Personalized gait retraining has shown promise as a conservative intervention for slowing knee osteoarthritis (OA) progression [1,2]. Changing the foot progression angle is an easy-to-learn gait modification that often reduces the knee adduction moment (KAM), a correlate of medial joint loading. Deployment to clinics is challenging, however, because customizing gait retraining still requires gait lab instrumentation. Innovation in wearable sensing and vision-based motion tracking could bring lab-level accuracy to the clinic, but current markerless motion-tracking algorithms cannot accurately assess if gait retraining will reduce someone's KAM by a clinically meaningful margin. To assist clinicians in determining if a patient will benefit from toe-in gait, we built a predictive model to estimate KAM reduction using only measurements that can be easily obtained in the clinic.
| Author(s): | Nataliya Rokhmanova and Katherine J. Kuchenbecker and Peter B. Shull and Reed Ferber and Eni Halilaj |
| Year: | 2022 |
| Month: | August |
| Project(s): | |
| BibTeX Type: | Miscellaneous (misc) |
| Address: | Ottawa, Canada |
| Electronic Archiving: | grant_archive |
| How Published: | Extended abstract presented at North American Congress of Biomechanics (NACOB) |
| State: | Published |
BibTeX
@misc{Rokhmanova22-NACOBEA-Predicting,
title = {Predicting Knee Adduction Moment Response to Gait Retraining},
abstract = {Personalized gait retraining has shown promise as a conservative intervention for slowing knee osteoarthritis (OA) progression [1,2]. Changing the foot progression angle is an easy-to-learn gait modification that often reduces the knee adduction moment (KAM), a correlate of medial joint loading. Deployment to clinics is challenging, however, because customizing gait retraining still requires gait lab instrumentation. Innovation in wearable sensing and vision-based motion tracking could bring lab-level accuracy to the clinic, but current markerless motion-tracking algorithms cannot accurately assess if gait retraining will reduce someone's KAM by a clinically meaningful margin. To assist clinicians in determining if a patient will benefit from toe-in gait, we built a predictive model to estimate KAM reduction using only measurements that can be easily obtained in the clinic.},
howpublished = {Extended abstract presented at North American Congress of Biomechanics (NACOB)},
address = {Ottawa, Canada},
month = aug,
year = {2022},
author = {Rokhmanova, Nataliya and Kuchenbecker, Katherine J. and Shull, Peter B. and Ferber, Reed and Halilaj, Eni},
month_numeric = {8}
}