Osteoarthritis and cartilage open · 2026
Agreement between markerless and marker-based motion capture for knee kinematics and kinetics during functional activities in knee osteoarthritis.
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Torres Rhodora Therese Gumabao, Senderling Benjamin, Kim Ehyun, Rose Michael, Gheller Mary, Neogi Tuhina, Kumar Deepak
Аннотация
OBJECTIVE: To assess the agreement between markerless and marker-based motion capture for measuring knee kinematics and kinetics during functional activities in people with knee osteoarthritis.
METHOD: Data from 22 participants (age 63.2 ± 7.6 years; BMI 28.2 ± 4.7 kg/m2; 64% women) with mild-to-moderate osteoarthritis related disability were analyzed. Synchronized marker-based motion capture (Qualisys Oqus, 250 Hz), color videos (Qualisys Miqus, 50 Hz), and force platform data (2000 Hz) were collected during self-selected & fast walking and sit-to-stand. Video data were processed using an artificial intelligence-based pose estimation algorithm (Theia3D v2022.1.0.2309). Knee kinematics and kinetics (discrete measures and waveforms) were derived from both systems for the stance phase of walking and ascent phase of sit-to-stand. Agreement was evaluated using intraclass correlation coefficients, Pearson's correlation coefficients, and Bland-Altman analyses.
RESULTS: Across tasks, RMSD ranged from 3.8° to 5.7° for kinematics and 0.2 to 1.2%BW∗ht for kinetics, with joint center differences <5 cm. Agreements on knee adduction moment (KAM) impulse during both walking speeds (ICC = 0.94) and 2nd KAM peak during fast walking (ICC = 0.91) were excellent. Agreements on peak knee adduction angle (ICC = 0.78-0.83), sagittal knee range of motion (ICC = 0.78-0.83), 1st peak of knee flexion moment (KFM) (ICC = 0.76-0.81), and KAM (ICC = 0.87-0.90) were good, as well as, 2nd KAM peak during self-selected walking (ICC = 0.84), KFM impulse during fast walking (ICC = 0.84), and peak KFM during sit-to-stand (ICC = 0.78).
CONCLUSION: The evaluated markerless system is a potential efficient alternative for estimating knee kinematics and kinetics for people with knee osteoarthritis, particularly for sagittal range of motion and frontal kinetics during walking, and sagittal moments in sit-to-stand.
METHOD: Data from 22 participants (age 63.2 ± 7.6 years; BMI 28.2 ± 4.7 kg/m2; 64% women) with mild-to-moderate osteoarthritis related disability were analyzed. Synchronized marker-based motion capture (Qualisys Oqus, 250 Hz), color videos (Qualisys Miqus, 50 Hz), and force platform data (2000 Hz) were collected during self-selected & fast walking and sit-to-stand. Video data were processed using an artificial intelligence-based pose estimation algorithm (Theia3D v2022.1.0.2309). Knee kinematics and kinetics (discrete measures and waveforms) were derived from both systems for the stance phase of walking and ascent phase of sit-to-stand. Agreement was evaluated using intraclass correlation coefficients, Pearson's correlation coefficients, and Bland-Altman analyses.
RESULTS: Across tasks, RMSD ranged from 3.8° to 5.7° for kinematics and 0.2 to 1.2%BW∗ht for kinetics, with joint center differences <5 cm. Agreements on knee adduction moment (KAM) impulse during both walking speeds (ICC = 0.94) and 2nd KAM peak during fast walking (ICC = 0.91) were excellent. Agreements on peak knee adduction angle (ICC = 0.78-0.83), sagittal knee range of motion (ICC = 0.78-0.83), 1st peak of knee flexion moment (KFM) (ICC = 0.76-0.81), and KAM (ICC = 0.87-0.90) were good, as well as, 2nd KAM peak during self-selected walking (ICC = 0.84), KFM impulse during fast walking (ICC = 0.84), and peak KFM during sit-to-stand (ICC = 0.78).
CONCLUSION: The evaluated markerless system is a potential efficient alternative for estimating knee kinematics and kinetics for people with knee osteoarthritis, particularly for sagittal range of motion and frontal kinetics during walking, and sagittal moments in sit-to-stand.