Determining Speed and Stride Length using an Ultrawide Bandwidth Local Positioning System
Bibliographic record
Abstract
There are many modalities that can profile speed and stride length for runners. One such modality includes using wearable technologies. An example of a wearable technology includes a global positioning system-based wearable. However, due to its limitations, an alternative may include a local positioning system-based wearable operating in the ultrawide bandwidth. Considering that a local positioning system is not good at determining gait events such as heel and step count, applying sensor fusion with an inertial measurement unit may be beneficial. Therefore, the purpose of the dissertation was to compare speed and stride length determined from an ultrawide bandwidth local positioning system equipped with an inertial measurement unit to a criterion standard (i.e. the “gold standard”) such as video motion capture and timing gates. The data suggest that the local positioning system used in the project may not be a valid tool without further processing. Using machine learning algorithms, pertinent features from a gait cycle that can better extract speed and stride length were explored. More specifically, using a stepwise linear regression model first and then using a feedforward neural network proved to be quite successful in estimating stride length. Chapter 1 provides an introduction to the project, Chapter 2 provides a review of relevant literature, Chapter 3 provides an insight into the materials and methods used, Chapter 4 shows the results obtained from the methods described earlier, Chapter 5 is a discussion of the results obtained and Chapter 6 concludes with suggestions regarding next steps that should be taken.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".