Biofeedback Gait Retraining under Real-World Running Conditions
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».