Students can't build their focus while studying and are not able to identify the issue
A desk device which will contain an eye retina sensor which will scan eye of the user and indicate when the user is distracted by producing a beep sound
Renukoot, Sonebhadra, UttarPradesh
Solution
| Component | Description |
|---|---|
| Technical Components | ### **Comprehensive Analysis of FocusTrack Solution** #### **1. Technical Components Needed** To meet the requirements while avoiding prohibited behaviors, the following components are necessary: **a. Hardware Components:** - **Retina Scanner Module**: Infrared-based eye-tracking sensor (e.g., Tobii Eye Tracker or custom IR sensor) - **Microcontroller (MCU)**: Raspberry Pi (for prototyping) or ESP32 (low-cost alternative) - **Sound Alert System**: Small piezoelectric buzzer or speaker - **Light Sensor**: Ambient light detection (BH1750 or similar) - **Local Storage**: MicroSD card or onboard flash memory (for offline tracking) - **Power Supply**: Rechargeable battery/USB power **b. Software Components:** - **Eye-Tracking Algorithm**: OpenCV + Dlib (for gaze detection) - **Local Data Processing**: Python/C++ for real-time analysis - **Mobile App**: Flutter/React Native (cross-platform) - **Backend (Optional)**: Firebase/Node.js (for cloud sync) - **Offline Sync Mechanism**: SQLite (local storage) **c. Avoidance of Prohibited Behaviors:** - **No Fake Reports**: Data validation before syncing - **Minimal Distraction**: Soft beep (adjustable volume) - **Offline Functionality**: Local storage + delayed sync --- #### **2. Recommended Tech Stack** | **Component** | **Tech Choices** | **Rationale** | |---------------------|------------------------------------------|---------------| | **Eye Tracking** | OpenCV + Dlib, Tobii SDK (if commercial) | Reliable gaze detection | | **Microcontroller** | Raspberry Pi 4 / ESP32 | Cost-effective, supports Python/C++ | | **Mobile App** | Flutter | Cross-platform (iOS/Android) | | **Backend** | Firebase (optional) | Easy sync, offline-first design | | **Local Storage** | SQLite | Lightweight, offline-compatible | --- #### **3. Detailed Implementation Steps** **Phase 1: Proof of Concept (Zero-Cost Model)** - ✔ **Objective**: Validate eye-tracking feasibility - ✔ **Tasks**: - Use a webcam + OpenCV to detect gaze direction (Python) - Simulate beep alerts when gaze drifts - Log focus duration locally **Phase 2: Prototype Development** - ✔ **Objective**: Functional hardware + basic app - ✔ **Tasks**: - Integrate IR eye tracker with Raspberry Pi - Develop Flutter app (basic UI + timer) - Implement local storage (SQLite) - Test offline functionality **Phase 3: Market-Ready Product** - ✔ **Objective**: Polish features, optimize UX - ✔ **Tasks**: - Miniaturize hardware (custom PCB) - Add cloud sync (Firebase) - Improve analytics dashboard - Beta testing with students --- #### **4. Required Technical Learning** - **Eye Tracking**: OpenCV + Dlib (Python/C++) - **Embedded Systems**: Raspberry Pi/ESP32 programming - **App Development**: Flutter (Dart) - **Data Sync**: Firebase/SQLite --- #### **5. Budget Calculation** | **Category** | **Item** | **Estimated Cost** | |-----------------------|-----------------------------|-------------------| | **Hardware Costs** | Raspberry Pi 4 + Camera | $60 | | | IR Eye Tracker Module | $50-$200 | | | Piezo Buzzer + Light Sensor | $10 | | | Enclosure + Battery | $30 | | **Software Costs** | Firebase (Spark Plan) | $0 (Free Tier) | | | OpenCV/Dlib (Open-Source) | $0 | | **Development Costs** | Prototyping (3 months) | $3,000 (labor) | | **Maintenance** | Server + Bug Fixes (Year 1) | $500 | | **Total** | | **$3,750+** | *(Note: Costs vary based on component choices and scale.)* --- ### **Final Recommendations** - **Start with a webcam-based POC** before investing in IR sensors. - **Use Flutter for the app** to ensure cross-platform compatibility. - **Prioritize offline functionality** with SQLite + delayed sync. - **Test extensively** to avoid false alerts (distraction risk). Would you like a deeper dive into any specific area (e.g., eye-tracking algorithms, PCB design)? |
| Key Features |
Feature: Eye retina scanning
Format: Retina sensor in desk device Usage: Detects if the student is looking at books/ screen
Feature: Study time tracking
Format: Device + App timer Usage: Records total focused study duration
Feature: Distraction alerts
Format: Beep sound from the device Usage: Reminds student when attention drifts
Feature: Progress Analytics
Format: Graphs, charts in App Usage: Displays focus score trends and improvement areas
Feature: Goals setting and Reminders
Format: App interface Usage: Allows students to set targets and track completion
Feature: Offline Tracking
Format: Local device memory Usage: Saves focus data without internet, syncs later |
| Implementation Steps | Firstly, we will prepare zero cost model see will it work or not then prepare a prototype and add some features and finally make it market ready |