Neural networks can accurately identify individual runners from their foot kinematics, but fail to predict their running performance
Bibliographic record
Abstract
Athletes and coaches may seek to improve running performance through adjustments to running form. Running form refers to the biomechanical characteristics of a runner's movement, and can distinguish individual runners as well as groups of runners, such as long-distance and short-distance runners. Yet, in long-distance running it is still unclear whether certain running forms lead to better performance. In this study, we used a neural network to test the extent to which individual running forms, measured from foot kinematics, exist within long-distance runners and whether running forms can predict performance. To accomplish this goal, 119 participants ran on a treadmill at three different speeds and overground at a self-selected sub- maximal speed while we collected data from insole-embedded Inertial Measurement Units (IMUs) mounted in both shoes. Participants reported their personal best 10 km run times. We used these data to train the neural network to identify individual runners from their running data. Then, we trained the same neural network architecture to predict the runners' performance. With enough data, the neural network was successful in identifying individual runners, but was comparable to a random coin flip (57 % accuracy) in predicting whether an individual runner is slow or fast. We interpret the success of the model to identify runners, but the subsequent failure of the same model to predict running performance as evidence that individual running form measured from foot kinematics contains insufficient information about a runner's performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".