Periodic Extended Kalman Filter to Estimate Rowing Motion Indoors Using a Wearable Ultra-Wideband Ranging Positioning System
Bibliographic record
Abstract
Rowing depends on weather conditions and athletes rely heavily on dryland training, specifically, on rowing machines. Past research implemented methods using inertial measuring systems to study the rower’s biomechanics and oar dynamics, mostly for outdoor rowing. Tracking the oar dynamics is of great significance to coaches and athletes because it is correlated to the rower’s technical skills and boat speed (on-water). Ultra- wideband (UWB) ranging as an indoor localization method is one of the most accurate and precise among available radio frequency technologies. UWB has been used in game and individual sports to track athletes and their performance during training and competition. This paper proposes an UWB indoor positioning system to track the periodic motion of a rowing machine’s handle at varying frequencies. Furthermore, it introduces a periodic extended Kalman filter to estimate the motion as a non-sinusoidal wave. The proposed UWB positioning system is based on the double sided two-way ranging time of arrival between two anchors and a single tag. The ranging measurements are processed using three mathematical models, trilateration, extended Kalman filter with constant velocity, and a periodic extended Kalman filter. The results are compared to a reference trajectory obtained from a Vicon 8 camera motion capture system. The periodic Kalman filter outperforms the other two models, however, the accuracy and precision of the predicted position of the handle depends on the accuracy of the ranging measurements. The proposed indoor rowing positioning system is able to effectively track rowing motion using UWB ranging and a periodic extended Kalman filter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".