Enhancing Spinal Health: A Real-time IoT-based Posture Detection System

This study addresses the health implications of poor posture due to modern daily routines and workplaces, offering an innovative, low-cost solution based on computer vision technology. While existing posture detection methods predominantly rely on hardware-based solutions involving specialized devices, this research leverages the YOLO object detector, achieving a remarkable 96% accuracy rate in detecting proper posture. The data collection process is streamlined through LED feedback, simplifying ground truth labeling. Furthermore, this model is seamlessly integrated into a user-friendly mobile application, providing users with comprehensive posture analysis on a daily and weekly basis. These insights empower individuals to make informed decisions to enhance their overall well-being. By offering a practical and accessible approach to posture correction, this research has the potential to revolutionize the way individuals improve their health and posture.