Activity Classification & Prediction

Overview

This project runs a activity classification model (Random Forest/Decision Tree) on the cloud based on sensors deployed in a house (ARUBA DATASET) and it predicts based on that.

The actual setup of the sensors are not done, so it’s a simulated sensor setup that randomly takes sensor values and according to timestamps and sends to the model to classify and hence predict the activity done at that time using those virtual sensor data.

This project serves as a proof of concept that one can predict and monitor activity over the internet through sensors while being away for tasks such as elderly care monitoring

Table of Contents

• Activity Classification & Prediction with Random Forest Classifier

  • Overview

  • Table of Contents

  • Requirements

  • Setup

    • Flask Server
    • Flutter Android App

  • Usage

  • Screenshots

  • License

Requirements

List all the dependencies and requirements for running the project.

Setup

Flask Server

1. Install Dependencies:

   pip install -r requirements.txt
   

   P.S. while running the code there may be some other dependency issues, that you may have to fix manually

2. Run the Model: Run RF.ipynb to generate the random_forest_model.mod

   python app.py
   

3. Run the server

   python server.py
   

   The Flask server will be running at http://<IP ADDRESS>:12345.

Flutter Android App

1. Install Flutter: Follow the official Flutter installation guide to install Flutter on your machine.

2. Install Packages:

   flutter pub get
   

3. Run the App:

   flutter run
   

4. Run the Following if required

// For Launcher Icon Generation
flutter pub run flutter_launcher_icons 

// For Running App (main.dart)
dart run launcher_name:main

// For generating App Splash Logo & Branding

dart run flutter_native_splash:create

// For generating App project

flutter create --project-name activity_sensor_flutter --org com.activity . 

Ensure that an Android emulator or a physical device is connected.

Usage

1. Run the server
2. Note the IP Address and Port
3. Open the app
4. Go to Settings
5. Input the IP and Port
6. Come back to home page
7. Pull down to refresh

Screenshots

License

This project is licensed under the MIT License.