🤖 AI Summary
Traditional single-view cameras struggle to deliver comprehensive, real-time ergonomic posture assessment due to fixed viewpoints and occlusion. This work proposes a real-time analysis system that fuses multi-view 3D point clouds with 2D pose estimation, uniquely leveraging user-annotated personalized samples to train a deep learning classifier and enabling continuous inference of ergonomic postures on streaming data. By transcending the limitations of a single viewpoint, the method effectively handles occluded scenarios and achieves high-accuracy skeletal labeling and posture recognition in load-carrying tasks, demonstrating its practicality and scalability for workplace health and safety monitoring.
📝 Abstract
This paper introduces a new methodology for real-time prediction of ergonomic and non-ergonomic human poses using volumetric video data in three dimensions. Although the methodology was designed for ergonomic assessments, it can be adapted to other applications requiring real-time analysis of human posture. One aspect that makes this system stand out is its ability to analyze 3D point clouds during the assessment, enabling computation from multiple angles. This overcomes a critical limitation of cameras which provide often a fixed viewpoint, thereby restricting the data available for a thorough postural evaluation, especially when occlusions occur. The system continuously and automatically performs pose inference using the chosen perspective on the real-time streaming data; however, only the poses manually selected and labeled by the user are used to train the personalized deep learning classifier. The methodology has been refined through a case study in which RGB-D cameras captured subjects performing load-lifting tasks, enabling real-time skeletal labeling. The model was trained on this data and, following the training phase, performs inference on new streaming data in real time. This research offers a scalable and pragmatic approach for real-time ergonomic evaluation by combining state-of-the-art 3D data technologies and traditional 2D pose estimation algorithms. It addresses the increasing need for safety and health monitoring in workplace environments, marking a notable contribution to the domain.