## 1 Introduction  
Three-dimensional clinical gait analysis (CGA) is an important diagnostic tool within the field of movement disorders [1]. Optical marker tracking systems are considered the gold standard for this type of assessment [2; 3]. However, discrepancies in CGA data across multiple analyses can be caused by differences in marker sets [4], observer error [5], marker placement [6] and patient variability [7]. Despite applied standardized protocols (e.g., marker-set, measurement pipeline), incompatibilities and discrepancies within CGA data of the same patient cohorts collected by different institutions are often observed, and have caused reservations concerning the applicability of CGA among multicenter studies [3; 4; 8]. [PERSON] et al. [4] compared the trunk, pelvis and lower limb kinematics of five separate measurement protocols (different marker sets), using a single data pool of subjects. These authors reported good correlations between kinematic variables in the sagittal plane (flexion/extension), but poorer correlations for out-of-sagittal-plane rotations, such as knee abduction/adduction. They also reported good correlations among protocols with similar biomechanical models [4]. [PERSON] et al. [4] concluded that the comparison of different measurement protocols results in higher data variability when compared to interobserver and interlaboratory comparisons for most gait characteristics. Obviously, model conventions and definitions are crucial for data comparison. [PERSON] et al. [8] identified the marker placement procedure of examiners as the largest source of error. The use of a standardized protocol for marker placement decreased data variability by up to 20%. Based on clinical experience, the authors of the current study hypothesized that significant discrepancies would exist between CGA data sets collected using different hardware and software infrastructures or configurations. Due to the observed limitations in the reproducibility of CGA data, efforts have focused on the reduction of measurement error, with the aim of producing data with the highest possible degree of reproducibility. To improve our understanding of the accuracy and reproducibility of CGA data, cross-laboratory studies must be performed using a single cohort of subjects. Previous research has reported on the repeatability of motion capture data using separate trials, sessions and observers under various conditions for a specific CGA setup or laboratory [5; 6; 7; 9; 10]. However, reports for multi-center repeatability are limited and have focused on differences in hardware configurations, marker placement, as well as between trials and days of measurement [3; 8; 11]. Unfortunately, a validation using the same motion capturing technology, but different hardware and software infrastructure/configurations (e.g., capturing software, camera type), among a single cohort of subjects is lacking. It is critical for clinicians and researchers to have reliable examination tools to accurately and objectively assess the functional status of a joint [12; 13; 14; 15; 16]. A high level of intraobserver reliability is imperative to accurately evaluate the longitudinal effects of the rehabilitation process and to identify differences between subjects [17; 18]. A central research question is whether the findings of clinical gait analyses conducted by multiple laboratories are consistent and reliable enough for making clinical decisions--or is the dependence on observers, repeat measurements or measurement and analysis protocols too large? The specific aims of this study were to evaluate the intra- and interobserver reliability and the equivalence of CGA data between two gait analysis laboratories (between-laboratory intraobserver reliability) using the same motion capturing technology, but different hardware and software infrastructure/configurations among a single cohort of subjects. The following two hypotheses were specifically investigated:  
1.