A Feasibility Study of an Intelligent Wearable System for Real-Time Posture Monitoring During Strength Training
Year: 2026
Type: preprint
Abstract: 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 during Romanian Deadlifts. 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). The system achieved 89.2% classification accuracy on a held-out test set across three posture categories (proper, rounded, and arched), with cross-validated performance reaching 94.5%. Angular drift remained minimal (1.8°–7.1°), and battery life supported up to 4 hours of operation. Usability testing with fourteen participants (aged 18–25) indicated high acceptance, with 90% finding the app intuitive. By enabling immediate feedback and encouraging posture correction, this interdisciplinary system offers a tool to support posture awareness, with potential implications for injury risk reduction pending further validation.
Source: Preprints.org
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7212435781