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.

翻译：本文提出了一种利用三维体积视频数据实时预测人体工效学与非工效学姿态的新方法。尽管该方法专为工效学评估设计，但可推广至其他需要实时人体姿势分析的场景。该系统的一大特色在于评估过程中能对三维点云进行分析，从而实现多角度计算。这突破了传统摄像头因固定视角导致数据受限的瓶颈，尤其在遮挡发生时，仍能提供充分的姿势评估数据。系统可基于用户选定的视角，持续自动对实时流数据进行姿态推断；但只有用户手动选取并标注的姿态才用于训练个性化深度学习分类器。通过一项案例研究对方法进行了优化——实验中采用RGB-D摄像头捕捉受试者执行负重提举任务的过程，实现实时骨骼标注。模型基于该数据训练后，可在训练阶段结束后对新流数据进行实时推断。本研究通过融合前沿三维数据技术与传统二维姿态估计算法，为实时工效学评估提供了可扩展且实用的方案，有效满足工作场所日益增长的安全与健康监测需求，为该领域作出了重要贡献。