Posture Compliance Monitor for Retinal Detachment Rehabilitation

A comprehensive system for monitoring patient posture compliance during retinal detachment rehabilitation, consisting of backend API, web interface, mobile application, and data analysis components.

📖 Research Publication

This system is the subject of peer-reviewed research published in:

Posture Compliance Monitoring System for Retinal Detachment Rehabilitation
DOI: 10.48130/vns-0025-0014
Published in Visual Neuroscience, 2025

Citation

@article{posture_compliance_2025,
  title={Posture Compliance Monitoring System for Retinal Detachment Rehabilitation},
  author={SheathedSharp(Zixian Zhu)},
  journal={Visual Neuroscience},
  year={2025},
  doi={10.48130/vns-0025-0014},
  url={https://www.maxapress.com/article/doi/10.48130/vns-0025-0014}
}

🎯 System Overview

This research project implements a multi-platform posture monitoring system with the following components:

• 🔧 Backend (Node.js/Express) - REST API server with MySQL database
• 🖥️ Web Frontend (Vue.js 2) - Healthcare provider interface with 3D visualization
• 📱 Mini-program (WeChat Mini Program) - Patient mobile application with Bluetooth connectivity
• 🐍 Data Analysis (Python) - Machine learning models and compliance analysis

System Architecture Overview

Figure 1: Complete system architecture showing multi-platform integration

Hardware Components and Sensor Integration

Figure 2: Sensor hardware and positioning device specifications

🏗️ Architecture

Backend API

• RESTful API endpoints for doctor and patient operations
• MySQL database for patient records and posture data
• WebSocket support for real-time communication
• CORS configured for cross-platform access

Web Interface

• Vue.js 2 with Vue Router and Vuex
• Three.js integration for 3D eyeball simulation
• Element UI components for interface
• Real-time posture angle visualization

WeChat Mini Program

• Bluetooth device integration
• Patient data upload and progress tracking
• User authentication and management
• Core posture degree processing modules

Python Data Analysis

• Machine learning models (exponential, quadratic)
• Patient compliance analysis and visualization
• Position data processing and categorization
• Day/night analysis separation

💻 Technology Stack

Component	Technologies	Purpose
Backend	Node.js, Express, MySQL, WebSocket	API services & data management
Web Frontend	Vue.js 2, Vuex, Vue Router, Three.js	Healthcare provider interface
Mobile App	WeChat Mini Program, Bluetooth API	Patient data collection
Data Analysis	Python, Pandas, NumPy, Matplotlib	ML models & statistical analysis
Database	MySQL	Patient records & posture data
Visualization	Three.js, ECharts, D3.js	3D eyeball simulation & charts

User Interface Design

Figure 3: Web interface and mobile application user interface design

🌟 Key Features

🏥 For Healthcare Providers

• Real-time Monitoring Dashboard: Track patient compliance in real-time
• 3D Eyeball Visualization: Interactive 3D model showing patient positioning
• Compliance Analytics: Detailed reports on patient adherence patterns
• Alert System: Notifications for non-compliance events
• Historical Data Analysis: Trend analysis and progress tracking

📱 For Patients

• Mobile App Interface: Easy-to-use WeChat mini-program
• Bluetooth Device Integration: Seamless sensor connectivity
• Progress Tracking: Visual feedback on compliance performance
• Educational Content: Guidance on proper positioning techniques
• Real-time Feedback: Immediate alerts for position corrections

🔬 For Researchers

• Data Export: CSV/Excel export for further analysis
• Machine Learning Models: Pre-trained compliance prediction models
• Statistical Tools: Advanced analytics for research purposes
• Anonymized Datasets: Privacy-compliant data for studies

Setup and Installation

Prerequisites

• Node.js 14+ and npm
• Python 3.7+ with pip
• MySQL 5.7+
• WeChat Developer Tools (for mini-program)

Environment Configuration

1. Copy the environment configuration:

   cp .env.example .env

2. Configure your database credentials and other settings in .env

Backend Setup

cd backend
npm install
npm run test  # Development server with nodemon

Web Frontend Setup

cd web
npm install
npm run serve  # Development server
npm run build  # Production build

Mini-program Setup

cd mini-program
npm install
# Use WeChat Developer Tools for compilation and testing

Python Analysis Setup

cd script
python -m pip install -r requirements.txt
# Run analysis scripts as needed
python get_position_Axyz_revision_LR.py

Data Processing Pipeline

The system processes patient posture data through several stages:

1. Data Collection: Bluetooth sensors collect angle measurements
2. Position Analysis: Raw data processed to determine posture positions (FD, SS, RL)
3. Compliance Calculation: Machine learning models analyze adherence patterns
4. Visualization: Generate compliance reports and 3D visualizations

File Structure

• /backend/ - Node.js API server
• /web/ - Vue.js web application
• /mini-program/ - WeChat mini-program
• /script/ - Python data analysis tools
• /script/model/ - Machine learning models
• /script/utils/ - Analysis utilities
• /script/data/position_data/ - Processed position data (CSV files)
• /docs/images/ - Research paper figures and documentation images

🔬 Research Context

This system was developed for clinical research on posture compliance during retinal detachment rehabilitation. The research addresses the critical need for accurate monitoring of patient positioning during post-operative recovery, which is essential for successful retinal reattachment.

Key Research Findings

• 📊 Compliance Monitoring: Real-time tracking of patient posture with high accuracy
• 🤖 Machine Learning: Advanced models for predicting compliance patterns
• 📱 Patient Engagement: Mobile interface improves patient adherence
• 🏥 Clinical Integration: Seamless workflow for healthcare providers

Research Results

Data Collection and Processing Pipeline

Figure 4: Data collection workflow and processing pipeline

Patient Positioning Analysis

Figure 5: Analysis of different patient positioning patterns and compliance metrics

Clinical Validation Results

Figure 6: Clinical study results showing system effectiveness and patient outcomes

Machine Learning Model Performance

Figure 7: Machine learning model performance evaluation and compliance prediction accuracy

📈 Clinical Impact

The system has demonstrated significant improvements in:

• Patient compliance rates with posturing requirements
• Clinical outcomes in retinal detachment cases
• Healthcare provider monitoring efficiency
• Patient education and engagement

🧪 Research Methodology

The study employed:

• Multi-platform data collection across web, mobile, and sensor devices
• Machine learning algorithms for compliance pattern analysis
• Clinical validation with anonymized patient data
• Statistical analysis of compliance outcomes

📄 License

This project is released under academic license for research and educational purposes.

Academic Use

• ✅ Research and educational use permitted
• ✅ Citation required when using in academic work
• ✅ Modification and distribution allowed with attribution

Commercial Use

• ❌ Commercial use requires separate license agreement
• 📧 Contact authors for commercial licensing inquiries

🔐 Data Privacy & Ethics

All patient data files and personal information have been removed or anonymized to protect patient privacy in compliance with:

• HIPAA (Health Insurance Portability and Accountability Act)
• Medical Research Ethics guidelines
• Data Protection Regulations

The system is designed to work with anonymized patient identifiers and maintains the highest standards of data privacy.

📞 Contact & Support

For questions about this research or system implementation:

• 📝 Paper: https://www.maxapress.com/article/doi/10.48130/vns-0025-0014
• 🐛 Issues: Use GitHub Issues for technical problems

🙏 Acknowledgments

We thank all patients, healthcare providers, and research collaborators who contributed to this study. Special recognition to the clinical teams who validated the system in real-world healthcare settings.