DIGITAL TWIN-BASED SEDENTARY RISK INTERVENTION SYSTEM USING ESP32

Abstract

The proposed system titled Digital Twin-Based Sedentary Risk Intervention System is designed to continuously monitor and analyze sedentary behavior using an IoT-enabled architecture to promote healthier lifestyle habits and reduce long-term health risks associated with prolonged inactivity. The system integrates wearable or environmental IoT sensors to capture real-time physical activity and inactivity patterns of a user. These sensor readings are processed and transmitted using an ESP32 microcontroller, which acts as the central communication unit, sending real-time data over WiFi to a server for further analysis. A digital twin model is developed using a Python Flask-based web framework, which virtually replicates the user’s physical activity profile and updates dynamically based on incoming sensor data. The collected data is systematically stored and managed in a MySQL database to ensure efficient retrieval, historical tracking, and analytical processing. A sedentary risk assessment module computes risk levels using a predefined sedentary risk formula that considers parameters such as inactivity duration, frequency of movement breaks, and cumulative daily sedentary time. Based on the computed risk score, users are classified into three categories: Normal, Medium Risk, and High Risk. To ensure timely behavioral correction, the system implements Just-In-Time Adaptive Intervention (JITAI) logic, which triggers personalized interventions based on the user’s current risk state and behavioral patterns. These interventions are delivered through multiple output channels including OLED display notifications, LED indicators, buzzer alerts, and optional voice-based feedback to maximize user awareness and responsiveness. In addition, a gamification module is incorporated to improve user engagement by assigning health scores, progress tracking, and motivational feedback, encouraging consistent physical activity and reduced sedentary time. The integration of digital twin technology with real-time IoT sensing, cloud-based analytics, adaptive intervention strategies, and gamified engagement creates a comprehensive and intelligent health monitoring ecosystem. This system not only provides real-time awareness of sedentary risks but also actively influences user behavior through continuous feedback and adaptive interventions, making it a scalable and effective solution for preventive healthcare and lifestyle management.