Gait retraining: out of the lab and onto the streets with the benefit of wearables
Bibliographic record
Abstract
Movement retraining can correct faulty movement patterns.1 However, as with any treatment, retraining needs to be activity specific—there are various types of ‘retraining’ and the treatment needs to match the movement fault.2 As experts at analysis and rehabilitation of movement, the concept of gait retraining fits well into a sport physiotherapist’s tool kit. The advent of readily accessible high-speed motion capture technology to assess and provide feedback on running patterns allows practitioners to incorporate gait retraining in their clinics. Furthermore, wearable technology makes it possible to measure many metrics ‘in the field’ that were previously only quantifiable in the lab. The purpose of this editorial is to discuss the potential of wearable technology to monitor and give feedback of gait outside of a lab and clinic setting. Traditionally, gait retraining using real-time biofeedback has been conducted in specialised lab settings with variable degrees of success depending on the targeted outcomes and the form of feedback.3 Sport physiotherapists have primarily applied it to treat injured runners, for instance, those with patellofemoral pain.1 4 …
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".