# Inter-Session Repeatability of Marker-Less Motion Capture of Treadmill Running Gait
## 2 Materials and Methods
### Data Processing and Analysis  
Noraxon MyoPressure software (Noraxon USA, Scottsdale, AZ, USA) collected and processed force and pressure TM data. Data were filtered with a 4 th order Butterworth low-pass filter (50 Hz) prior to bilateral stance time, step length, and cadence being computed and subsequently averaged for each trial. Visual observation of foot strike and evaluation of center of pressure mapping was used to classify foot strike into rearfoot strike (RFS) and non-rearfoot strike patterns. Theia3D ML motion capture software (Theia Marker-less Inc., Kingston, ON, CA) determined three-dimensional human segment location based upon the eight video record  
Figure 1: Eight synchronized Sony RXO II cameras (Sony Corporation; Minato, Japan) (120 Hz) were arranged on a truss 2.9 m from the floor in a horseshoe design (5.5 \(\times\) 5.5 m) to establish a capture volume of approximately 2.4 \(\times\) 2.4 \(\times\) 3.1 m. A Noraxon PhysTread Pressure Treadmill (Noraxon USA; Scottsdale, AZ) was centrally located and the floor was marked so it could be placed in the same location for each testing day. ings per trial. The derived 19-segment kinematic model consisted of an upper extremity and lower extremity kinematic chain. Lower extremity joints (hip, knee, and ankle) connecting neighboring segments within the kinematic chain were modeled as having three rotational degrees of freedom. Kinematic data (4 \(\times\) 4 segment rotation matrices) and locations of virtual bilateral heel markers were exported and subsequently processed with Visual3D Professional (C-Motion Inc.; Germantown, MD, USA). Data were filtered with a GCVSPL filter with a 12 Hz cutoff frequency [18]. A built-in Visual3D model with segment properties (segmental mass and center of mass location) derived from previous work [19; 20; 21] was applied to determine full body center of mass (COM) location throughout each trial. The Z-coordinate (vertical direction) of the distal endpoint of the toe segment was utilized to determine the toe-off gait event for each running gait cycle. A two-frame offset preceding when the Z-coordinate switched from the local minima to superior vertical movement was tagged as toe off (Figure 2). The vertical velocity of the COM (vCOM\({}_{\text{z}}\)) was utilized to determine foot contact. This algorithm, similar to [PERSON] and [PERSON] [16] who utilized the peak inferiorly directed pelvis segment velocity to detect foot strike, determined foot contact to be the data frame preceding the minima of vCOM\({}_{\text{z}}\) (Figure 3). Stance time, cadence, and step length computed and based on these kinematical-derived gait events were then compared to instrumented TM data from the 21 participants across the three respective speeds (63 trials) from the first data collection session. Figure 2: The toe-off gait event was determined for each footfall utilizing the distal endpoint of the toe segment derived from the ML motion capture data. A two-frame offset preceding the local minima (red circle) of the Z-coordinate position was tagged as toe off. Lower extremity joint angles (hip, knee, and ankle) were calculated between adjacent segments in all planes via Visual3D software using a standardized approach (Cardan rotation sequence: X-Y-Z) consistent with the joint coordinate system [22]. Segmental angles of the pelvis and trunk were computed relative to the laboratory coordinate system. Each gait cycle within the 25-30 \(s\) trials was time normalized (100 points) and exported to a custom Python software (ver. 5.3.3) script for final analysis. Time normalization was performed separately using the ML kinematic-derived gait events. All trials exceeded seven gait cycles, which has been reported to increase the confidence level to 90% (R > 0.9) for running kinematic data [23].