Estimating Movement Direction From Body Orientation Using Dual Ultra-Wideband Sensors
Bibliographic record
Abstract
Accurate short-term prediction of human movement is vital for safety-critical and context-aware applications in dynamic environments. While conventional trajectory prediction methods depend on historical motion data, they often fall short in anticipating sudden directional changes. This study investigates whether body orientation, estimated using a dual Ultra-Wideband (UWB) sensor configuration, can serve as a reliable predictor of near-future movement direction. A wearable device with two shoulder-mounted UWB tags was used to collect position and orientation data during controlled walking experiments. Eight participants walked freely within a controlled lab environment while data were recorded. Circular cross-correlation was applied to analyze the temporal relationship between body orientation and subsequent movement direction. Results revealed a strong and statistically significant correlation across all participants (mean correlation = 0.7688, p < 0.001), with an average optimal lead time of 200 ms. The relationship remained robust using a standardized 200 ms lag (mean correlation = 0.7453). These findings demonstrate that UWB-derived body orientation can effectively predict short-term movement direction, supporting the use of UWB sensing not only for localization but also as a foundation for predictive on-body systems that enhance real-time safety and mobility monitoring.
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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 | 0.000 |
| 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".