Tracking real-world changes in osteoarthritic gait patterns using wearable sensors

Abstract

Intra-articular corticosteroid knee injections (ICIs) were used as a tool to determine the sensitivity of wearable inertial sensors and machine learning algorithms in identifying meaningful changes in gait patterns amidst day-to-day fluctuations in out-of-laboratory gait. Specifically, three overarching aims were proposed; I) Determine if three gait trials could define an everyday typical gait pattern, II) investigate if post-injection atypical strides are significantly different from pre-injection atypical strides and III) explore the relationship between changes in pain and atypical strides. Nine knee OA patients (7M/2F) were recruited from St. Joseph’s Healthcare Hamilton. Participants completed a total of four walking trials prior to the ICI and three following. Participants were fitted with two wearable sensors on each shank just below the knee, and one sensor on the lower back during every trial. Data from these sensors were processed to train and test a one-class support vector machine (OCSVM). Individual gait models were created based on three out of the four pre-injection trials. Each trained model was tested on a withheld pre-injection trial and three post-injection trials to determine the number of typical and atypical gait cycles. Self-reported pain was analyzed throughout the study and compared to the percent of atypical strides seen during each walk. It was found that three gait trials could not define a typical gait model and that post-injection atypical strides were not significantly different from with-held pre-injection atypical strides. Finally, large variations and fluctuations in self-reported pain were observed on a week-to-week basis, which were not significantly correlated to atypical strides observed. This study was the first to investigate the sensitivity of wearable inertial sensors and machine learning algorithms to detect changes in real-world gait patterns and provides foundational work for using wearable sensors to monitor and triage knee OA patients.