Biofeedback Gait Retraining under Real-World Running Conditions
Bibliographic record
Abstract
Gait retraining has been used as an intervention to mitigate the risk of injuries among distance runners. Lab-based gait retraining has demonstrated promising results in changing biomechanical parameters associated with injuries. However, there was limited evidence that supports the transfer of training effect to conditions that resemble real-world running. The main objective of this thesis was to optimize the training protocol for training under real-world conditions and five studies were conducted to address three specific aims: 1) identify the limitations of conventional training protocols, 2) assess habitual gait adaptations in real-world running, and 3) establish the technical specifications for gait assessment using wearables. Regarding the first specific aim, two studies were conducted to examine the transfer of training effect to untrained conditions, including overground and slopes. Results of both studies suggested incomplete transfer, hence, gait retraining along overground running routes with slopes was recommended. For the second specific aim, our third study examined the natural biomechanical adaptations along slopes. Differences in speed and cadence were observed between various slope conditions from real-world training data. As these changes could potentially affect training, an adaptive feedback model was recommended. Tibial acceleration can be measured using wearables and is a common outcome measure for gait retraining. The fourth and fifth studies addressed the third specific aim and presented the technical considerations required for accurate and reliable tibial acceleration measurements under conditions that resemble real-world running. Based on the findings, it was recommended to use wearables with an accelerometer operating range wider than ±16-g and to measure a minimum of 100 consecutive strides during each condition. Finally, a gait retraining protocol for training under real-world conditions was proposed based on the findings of the five studies and was evaluated. The evaluation study has demonstrated the feasibility of using adaptive feedback in real-world training using wearables. Reduction of tibial acceleration was observed after the training in various slope conditions. Overall, the findings of this thesis provided insights for further optimization of the gait retraining protocol and future development of feedback systems suitable for use under real-world conditions.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".