# Inter-Session Repeatability of Marker-Less Motion Capture of Treadmill Running Gait
## 4 Discussion  
Distinguishing between measurement error and true change in repeated measures study design (clinical or research application) requires reliable data sources. The aim of the present study was to assess the reliability of an ML motion capture system to measure key biomechanical metrics during TM running. In the current investigation, spatiotemporal metrics derived from the automatic detection of foot strike and toe off from ML kinematics demonstrated excellent agreement (ICC = 0.982) with the same metrics derived from an instrumented pressure TM. Mean differences in stance time were less than 10 milliseconds (less than the sampling interval of the instrumented TM) indicating that the automated kinematic-based algorithms detected stance time within one frame of force data. These results are within the error tolerance reported for automated event detection methods from marker-based motion capture of TM running [15,16]. As this study's sample of runners included a variety of foot strikes utilizing various footwear and running speeds, the proposed ML kinematic algorithms appear to be quite robust and possibly viable as an alternative for clinics/labs without a force measurement device. The average intra-trial variation of lower extremity joint angles in all planes during TM running was small (<3\({}^{\circ}\)). This is smaller than the intra-trial variability demonstrated during overground walking using the same ML motion capture system [13]. The reduced variation can most likely be attributed to a more consistent velocity obtained during TM gait versus overground gait. Another potential reason for the reduced variation was that the present investigation used more than 30 gait cycles per trial, while an overground trial would only include 2-3 gait cycles. The increased number of gait cycles may better capture the normal variability during gait and reduce the influence of the extreme gait cycles. The maximum variability in the joint kinematics was 4.7\({}^{\circ}\) for knee flexion during the swing phase. Although it would be challenging to discern what percentage of this measured variance is due to intrinsic (natural variation) versus extrinsic (ML motion capture software) factors, increased knee flexion variability has been previously reported during terminal swing in running [26]. The greatest variations over the entire gait cycle during TM running occurred for the sagittal plane joint angles (average 2.0\({}^{\circ}\)). The frontal plane joint angles demonstrated the least variance (average 1.2\({}^{\circ}\)). The variability between sessions was less than the variability within a running TM trial, and as a result, the variability ratio for all lower extremity joint angles was less than one. This indicates that performing multiple sessions with an ML motion capture system (while being deliberate about methodological consistency) does not increase kinematic variability. This demonstrates one of the advantages of an ML motion capture system over a marker-based motion capture system. With a marker-based motion capture system, the placement of the reflective and the movement of the skin/clothing present potential sources