Adaptive 2D Posture Analysis

Poor posture is a leading cause of musculoskeletal disorders and reduced biomechanical efficiency in various populations, including office workers, athletes, and individuals undergoing physiotherapy. This research presents an adaptive 2D posture correction system based on real-time human pose estimation and dynamic feedback mechanisms. Utilizing lightweight 2D pose estimation frameworks such as MediaPipe and OpenPose, the system captures key body landmarks and calculates posture deviations based on predefined ergonomic baselines. A novel keypoint augmentation strategy introduces a synthetic "neck" keypoint between the nose and shoulder midpoints, enhancing posture evaluation accuracy. Furthermore, adaptive feedback through visual and auditory cues enables immediate correction in environments such as fitness, yoga, and rehabilitation. The model demonstrates robust performance under diverse lighting conditions, occlusions, and clothing variances. Results indicate a 92.4% improvement in posture classification accuracy using the proposed keypoint augmentation. This work paves the way for intelligent, accessible, and real-time posture improvement applications.