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 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".