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Purpose <p>To address the subjective and limited quantification in knee osteoarthritis (KOA) gait assessment, this study proposes an HSMR-based spatiotemporal dual-topology algorithm for objectively quantifying lower-limb multi-joint synergy.</p> Methods <p>This algorithm utilizes the Human Skeleton and Mesh Recovery (HSMR) framework to reconstruct a biomechanically constrained Skeletal Kinematics Enveloped by a Learned body model (SKEL) parametric model and combines Qualisys for accuracy validation, enabling lower-limb joint angle extraction from monocular videos. Using data from 68 participants (32 controls, 36 KOA), temporal topology analysis quantifies single-joint complexity and stability, while a 3D spatiotemporal fusion algorithm characterizes multi-joint spatial synergy through 3D hip–knee–ankle cyclograms and projections. Based on this, compensatory changes in temporal motion sequences and variations in spatial hip–knee–ankle coordination mechanisms are objectively analyzed for KOA patients classified by Kellgren–Lawrence (KL) grades.</p> Results <p>At the temporal level, knee joint topological complexity in the KOA group decreased progressively with increasing KL grade, with KL2 and KL3-4 decreasing by approximately 7.8% and 12.8%, respectively, compared with controls; ankle joint cyclic stability in the KOA group showed an overall decline of about 35–40%. At the spatial coordination level, the swing-phase similarity score for KL3-4 decreased by 32.7% compared with controls, and the hip–knee two-dimensional projection similarity score decreased by 34.8%.</p> Conclusion <p>The spatiotemporal dual-topology features of this algorithm can significantly distinguish KOA pathological grades, making it a high-precision and interpretable tool for digital gait analysis.</p>

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Quantitative Assessment of Lower-Limb Multi-joint Synergy in Knee Osteoarthritis by Integrating Spatiotemporal Dual-Topology Features Under the HSMR Framework

  • 期刊论文

  • 发表日期：

    2026年7月14日

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  • Yuzhe Tan,
  • Xintong Zhao,
  • Zhijie Xiang,
  • Haicheng Wei,
  • Jing Zhao,
  • Xingzhou Du,
  • Yu Qin,
  • Yuanyi Jiao,
  • Xiaoyue Chang,
  • Xueqi Liu

摘要

Purpose

To address the subjective and limited quantification in knee osteoarthritis (KOA) gait assessment, this study proposes an HSMR-based spatiotemporal dual-topology algorithm for objectively quantifying lower-limb multi-joint synergy.

Methods

This algorithm utilizes the Human Skeleton and Mesh Recovery (HSMR) framework to reconstruct a biomechanically constrained Skeletal Kinematics Enveloped by a Learned body model (SKEL) parametric model and combines Qualisys for accuracy validation, enabling lower-limb joint angle extraction from monocular videos. Using data from 68 participants (32 controls, 36 KOA), temporal topology analysis quantifies single-joint complexity and stability, while a 3D spatiotemporal fusion algorithm characterizes multi-joint spatial synergy through 3D hip–knee–ankle cyclograms and projections. Based on this, compensatory changes in temporal motion sequences and variations in spatial hip–knee–ankle coordination mechanisms are objectively analyzed for KOA patients classified by Kellgren–Lawrence (KL) grades.

Results

At the temporal level, knee joint topological complexity in the KOA group decreased progressively with increasing KL grade, with KL2 and KL3-4 decreasing by approximately 7.8% and 12.8%, respectively, compared with controls; ankle joint cyclic stability in the KOA group showed an overall decline of about 35–40%. At the spatial coordination level, the swing-phase similarity score for KL3-4 decreased by 32.7% compared with controls, and the hip–knee two-dimensional projection similarity score decreased by 34.8%.

Conclusion

The spatiotemporal dual-topology features of this algorithm can significantly distinguish KOA pathological grades, making it a high-precision and interpretable tool for digital gait analysis.