Real-time estimation of knee adduction moment for gait retraining in patients with knee osteoarthritis

Chao Wang, Peter P. K. Chan, Ben M. F. Lam, Sizhong Wang, Janet H. Zhang, Zoe Y. S. Chan, Rosa H. M. Chan, Kevin K. W. Ho, Roy Cheung

Research output: Contribution to journalArticlepeer-review

Abstract

Previous clinical studies have reported that gait retraining is an effective non-invasive intervention for patients with medial compartment knee osteoarthritis. These gait retraining programs often target a reduction in the knee adduction moment (KAM), which is a commonly used surrogate marker to estimate the loading in the medial compartment of the tibiofemoral joint. However, conventional evaluation of KAM requires complex and costly equipment for motion capture and force measurement. Gait retraining programs, therefore, are usually confined to a laboratory environment. In this study, machine learning techniques were applied to estimate KAM during walking with data collected from two low-cost wearable sensors. When compared to the traditional laboratory-based measurement, our mobile solution using artificial neural network (ANN) and XGBoost achieved an excellent agreement with R2 of 0.956 and 0.947 respectively. With the implementation of a real-time audio feedback system, the present algorithm may provide a viable solution for gait retraining outside laboratory. Clinical treatment strategies can be developed using the continuous feedback provided by our system.
Original languageEnglish
Pages (from-to)888-894
Number of pages7
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume28
Issue number4
DOIs
Publication statusPublished - 2020

Keywords

  • foot
  • gait
  • walking

Fingerprint

Dive into the research topics of 'Real-time estimation of knee adduction moment for gait retraining in patients with knee osteoarthritis'. Together they form a unique fingerprint.

Cite this