Automatic estimation of Hand Activity Level from upper-limb trajectories: a probabilistic regression framework
提出一种概率回归框架，利用视频中的上肢轨迹自动估计手部活动水平（HAL）并给出置信度，为重复性手工作业的肌肉骨骼损伤风险提供客观、可靠的评估。
Accurate measurement of Hand Activity Level (HAL) is crucial for evaluating musculoskeletal injury risk in repetitive hand-intensive work. Manual HAL assessments are often subjective and impractical for large-scale or continuous monitoring. This study presents a probabilistic regression framework that leverages video-based upper-limb pose trajectories to automatically estimate HAL scores while providing associated confidence measures. By enabling ergonomic risk assessment with quantified uncertainty, the proposed method delivers objective and reliable HAL predictions. Experimental results demonstrate strong in-domain performance (Root Mean Square Error [RMSE] = 0.24, Mean Absolute Error [MAE] = 0.17) and robust cross-domain generalisation (RMSE = 0.74, MAE = 0.54), highlighting both the accuracy and transferability of the framework.