Representing Gait Outcomes For Advanced Knee Osteoarthritis Using a Wearable Inertial Sensor System

Abstract

This thesis aimed to develop an efficient Inertial Measurement Unit (IMU) protocol for capturing kinematic and kinetic gait patterns in knee Osteoarthritis (OA) patients in a free-living environment, moving beyond costly lab-based motion capture. The study involved end-stage knee OA patients before and after knee arthroplasty (KA). The first objective showed good statistical agreement between IMU-derived and motion capture discrete metrics for knee adduction and flexion angles, with differences within clinically acceptable limits (minimum clinically important difference or typical inter-session variability). The second objective explored using IMU-derived features (accelerations/angular velocities) to estimate the temporal patterns of kinetic waveforms (flexion/adduction moments). Shank-only models explained over half the variance, improving with foot sensor data (R2=0.55-0.69). These results validate the ability to capture clinically relevant gait outcomes with IMU's, enabling the transition to remote, continuous monitoring of OA progression and recovery outside of the clinic.