ArticleCommunications medicine2026
Smartphone-derived joint angular velocities in sit-to-stand motion provide a spatiotemporal marker for symptomatic knee osteoarthritis.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Smartphone-Derived Movement Analysis for Musculoskeletal Assessment: Smartphone-Estimated Relative Vertical Power During the Sit-to-Stand Test as an Accessible Predictor of Knee Extensor Strength in Older Adults.Medicina (Kaunas, Lithuania) · 2026Article
- Movement-Based Low Back Pain Subgroups Using Motion Tape Strain Data with Biomechanical and Causal Feature Engineering.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundKnee 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.
methodWe 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.
resultsHere 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 = 8.6
conclusionsThis 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.
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Registered trials
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