Academic Journal
JointTracker: Real-time inertial kinematic chain tracking with joint position estimation [version 2; peer review: 1 approved, 2 approved with reservations]
| العنوان: | JointTracker: Real-time inertial kinematic chain tracking with joint position estimation [version 2; peer review: 1 approved, 2 approved with reservations] |
|---|---|
| المؤلفون: | Michael Lorenz, Didier Stricker, Markus Miezal, Gabriele Bleser-Taetz, Bertram Taetz |
| المصدر: | Open Research Europe, Vol 4 (2025) |
| بيانات النشر: | F1000 Research Ltd, 2025. |
| سنة النشر: | 2025 |
| المجموعة: | LCC:Science LCC:Social Sciences |
| مصطلحات موضوعية: | inertial motion capture, 3D kinematics, 3D human pose estimation, online joint position estimation, calibration-free, recursive state estimation, eng, Science, Social Sciences |
| الوصف: | In-field motion capture is drawing increasing attention due to the multitude of application areas, in particular for human motion capture (HMC). Plenty of research is currently invested in camera-based markerless HMC, however, with the inherent drawbacks of limited field of view and occlusions. In contrast, inertial motion capture does not suffer from occlusions, thus being a promising approach for capturing motion outside the laboratory. However, one major challenge of such methods is the necessity of spatial registration. Typically, during a predefined calibration sequence, the orientation and location of each inertial sensor are registered with respect to an underlying skeleton model. This work contributes to calibration-free inertial motion capture, as it proposes a recursive estimator for the simultaneous online estimation of all sensor poses and joint positions of a kinematic chain model like the human skeleton. The full derivation from an optimization objective is provided. The approach can directly be applied to a synchronized data stream from a body-mounted inertial sensor network. Successful evaluations are demonstrated on noisy simulated data from a three-link chain, real lower-body walking data from 25 young, healthy persons, and walking data captured from a humanoid robot. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2732-5121 |
| Relation: | https://open-research-europe.ec.europa.eu/articles/4-33/v2; https://doaj.org/toc/2732-5121 |
| DOI: | 10.12688/openreseurope.16939.2 |
| URL الوصول: | https://doaj.org/article/8a83ee11e8c740d79d34327e4296ffc1 |
| رقم الانضمام: | edsdoj.8a83ee11e8c740d79d34327e4296ffc1 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 27325121 |
|---|---|
| DOI: | 10.12688/openreseurope.16939.2 |