# Inter-Session Repeatability of Marker-Less Motion Capture of Treadmill Running Gait
## 1 Introduction  
Three-dimensional (3D) motion capture within human biomechanics typically involves the use of infrared cameras and retro-reflective markers affixed to participants. This methodology, which can track 3D marker location at the sub-millimeter level depending on the capture volume, is commonly accepted as the nearest method to approach the \"gold standard\" fluoroscopy approach [1]. Although these marker-based methods are widely used in clinical and research domains, several limitations may challenge their practicality for certain applications. Fundamentally, motion capture aims to measure movement of the skeletal system. Retro-reflective markers placed on the skin of key bony landmarks (e.g., lateral malleolus) may move relative to the underlying skeletal system (i.e., bones) due to the movement of clothing (if present), skin, and soft tissue (e.g., adipose) [2]. Several methodological approaches have sought to minimize the influence of these artifacts; however, they cannot eliminate them [3]. As such, participants are typically asked to wear minimal or tight clothing to reduce this error source. Further, when data collection periods are long or participants sweat profusely (e.g., the maximal aerobic capacity running test), the propensity for markers moving or falling off is heightened. Both situations would compromise data fidelity. Markers placed on the participant are used to define a biomechanical model of the skeletal system. For example, markers are commonly placed on the femoral condyles with the vector connecting them representing the knee joint axis (i.e., the axis about which flexion and extension occur). The placement of the marker on the exact location of the condyle is subject to both random and systematic error as placement in research investigations can vary both between and within researchers [4; 5; 6] (i.e., repeated measures design). Thus, although markers can be tracked precisely, their location relative to the underlying skeletal system is approximate, inducing data variability and compromising reliability. From a pragmatic standpoint, the time required to securely fixate markers to participants is a serious drawback to the ease of use and clinical adoption of marker-based motion capture. Accurately fixing markers to a participant could take between 20 and 30 min for a skilled researcher in addition to the time required to prepare the markers prior to the data collection session. The presence of the markers on the body/clothing may also raise the specter that the participant will be cognizant of their presence and potentially alter their movement. Marker-less (ML) motion capture systems are similar to marker-based systems in that they seek to accurately and repeatably measure the 3D human segmental motion. Fundamentally, both approaches attempt to simplify the complex representation of the human skeletal system with a simplified biomechanical model. ML systems do not require markers to be placed on the participant and rely on synchronized 2D video cameras to obtain a 3D reconstruction. The obviation of markers has (i) vastly reduced the time and complexity for subject preparation, (ii) is non-invasive, and (iii) allows for data collection in non-laboratory settings (e.g., sporting games). With recent technological advances in computational speed and an apparent commercial market for ML systems, several software packages are available for purchase (e.g., Theia3D, DariMotion, and The Captury) or available as open source (e.g., OpenPose and OpenCap). It is beyond the scope of this manuscript to provide a comprehensive review dissecting similarities and/or differences between ML software approaches that each take towards generating 3D pose representations of the biomechanical model; however, the reader is pointed towards [PERSON] et al. [7] for a comprehensive review.