Justus Eilers, Pawel Glöckner, Lisa Ihde, Mohammed Kamal, Justin Trautmann
Learning new movements individually without professional assistance can be difficult. Therefore, a system was implemented that uses inertial measurement units (IMUs) and diﬀerent visual pose estimation techniques in order to generate raw IMU accelerations from computer vision and 3D trajectories from IMU data respectively. Additionally, the system allows to compare IMU data and poses in the domains of acceleration as well as position, which enables a multi-level comparison and generates deep insights into the performed movements. Finally, a dashboard was implemented in order to visualize the different data sources and calculate insights about Key Performance Indicators interactively.