Real-Time Estimation of Knee Adduction Moment for Gait Retraining in Patients with Knee Osteoarthritis

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

8 Scopus Citations
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Author(s)

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

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number9025245
Pages (from-to)888-894
Number of pages7
Journal / PublicationIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume28
Issue number4
Online published5 Mar 2020
Publication statusPublished - Apr 2020

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.

Research Area(s)

  • Foot progression angle, machine learning, real-time biofeedback

Citation Format(s)

Real-Time Estimation of Knee Adduction Moment for Gait Retraining in Patients with Knee Osteoarthritis. / Wang, Chao; Chan, Peter P. K.; Lam, Ben M. F.; Wang, Sizhong; Zhang, Janet H.; Chan, Zoe Y. S.; Chan, Rosa H. M.; Ho, Kevin K. W.; Cheung, Roy T. H.

In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 28, No. 4, 9025245, 04.2020, p. 888-894.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review