@misc{Hudhud-Mughrabi25-GRCEA-Filter,
  title = {Error-State Extended Kalman Filter Sensor Fusion for Tracking Collaborating Humans},
  abstract = {How teams collaborate to perform complex tasks , from team sports to surgical procedures, has previously been investigated via multimodal sensing and analysis. Ultra-wideband (UWB) positioning systems are highly mobile and can be used to track collaborating team members even in cramped environments.However, the sampling rate of UWB systems is inversely proportional to the number of people tracked, and their accuracy is hindered by electromagnetic occlusion. To improve position and orientation estimation during team collaborative studies, we propose to fuse UWB positioning with a wearable inertial measurement unit (IMU) by applying an error-state extended Kalman filter (ES-EKF). This filter offers faster and more consistent estimation and remains functional even in the absence of UWB input.Single-human and multi-human sessions were recorded and filtered for evaluation against ground truth from optical motion capture. By integrating IMU readings, the ES-EKF increases the sampling rate from 0.5–20 Hz to 100 Hz. Even by correcting only planar position in the room, the ES-EKF yields improved results over UWB in four out of six DOF: lateral and longitudinal position and yaw and pitch orientation.},
  howpublished = {Extended abstract (3 pages) presented at the German Robotics Conference (GRC)},
  address = {Nuremberg, Germany},
  month = mar,
  year = {2025},
  slug = {hudhud-mughrabi25-grcea-filter},
  author = {Hudhud Mughrabi, Moaaz and Allemang--Trivalle, Arnaud and Kuchenbecker, Katherine J.},
  month_numeric = {3}
}