A Wearable System for Real-Time Posture Monitoring and Feedback during Strength Training

Lower back pain (LBP) affects an estimated 75–80% of individuals worldwide, with poor posture during strength training identified as a significant contributing factor. This study presents an intelligent, low-cost wearable system for real-time lumbar posture monitoring, demonstrated using Romanian Deadlifts as a case study. The system combines an MPU-9250 inertial measurement unit (IMU), an ESP32 microcontroller, a cloud-deployed Random Forest model (PostureProML), and a Flutter-based mobile application (PostureProne). It achieved a 94.5% classification accuracy across three posture categories; proper, rounded, and arched with minimal angular drift (1.8°–7.1°) and a 4-hour operational battery life. Usability testing with ten participants (aged 18–25) indicated high acceptance, with 90% finding the app intuitive. By enabling immediate feedback and encouraging posture correction, this interdisciplinary solution offers a practical pathway to reducing gym-related injuries.