy updated TensorFlow.js model

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🐛 Troubleshooting

Camera not working

# iOS: Rebuild with camera permissions
npx expo prebuild --clean
npx expo run:ios

Slow processing

• Check device specs (needs TensorFlow.js support)
• Reduce image size in camera settings
• Verify ML Kit is installed correctly

TypeScript errors

# Rebuild with proper types
rm -rf node_modules
npm install
npx expo start --clear

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• Google Gemini - Multimodal AI capabilities
• TensorFlow.js - Edge ML framework
• ML Kit - Fast offline OCR
• Expo - Development platform

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📧 Contact

For questions, support, or to contribute, please visit the GitHub Repository and open an issue.

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Built with ❤️ for improving medication safety in low-resource settings