Abstract
Reliable IMU-UWB motion estimation depends on upstream analytical choices, not only on predictor design. We frame wearable motion estimation as a reference-aware problem involving UWB-derived reference construction, IMU sensor selection, and temporal context. The proposed workflow reconstructs and cleans 2D UWB trajectories, aligns them with IMU streams, and creates learning-ready inputs for supervised speed estimation. On 619 indoor sessions from 73 participants, these choices affected accuracy and robustness. The best subject-disjoint configuration achieved RMSE 0.401 and Pearson correlation 0.791; excluding magnetometer channels improved robustness under unseen conditions.
Paper Type
Poster
DOI
10.62036/ISD.2026.92
Reference-Aware Design Choices for IMU-UWB Wearable Motion Analytics
Reliable IMU-UWB motion estimation depends on upstream analytical choices, not only on predictor design. We frame wearable motion estimation as a reference-aware problem involving UWB-derived reference construction, IMU sensor selection, and temporal context. The proposed workflow reconstructs and cleans 2D UWB trajectories, aligns them with IMU streams, and creates learning-ready inputs for supervised speed estimation. On 619 indoor sessions from 73 participants, these choices affected accuracy and robustness. The best subject-disjoint configuration achieved RMSE 0.401 and Pearson correlation 0.791; excluding magnetometer channels improved robustness under unseen conditions.
Recommended Citation
Kuczyński, T., Świątek, J., Piłka, T. & Górecki, T.(2026). Reference-Aware Design Choices for IMU-UWB Wearable Motion Analytics. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.92