Learn2Vibe

How to Build a Smart Desk Lighting Mood Synchronizer

Create an intelligent desk lighting system that adapts to your mood and activities. This project combines hardware and software to deliver personalized lighting experiences, boosting productivity and creating the perfect ambiance for any task or emotion.

Create your own plan

Learn2Vibe AI

Online

AI

What do you want to build?

Login or Sign Up Required

You need to be logged in to save this plan to your profile.

LoginSign Up

Simple Summary

Build a smart desk lighting system that synchronizes with your mood and activities, enhancing productivity and ambiance in your workspace.

Product Requirements Document (PRD)

Goals:

  • Create a smart lighting system that responds to user's mood and activities
  • Enhance workspace productivity through intelligent lighting
  • Provide a customizable and user-friendly lighting experience

Target Audience:

  • Professionals working from home
  • Students
  • Anyone interested in smart home technology

Key Features:

  1. Mood detection through user input or wearable device integration
  2. Activity recognition (e.g., working, relaxing, reading)
  3. Customizable lighting presets for different moods and activities
  4. Voice control integration
  5. Mobile app for remote control and customization
  6. Energy efficiency tracking and optimization

User Requirements:

  • Easy setup and configuration
  • Intuitive controls for adjusting lighting
  • Seamless integration with existing smart home ecosystems
  • Ability to create and save custom lighting scenes
  • Privacy-focused design for mood and activity data

User Flows

  1. Mood-based Lighting Adjustment:

    • User opens mobile app
    • Selects current mood or allows automatic detection
    • System adjusts lighting to match mood
    • User can fine-tune settings if desired

  2. Activity-based Lighting Preset:

    • User starts a specific activity (e.g., reading)
    • System detects activity or user selects it manually
    • Lighting adjusts to optimal settings for the activity
    • User can save custom presets for future use

  3. Voice Control Interaction:

    • User issues voice command (e.g., "Set lighting for relaxation")
    • System processes command and adjusts lighting accordingly
    • User receives verbal confirmation of the change

Technical Specifications

  • Hardware: Custom PCB with microcontroller (e.g., ESP32), LED strips, sensors
  • Firmware: C++ with Arduino framework
  • Mobile App: React Native for cross-platform development
  • Backend: Node.js with Express.js
  • Database: MongoDB for user preferences and lighting scenes
  • APIs: RESTful API for mobile app communication
  • Integrations: IFTTT for broader smart home connectivity
  • Machine Learning: TensorFlow Lite for activity recognition (optional)

API Endpoints

  • POST /api/auth/register
  • POST /api/auth/login
  • GET /api/lighting/presets
  • POST /api/lighting/presets
  • PUT /api/lighting/current
  • GET /api/user/preferences
  • PUT /api/user/preferences
  • POST /api/activities/detect

Database Schema

Users:

  • id: ObjectId
  • email: String
  • password: String (hashed)
  • preferences: Object

LightingPresets:

  • id: ObjectId
  • userId: ObjectId
  • name: String
  • settings: Object

ActivityLogs:

  • id: ObjectId
  • userId: ObjectId
  • activity: String
  • timestamp: Date
  • lightingUsed: Object

File Structure

smart-desk-lighting/ ├── firmware/ │ ├── src/ │ │ ├── main.cpp │ │ ├── lighting.h │ │ ├── sensors.h │ │ └── wifi_manager.h │ └── platformio.ini ├── mobile-app/ │ ├── src/ │ │ ├── components/ │ │ ├── screens/ │ │ ├── services/ │ │ └── App.js │ └── package.json ├── backend/ │ ├── src/ │ │ ├── routes/ │ │ ├── models/ │ │ ├── controllers/ │ │ └── app.js │ └── package.json ├── ml-models/ │ └── activity_recognition.tflite └── README.md

Implementation Plan

  1. Hardware Development (2 weeks)

    • Design and prototype custom PCB
    • Integrate microcontroller and sensors
    • Test basic lighting control

  2. Firmware Development (3 weeks)

    • Implement core lighting control functions
    • Develop Wi-Fi connectivity and API communication
    • Create basic mood and activity detection algorithms

  3. Mobile App Development (4 weeks)

    • Design UI/UX for the app
    • Implement user authentication and profile management
    • Develop lighting control interface and preset management

  4. Backend Development (3 weeks)

    • Set up Node.js server and MongoDB database
    • Implement RESTful API for mobile app and device communication
    • Develop user management and preference storage systems

  5. Integration and Testing (2 weeks)

    • Integrate all components (hardware, firmware, app, backend)
    • Conduct thorough testing of all features
    • Perform security audit and optimize performance

  6. Machine Learning Integration (Optional, 2 weeks)

    • Train activity recognition model
    • Implement model in firmware and test accuracy

  7. Final Testing and Refinement (1 week)

    • Conduct user acceptance testing
    • Address feedback and bug fixes
    • Prepare for production release

Deployment Strategy

  1. Hardware Production:

    • Finalize PCB design and order small batch for testing
    • Assemble and quality check devices

  2. Firmware Deployment:

    • Set up OTA (Over-The-Air) update system
    • Deploy initial firmware to devices

  3. Mobile App Release:

    • Submit to App Store and Google Play Store
    • Plan for phased rollout to manage load and gather feedback

  4. Backend Deployment:

    • Set up cloud infrastructure (e.g., AWS, Google Cloud)
    • Deploy backend services with containerization (Docker)
    • Implement CI/CD pipeline for automated testing and deployment

  5. Database:

    • Set up MongoDB Atlas for scalable, managed database solution

  6. Monitoring and Maintenance:

    • Implement logging and monitoring (e.g., ELK stack, Prometheus)
    • Set up automated alerts for critical issues
    • Plan regular maintenance and update schedule

Design Rationale

The Smart Desk Lighting Mood Synchronizer is designed with a modular architecture to allow for easy expansion and maintenance. The choice of ESP32 for the hardware provides a good balance of processing power and connectivity options. React Native is used for the mobile app to ensure cross-platform compatibility and reduce development time. The Node.js backend with MongoDB offers flexibility and scalability for handling user data and device communication. The optional machine learning component allows for more accurate activity recognition, enhancing the user experience. Overall, this design focuses on creating a responsive, user-friendly system that can easily integrate with existing smart home setups while prioritizing privacy and energy efficiency.