Validation of a Wearable System for Lower Extremity Assessment

Abstract

Objective: Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients’ functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor-based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold-standard motion capture and gait measurement systems. Methods: Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor-based wearable system, a gold-standard motion capture system, and a pressure-based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t-test was conducted for inter-group comparison to analyze the data. Results: The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold-standard pressure-based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold-standard video-based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. Conclusions: This IMU-based wearable system delivered precise measuring results to evaluate patients with knee OA. This technology can also be used to guide rehabilitation exercises for patients with knee OA.