# Inter-Session Repeatability of Marker-Less Motion Capture of Treadmill Running Gait
## 1 Introduction
Theia3D (Theia Marker-less Inc., Kingston, ON, Canada), a commercial provider of ML motion capture, utilizes a machine-learning based approach to solve the 3D biomechanical model representation from synchronized and calibrated 2D video. Kinematics measured with Theia3D and concurrently with the gold standard marker-based motion capture have compared favorably for treadmill walking gait, overground walking gait, baseball pitching, and boxing [8; 9; 10; 11]. In particular, walking gait spatial metrics (e.g., stride length, step length, and stride width) determined from Theia3D had good comparisons to marker-based motion capture and fell below the minimal detectable change for these parameters [12]. Sagittal and frontal plane lower body joint angles determined from Theia3D deviated from marker-based motion capture by between 2.6 and 11\({}^{\circ}\), while transverse plane lower body joint angles deviated up to 13\({}^{\circ}\) for walking gait [10]. However, a follow-up study determined the inter-session repeatability for walking lower extremity joint angles determined from Theia3D was less than 2.5\({}^{\circ}\) across all joint angles which outperforms traditional marker-based motion capture [13]. For upper extremity sporting motions (baseball pitching and boxing), Theia3D's joint angle determination differed more substantially from the gold standard with reduced agreement occurring along the internal/external joint axes [9; 14]. Despite the differences, segment velocities compared favorably, and patterns were similar enough to warrant consideration for future applications. To date, no investigation has reported the reliability of treadmill running gait kinematics when assessed with ML motion capture over multiple sessions. Determination of key event markers (e.g., foot strike and toe off) is critical for the biomechanical analysis of running gait. In experimental and clinical setups without a force plate, these event markers can either be determined via visual inspection of a video recording or by kinematic algorithms. Kinematic algorithms using marker-based motion capture have achieved an absolute error of less than 24.7 ms for foot contact (using a reafoot strike) and less than 5.3 ms for toe off when compared to kinetic outputs [15]. [PERSON] and [PERSON] [16] also reported excellent agreement between event timings from a force plate and their foot contact algorithm which utilizes the vertical velocity of the pelvis segment. The algorithm's accuracy was assessed for all foot strike types (rear, mid, and forefoot) during overground running. To date, no investigation has reported the repeatability of ML motion capture (Theia3D) for the determination of kinematics (e.g., joint angles) and subsequent detection of key gait events during treadmill running across a range of speeds. Therefore, the following specific aims are proposed for this study:  
1. To determine the level of agreement between spatiotemporal metrics (stance time, step length, and cadence) derived from automatic detection of foot strike and toe off during running to a gold standard assessment (an instrumented pressure treadmill). 2. To determine the intra-trial variability, inter-session variability, and variability ratio of lower extremity joint angles (hip, knee, and ankle) computed from an ML motion capture system across the entire running gait cycle. 3. To determine the inter-session repeatability, standard error of measurement, and minimal detectable change of 15 key discrete biomechanical metrics of the stance phase of running.