The Posture Detection App is designed to monitor and improve user posture using an ESP32 microcontroller paired with four sensors. This app captures real-time posture data, sends it to an iOS app for display, and stores it in local CoreData for immediate analysis. Long-term posture trends are further analyzed using machine learning (ML) models supplied by an external backend, offering insightful feedback on posture improvements over time.

In addition to its posture-tracking capabilities, the app features secure login functionality, integrated with a robust external backend to handle user authentication and data management.

• Real-time Posture Monitoring:
  Data from four sensors connected to an ESP32 is continuously monitored and transmitted to the iOS app, providing real-time feedback on posture.

• Local Data Storage:
  The app stores posture data locally using CoreData, allowing users to track their current trends, monitor improvements, and review posture performance over time.

• Long-term Trend Analysis with Machine Learning:
  An advanced machine learning model analyzes stored posture data from the app's backend, offering long-term posture trends and insights into user progress.

• User Authentication:
  Secure login functionality powered by an external backend. Users can securely log in to the app, ensuring their posture data is personal and protected.

• External Backend Integration:
  The app communicates with a dedicated backend that not only handles machine learning for long-term trend analysis but also manages user data and authentication, ensuring a seamless experience for the end-user.

• Hardware:

  • ESP32 Microcontroller: Utilized for real-time sensor data collection and communication with the iOS app.
  • Four Sensors: Collect posture data to be sent to the mobile app.

• Software:

  • iOS App (Swift): The primary mobile application that displays posture data, trends, and long-term analysis.
  • CoreData: Local data storage to manage user posture data and trends.
  • Machine Learning Model: Analyzes posture trends based on historical data and provides long-term posture feedback.
  • External Backend: Powers authentication and machine learning model integration, ensuring smooth data synchronization and user management.

• The app uses ESP32 to collect real-time data from four sensors placed at various points to detect the user's posture.
• The data is sent to the iOS app where users can view their posture in real-time, along with relevant feedback.

• By utilizing a machine learning model hosted on an external backend, the app can predict and display long-term trends in the user’s posture.
• The machine learning model uses data collected from previous sessions to generate valuable insights and make suggestions for improving posture.

• The app supports a secure login system, ensuring that each user’s posture data remains private.
• Backend authentication verifies the user’s credentials and manages the user data securely, with features like token-based authentication.

• Posture data, including trends and daily measurements, are saved locally using CoreData.
• This allows users to access their most recent posture information and review past data without requiring a constant internet connection.

1. Posture Data Collection: The ESP32 collects data from the sensors and sends it to the iOS app.
2. Local Data Storage: The iOS app stores this data in CoreData to maintain a local record of the user’s posture.
3. Trend Analysis: The app then communicates with an external backend, where a machine learning model analyzes the data and provides long-term insights.
4. Login and Authentication: Users can securely log into the app, ensuring their posture data remains private and accessible only to them.

1. Clone the repository:

2. Open the Posture_Detection-App.xcodeproj in Xcode.

3. Ensure the project is linked with the necessary dependencies (CoreData, external backend, etc.).

4. Connect the ESP32 hardware with the app via Bluetooth or Wi-Fi.

5. Run the app on your iOS device for real-time posture monitoring and data tracking.

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

• ESP32 for the hardware and sensor integration.
• CoreData for local data storage.
• Machine Learning Model for long-term analysis.
• External Backend for user management and data analysis.

This Posture Detection App offers a powerful and efficient way for users to monitor and improve their posture using advanced hardware and software technologies. By combining real-time data collection, long-term trend analysis, secure user authentication, and local storage with CoreData, this app provides an all-in-one solution for posture health and improvement.