Sentinel

Sentinel is an offline-first, AI-powered medical triage application designed to provide immediate first-aid guidance. By leveraging a custom 1D-CNN model and a Sentence Transformers model optimized for mobile devices, it assesses symptoms in real-time to categorize emergencies and offer validated medical protocols without requiring an internet connection.

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✨ Key Features

🧠 AI & Triage

• Smart Severity Grading: Automatically classifies incidents into Critical, Urgent, or Routine based on symptom description.
• Hybrid Inference: Uses a specialized 1D-CNN for rapid classification with a fallback to semantic analysis for complex queries.
• Confidence Scoring: Visual indicators (Green/Orange/Red) showing the AI's certainty level.

📱 User Experience

• Voice-to-Text Interface: Hands-free operation for high-stress emergency situations.
• Local History: Secure, on-device storage of past assessments (GDPR/HIPAA compliant design).
• Tactile Feedback: Haptic responses and pulse animations during voice recording.

🚑 Safety Protocols

• Emergency Integration: One-tap direct connection to emergency services (999/911) for critical triage results.
• Validated Content: First-aid steps derived from standard medical protocols.

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🧠 Machine Learning Architecture

Sentinel is powered by a custom Text Classification Model trained specifically for medical intent recognition.

The Model Pipeline (ml_pipeline/notebooks/text_classification.ipynb)

1. Preprocessing:

   • Custom stop-word removal (filtering conversational filler like "please", "i think").
   • Tokenization with a vocabulary size of 1000 words.
   • Sequence padding to a fixed length of 15 tokens.

2. Network Architecture: The model utilizes a 1D-Convolutional Neural Network (CNN) structure, chosen for its ability to detect local patterns (n-grams) in short text sequences.

     graph TD
     A["Input Sequence (15 tokens)"] --> B["Embedding Layer (32-dim)"]
     B --> C["Conv1D (64 filters, kernel=3)"]
     C --> D["GlobalMaxPooling1D"]
     D --> E["Dense Layer (32 units, ReLU)"]
     E --> F["Dropout (0.4)"]
     F --> G["Output Layer (Softmax)"]
   

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   • Conv1D: Detects specific symptom phrases (e.g., "chest pain", "bone sticking out").
   • GlobalMaxPooling: Captures the most significant feature in the sentence, ensuring that critical keywords trigger the correct classification regardless of their position.
   • Dropout: Prevents overfitting during training on the small, specialized dataset.

Secondary Model: Semantic Vector Search (Transformer)

When the primary CNN is uncertain (confidence < 65%), Sentinel engages a quantized version of the all-MiniLM-L6-v2 Sentence Transformer. Unlike the CNN, this model does not classify text directly but understands meaning.

1. Embedding Generation: The user's input is tokenized (BERT WordPiece) and passed through the Transformer to generate a 384-dimensional dense vector representation.

2. Vector Similarity: This vector is compared against a local database of "Anchor Vectors" (pre-computed centroids for each medical intent) using Cosine Similarity.

   graph LR
   A[User Input] --> B[BERT Tokenizer];
   B --> C[MiniLM Transformer];
   C --> D[384-dim Vector];
   D --> E{Dot Product Comparison};
   E --> F[Anchor: Fracture];
   E --> G[Anchor: Burn];
   E --> H[Anchor: CPR];
   E --> I[Result: Highest Similarity];
   

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• Why this matters: It allows the app to understand phrasing it has never seen before (e.g., "my tummy feels like it's on fire" matches "Abdominal Pain") by mapping them to the same semantic space.
• Performance: The model is quantized to int8, reducing size to ~20MB while maintaining >81% accuracy on the MTEB leaderboard benchmarks.

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🛠️ Tech Stack

Mobile Application

• Framework: React Native (via Expo SDK 50+)
• Language: TypeScript
• State Management: React Hooks & Context
• UI Components: React Native Paper
• Animations: React Native Reanimated 3

Machine Learning

• Training: Python, TensorFlow, Keras
• Inference: TensorFlow Lite (on-device)
• Data Processing: Pandas, NumPy

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📂 Project Structure

sentinel/
├── android/                # Native Android project files
├── assets/                 # Static assets (fonts, images)
├── ml_pipeline/            # Python ML environment
│   ├── data/               # Raw training datasets (intents.json)
│   ├── notebooks/          # Jupyter notebooks (V4 Model Training)
│   └── preprocess.py       # Data cleaning logic
├── src/
│   ├── app/                # Expo Router file-based navigation
│   ├── assets/             # App-bundled assets
│   │   └── models/         # TFLite models & vocabularies
│   ├── components/         # Reusable UI components
│   ├── constants/          # App-wide constants (Colors, SeverityMap)
│   ├── data/               # Static data (emergency_data.json)
│   ├── hooks/              # Custom hooks (useVoiceToText)
│   ├── screens/            # Main screen logic (ChatScreen)
│   └── services/           # Core Business Logic
│       ├── CNNEngine.ts    # Primary 1D-CNN Inference
│       ├── CNNVocab.ts     # Vocabulary management for CNN
│       ├── HistoryService.ts # Local storage (AsyncStorage)
│       ├── HybridEngine.ts # Orchestrator for CNN + ST
│       ├── KnowledgeBase.ts # Medical protocols database
│       ├── STEngine.ts     # Secondary Transformer Inference
│       └── STVocab.ts      # Vocabulary for Transformer
├── app.json                # Expo configuration
└── package.json

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🚀 Getting Started

Option A: Download Latest Release (Recommended for Users)

You can download the latest Android APK from our GitHub Releases Page.

1. Download sentinel-v1.0.apk.
2. Install on your Android device (you may need to allow installation from unknown sources).

Option B: Build from Source (For Developers)

⚠️ Important: This project uses native modules (TensorFlow Lite) and cannot be run in the standard Expo Go app. You must build a Development Client.

1. Clone the repository

   git clone https://github.com/yourusername/sentinel.git
   cd sentinel

2. Install dependencies

   npm install

3. Build & Run Development Client Connect your Android device via USB (ensure USB Debugging is on) or start an emulator.

   # This will build the native app and install it on your device
   npx expo run:android

   (For iOS, use npx expo run:ios - requires macOS)

4. Start the Bundler Once the app is installed on your device, the Metro bundler should start automatically. If not:

   npx expo start --dev-client

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👥 Team Credits

• Mohammad Kamrul Hasan (Lead Developer)
• Hasibul Islam (Developer)
• Sayeeb Rahman (Researcher)

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⚠️ Medical Disclaimer

Sentinel is a prototype for educational and demonstrative purposes only.

It is not a replacement for professional medical advice, diagnosis, or treatment. The triage results are generated by an AI model and may be inaccurate. In the event of a medical emergency, always contact your local emergency services immediately.