Relation of gait measures with mild unilateral knee pain during walking using machine learning

Abstract

Gait alterations in those with mild unilateral knee pain during walking may provide clues to modifiable alterations that affect progression of knee pain and osteoarthritis (OA). To examine this, we applied machine learning (ML) approaches to gait data from wearable sensors in a large observational knee OA cohort, the Multicenter Osteoarthritis (MOST) study. Participants completed a 20-m walk test wearing sensors on their trunk and ankles. Parameters describing spatiotemporal features of gait and symmetry, variability and complexity were extracted. We used an ensemble ML technique ("super learning") to identify gait variables in our cross-sectional data associated with the presence/absence of unilateral knee pain. We then used logistic regression to determine the association of selected gait variables with odds of mild knee pain. Of 2066 participants (mean age 63.6 [SD: 10.4] years, 56% female), 21.3% had mild unilateral pain while walking. Gait parameters selected in the ML process as influential included step regularity, sample entropy, gait speed, and amplitude dominant frequency, among others. In adjusted cross-sectional analyses, lower levels of step regularity (i.e., greater gait variability) and lower sample entropy(i.e., lower gait complexity) were associated with increased likelihood of unilateral mild pain while walking [aOR 0.80 (0.64-1.00) and aOR 0.79 (0.66-0.95), respectively].

Figure 1

Selection for analysis dataset. Selection of participants for analysis dataset from MOST cohort.

Figure 2

Machine learning process. Representation of the machine learning process repeated 100 times: data is split into 70% training set, 30% test set. Ensemble of ML algorithms is trained on the training set, then testing is done on the testing set and predictions are evaluated. Influential variables are assessed and saved for that run. At the end of all runs influential variables across all runs are assessed.