Motion Capture Benchmark of Real Industrial Tasks and Traditional Crafts for Human Movement Analysis
Résumé
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 analyzing and quantifying their body
dexterity. The models allowed accurate generation of human professional poses and an intuitive description
of how body joints cooperate and change over time through the performance of the task.
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