# Joint modeling and marker set selection significantly influence functional biomechanics in end-stage knee osteoarthritis: evidence from the sit-to-stand task

## TL;DR

This study shows that different motion capture protocols significantly affect biomechanical measurements in knee osteoarthritis patients during a sit-to-stand task.

## Contribution

The study reveals that marker set and modeling choices critically influence knee biomechanics measurements in end-stage osteoarthritis.

## Key findings

- Peak variability in knee flexion angle reached 23.99° across different protocols.
- Cluster-based marker sets showed reduced variability compared to anatomical-based ones.
- Differences were most pronounced during movement initiation and stabilization phases.

## Abstract

The sit-to-stand (STS) movement represents a mechanically demanding task, particularly informative in patients with knee osteoarthritis. While three-dimensional optoelectronic motion capture is the gold standard for analyzing joint biomechanics, the influence of protocol selection remains poorly characterized in the context of STS. This study investigated protocol-induced variability in knee kinematics and kinetics by evaluating two widely used marker sets: the anatomical-based IOR and the cluster-based CAST, each combined with either inverse kinematics or a six degrees-of-freedom joint model.

Twenty-four patients (mean age of 67 ± 5 years and BMI of 28.9 ± 3.8 kg/m2) with end-stage KOA (Kellgren-Lawrence grade 3 or 4) performed three STS trials, and biomechanical outputs were compared across the four resulting protocols using Mean Absolute Variability (MAV), Mean Absolute Differences (MAD), and Statistical Parametric Mapping (SPM).

Results revealed substantial variability across protocols, with the highest discrepancies observed in the sagittal plane: peak MAV reached 23.99° for knee flexion angle and 0.24 Nm/kg for knee flexion moment. Frontal and transverse parameters also showed clinically meaningful differences, particularly for knee adduction and internal rotation angles, with MAD values exceeding established thresholds. Differences were amplified when both markers set, and modeling strategy varied. In this context, cluster-based configurations showed reduced variability. SPM analyses revealed temporally localized differences, particularly at the initiation and final stabilization phases of the movement.

These findings emphasize the critical role of protocol selection in motion analysis and its direct impact on the interpretation of knee biomechanics during functional tasks, highlighting the importance of adopting consistent and robust methodological frameworks to ensure clinical reliability and cross-study comparability.

https://clinicaltrials.gov/, identifier NCT06634654.