The effect of simulated marker misplacement on the interpretation of inter-limb differences during a change of direction task

The objective assessment of biomechanical asymmetries during movement tasks is used to monitor rehabilitation following anterior cruciate ligament reconstruction (ACLR). Marker placement is an important source of methodological variability within human motion analysis. It is currently unclear how marker placement error effects the interpretation of biomechanical asymmetries throughout post ACLR rehabilitation. The aim of this investigation was to determine the effect of random marker placement variation on the interpretation of inter-limb differences during a change of direction (CoD) task. Forty-seven participants 9 months post-ACLR and fifty uninjured controls completed a 90° CoD task on both limbs. Inter-limb differences in kinematic and kinetic metrics during the CoD stance phase were calculated for both groups using the Vicon Plug-in Gait model, and ACLR subjects were classified as having ‘normal’ or ‘abnormal’ inter-limb differences relative to the control group. Simulated random marker displacements based on published marker placement error ranges were then repeatedly applied to the lateral thigh, femoral epicondyle and tibia markers. ACLR inter-limb differences were recalculated each time, allowing the estimation of 95% confidence intervals and minimal identifiable between-session changes. ACLR subjects were also reclassified relative to the control group after each simulation and the percentage of participants to change classification was calculated. Marker displacements caused large deviations in inter-limb difference measures in several variables including hip rotation angle, knee abduction angle and knee abduction moment, thus limiting the ability to identify participants with large inter-limb differences relative to a control group. These findings highlight the challenges in using marker-based biomechanical models to conduct objective assessments of inter-limb differences during CoD tasks.