Gait assessment in unicompartmental knee arthroplasty patients: Principal component modelling of gait waveforms and clinical status

Abstract

The reduction and analysis of gait waveform data is a significant barrier to the clinical application of gait analysis. Principal component modelling of gait waveform data reduced the waveform data to measures of distance from normal and these distance measures were shown to be sensitive to changes in gait pattern associated with knee osteoarthritis and its treatment by unicompartmental arthroplasty. Principal component models were developed for eight knee kinematic and kinetic gait waveforms of a group of 30 normal elderly subjects. Each model consisted of a set of loading vectors, principal component scores and residuals. The loading vectors revealed the structure of the model and the scores and residuals were used as the distance measures about which confidence intervals were developed. Pre-operative and post-operative gait data from 13 unicompartmental arthroplasty (UCA) patients were used to demonstrate the application of the principal component models to pathological gait data. A gait score was developed to indicate the overall assessment of the kinematic and kinetic gait measures by the principal component models. This gait score was shown to agree with the clinical status as measured by the Knee Society Score (pre-op: r s=0.86; post-op: r s=0.73). Thus, the differences in gait pattern detected by the principal component models were clinical relevant.