Smartphone-Derived Joint Angular Velocities in Sit-to-Stand Motion: A Novel Spatiotemporal Marker for Symptomatic Knee Osteoarthritis

Background and Objective: Knee osteoarthritis (OA) is a debilitating condition that compromises mobility and exacerbates knee pain, necessitating accurate and accessible diagnostic tools. Traditional motion capture technology, while effective, is often cost-prohibitive and limited to laboratory settings. In response, we developed a novel, smartphone-based approach utilizing spatiotemporal analysis of joint angular velocities and angles in sit-to-stand (STS) motion to detect symptomatic knee OA. Our deep learning model, STS-Dynamics Net, analyzed 864 sit-to-stand motion videos from 120 participants, providing a nuanced assessment of joint dynamics and temporal interactions in trunk, knee, and ankle angles and velocities. Notably, our findings demonstrate that joint angular velocities are a robust spatiotemporal biomarker for knee OA detection, outperforming the WOMAC questionnaire and maximum trunk angle in diagnostic accuracy and rivalling the performance of gold-standard 3D marker-based systems. Furthermore, our analysis revealed a significant correlation between angular velocities and muscle volumes and fat-to-muscle ratios in the quadriceps and hamstrings, underscoring the role of muscle weakness in knee OA pathogenesis. This innovative approach has the potential to revolutionize knee OA detection, enabling reliable, cost-effective, and self-administered assessments in community settings and bridging the gap in accessible healthcare monitoring.