Smartphone-derived joint angular velocities in sit-to-stand motion provide a spatiotemporal marker for symptomatic knee osteoarthritis

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 smartphone-based approach utilizing spatiotemporal analysis of joint angular velocities and angles in sit-to-stand (STS) motion to detect symptomatic knee OA. We analyzed 2063 sagittal-viewed sit-to-stand motion videos from 309 participants and proposed a STS-D Index based on a deep learning model, STS Dynamics Net, which provides a nuanced quantification of joint dynamics and temporal interactions in trunk, knee, and ankle angles and velocities for detection of symptomatic knee OA. Here we show that joint angular velocities are a robust spatiotemporal biomarker for symptomatic knee OA detection (AUC 0. 7759 ± 0. 0219), not only do they outperform the STS pace (AUC 0. 6554 ± 0. 0268, p = 8. 610-5) and maximum trunk angle (AUC 0. 7025 ± 0. 0253, p = 2. 110-3) in diagnostic accuracy and rival the performance of gold-standard 3D marker-based systems (AUC 0. 7855 ± 0. 0229), but they also show significant correlations with WOMAC sub-scores (p < 0. 0001). Furthermore, our analysis reveals 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. Chan et al. develop a smartphone-based method that uses artificial intelligence to analyze sit-to-stand movements from standard video recordings. Video-derived joint movement speed outperforms conventional clinical motion assessment and achieves accuracy on par with laboratory-based motion analysis for detecting knee osteoarthritis. Knee osteoarthritis is a painful condition that limits movement, yet its assessment often relies on subjective clinical observation or specialized equipment. This study aimed to develop a simple and affordable screening method using standard smartphones. We analyzed smartphone videos from over 300 participants performing a sit-to-stand movement. Using artificial intelligence, we quantified joint movement speed and angles of the knee, trunk, and ankle. We found that video-derived movement speed is a more sensitive and objective indicator of knee osteoarthritis than conventional clinician-based motion assessment, while achieving performance comparable with laboratory-based motion analysis systems. This smartphone-based approach supports accessible, home-based knee health screening and long-term monitoring.