Gait & posture · 2025
Measuring static and dynamic lower limb alignment in patients with advanced knee osteoarthritis using markerless motion capture.
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Calderone Jacob, Outerleys Jereme, Diaz Dilernia Fernando, Mann Steve, Wood Gavin, Deluzio Kevin, Laende Elise
Аннотация
PURPOSE: Marked lower limb malalignment is associated with the progression of knee osteoarthritis (OA). Markerless motion capture is a computer-vision tool that can measure lower limb alignment throughout both static and dynamic tasks.
RESEARCH QUESTIONS: The primary objective of the study was to quantify static and dynamic lower limb alignment for patients diagnosed with advanced medial and lateral knee OA. This analysis also explored sex differences in alignment from the static and dynamic tasks. The secondary objective was to investigate if the mean knee adduction angle during quiet standing and the peak knee adduction angle during the first half of stance of gait were associated.
METHODS: Ninety-two patients (37 male, 55 female) diagnosed with advanced knee OA (83 with predominantly medial knee OA, and 9 with predominantly lateral knee OA) completed a quiet standing task and a gait task for markerless motion capture using Theia3D software. Two-way analysis of variance tests were used to investigate sex differences and unpaired t-tests were computed to compare knee OA groups.
RESULTS: Statistically significant differences in the knee adduction angle were found between medial and lateral knee OA groups during quiet standing (p < 0.001) and the first half of stance of gait (p < 0.0001). A strong correlation was found (Spearman's ρ = 0.86, p < 0.0001) in lower limb alignment between the static and gait tasks.
SIGNIFICANCE: These results show how markerless technology was able to quantify lower limb alignment and demonstrates potential for integration into clinic to assess musculoskeletal diseases.
RESEARCH QUESTIONS: The primary objective of the study was to quantify static and dynamic lower limb alignment for patients diagnosed with advanced medial and lateral knee OA. This analysis also explored sex differences in alignment from the static and dynamic tasks. The secondary objective was to investigate if the mean knee adduction angle during quiet standing and the peak knee adduction angle during the first half of stance of gait were associated.
METHODS: Ninety-two patients (37 male, 55 female) diagnosed with advanced knee OA (83 with predominantly medial knee OA, and 9 with predominantly lateral knee OA) completed a quiet standing task and a gait task for markerless motion capture using Theia3D software. Two-way analysis of variance tests were used to investigate sex differences and unpaired t-tests were computed to compare knee OA groups.
RESULTS: Statistically significant differences in the knee adduction angle were found between medial and lateral knee OA groups during quiet standing (p < 0.001) and the first half of stance of gait (p < 0.0001). A strong correlation was found (Spearman's ρ = 0.86, p < 0.0001) in lower limb alignment between the static and gait tasks.
SIGNIFICANCE: These results show how markerless technology was able to quantify lower limb alignment and demonstrates potential for integration into clinic to assess musculoskeletal diseases.