Human movement analysis is a key area of research in robotics, biomechanics,
and data science. It encompasses tracking, posture estimation, and movement
synthesis. While numerous methodologies have evolved over time, a systematic
and quantitative evaluation of these approaches using verifiable ground truth
data of three-dimensional human movement is still required to define the
current state of the art. This paper presents seven datasets recorded using
inertial-based motion capture. The datasets contain professional gestures
carried out by industrial operators and skilled craftsmen performed in real
conditions in-situ. The datasets were created with the intention of being used
for research in human motion modeling, analysis, and generation. The protocols
for data collection are described in detail, and a preliminary analysis of the
collected data is provided as a benchmark. The Gesture Operational Model, a
hybrid stochastic-biomechanical approach based on kinematic descriptors, is
utilized to model the dynamics of the experts' movements and create
mathematical representations of their motion trajectories for analysis and
quantifying their body dexterity. The models allowed accurate the generation of
human professional poses and an intuitive description of how body joints
cooperate and change over time through the performance of the task.

这篇论文利用惯性运动捕捉技术记录了工业操作者和熟练工匠在实际场景中进行的专业手势，并使用基于运动学描述符的混合随机 - 生物力学方法 Gesture Operational Model 对专家的运动进行建模和数学表示，从而精确生成人体专业姿态，直观描述身体关节如何协作并随时间变化而变化。