@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},
  slug = {rokhmanova22-nacobea-predicting},
  author = {Rokhmanova, Nataliya and Kuchenbecker, Katherine J. and Shull, Peter B. and Ferber, Reed and Halilaj, Eni},
  month_numeric = {8}
}