View-Aware Pose Analysis: A Robust Pipeline for Multi-Person Joint Injury Prediction from Single Camera

Abstract

This paper presents a novel, accessible pipeline for the prediction and prevention of motion-related joint injuries in multiple individuals. Current methodologies for biomechanical analysis often rely on complex, restrictive setups such as multi-camera systems, wearable sensors, or markers, limiting their applicability in everyday environments. To overcome these limitations, we propose a comprehensive solution that utilizes only single-camera 2D images. Our pipeline comprises four distinct stages: (1) extraction of 2D human pose keypoints for multiple persons using a pretrained Human Pose Estimation model; (2) a novel ensemble learning model for person-view classification—distinguishing between front, back, and side perspectives—which is critical for accurate subsequent analysis; (3) a view-specific module that calculates body-segment angles, robustly handling movement pairs (e.g., flexion–extension) and mirrored joints; and (4) a pose assessment module that evaluates calculated angles against established biomechanical Range of Motion (ROM) standards to detect potentially injurious movements. Evaluated on a custom dataset of high-risk poses and diverse images, the end-to-end pipeline demonstrated an 87% success rate in identifying dangerous postures. The view classification stage, a key contribution of this work, achieved a 90% overall accuracy. The system delivers individualized, joint-specific feedback, offering a scalable and deployable solution for enhancing human health and safety in various settings, from home environments to workplaces, without the need for specialized equipment.