BodyTrack - Backend Server

Overview

This repository contains the backend server of the BodyTrack system - a real-time posture analysis and feedback platform developed as part of a final software engineering project.

The server is responsible for receiving video frames from the mobile client, analyzing body posture using a processing pipeline, managing training sessions, and returning feedback decisions to the client in real time.

System Role

The backend server acts as the central processing unit of the system. Its main responsibilities include:

• Managing active training sessions
• Receiving video frames from the Android client
• Running posture analysis and validation logic
• Tracking historical frame data for stable decision making
• Generating posture feedback and session summaries

The server is designed to be stateful, handling each user session independently while maintaining performance suitable for real-time interaction.

Technologies Used

• Python 3
• Flask - HTTP API and request routing
• Gunicorn - production-grade WSGI server
• Systemd - service management
• Linux (Ubuntu) - deployment environment

Project Structure (Simplified)

Server/
│
├── Server.py                 # Main entry point (Flask app)
├── Management/               # Session and test management
├── Pipeline/                 # Posture analysis pipeline
├── Config/                   # Configuration and parameters
├── Utils/                    # Shared utilities and definitions
└── README.md                 # This file

The internal structure follows a layered design, separating request handling, session management, processing logic, and configuration.

Requirements

• Python 3.9+
• Linux-based environment (recommended)
• Virtual environment support (venv)
• Network access (HTTP)

Installation

Clone the repository:

git clone <repository-url>
cd Server

Create and activate a virtual environment:

python3 -m venv venv
source venv/bin/activate

Install required dependencies:

pip install -r requirements.txt

Running the Server

Development Mode

For local development and testing:

source venv/bin/activate
python Server.py

The server listens on:

• Host: 0.0.0.0
• Port: 8080

This mode is intended for debugging and development only.

Production Mode (Gunicorn)

For production deployment, the server is executed using Gunicorn, which runs the Flask application as a managed process:

gunicorn Server:app --bind 0.0.0.0:8080

In production, the server is typically managed through a system service to ensure availability and automatic restarts.

Service Management

In the deployed environment, the backend server runs as a system-managed service named: bodytrack

This service controls the Gunicorn process and allows standard lifecycle operations.

Common Commands

• sudo systemctl start bodytrack -sudo systemctl stop bodytrack
• sudo systemctl restart bodytrack
• sudo systemctl status bodytrack

These commands allow starting, stopping, and monitoring the server without manual process management.

Logs and Monitoring

Runtime logs are automatically collected by the operating system’s logging system. To view live server logs: sudo journalctl -u bodytrack -f

Logs include:

• Server startup information
• Runtime warnings
• Error messages
• Session-level issues

Log inspection is the primary method for diagnosing unexpected behavior.

Configuration

System behavior is controlled through centralized configuration files and parameters. Configuration allows:

• Adjusting thresholds
• Modifying timing values
• Tuning posture validation logic

Changes to configuration do not require modifications to the core processing pipeline, as long as the parameter structure is preserved.

Notes on Security and Networking

The server currently communicates over HTTP using a public IP address. Future improvements may include:

• HTTPS support
• Domain-based access
• Secure certificate configuration

These changes do not affect the internal processing pipeline.

Intended Audience

This repository is intended for:

• Developers maintaining or extending the backend
• Academic reviewers evaluating system implementation
• Future contributors to the BodyTrack project

End users interact with the system through the mobile client, not directly with this server.

Academic Context

This backend server was developed as part of a final capstone project in software engineering, with emphasis on:

• Real-time processing
• Clean architecture
• Maintainability

Practical deployment considerations

For detailed design decisions, architecture diagrams, and evaluation results, refer to the accompanying project documentation.