Markerless motion capture provides repeatable gait outcomes in patients with knee osteoarthritis.

Motion analysis has seen minimal adoption for orthopaedic clinical assessments. Markerless motion capture solutions, namely Theia3D, address limitations of previous methods and provide gait outcomes that are robust to clothing choice and repeatable in healthy adults. Repeatability in orthopaedic populations has not been investigated and is important for clinical utility and adoption. The purpose of this study was to evaluate the repeatability of Theia3D for gait analysis in a knee osteoarthritis population. Ten orthopaedic patients with knee osteoarthritis underwent gait analysis on three visits, with an average of 8 days between. Participants were recorded during one-minute overground walking trials at self-selected typical and fast speeds by 8 synchronized video cameras. Video data were processed using Theia3D. Intraclass correlations were used to examine the repeatability of temporal distance metrics as well as segment lengths of the underlying kinematic model. Inter-trial and inter-session variability of lower extremity joint angles were estimated for each point of the gait cycle. Intraclass correlations were greater than 0.98 for all temporal distance metrics for both speeds. Lower body segment lengths had intraclass correlations above 0.90. Participant average joint angle waveforms displayed consistent patterns between visits. The average inter-trial and inter-session variability in joint angles across speeds were 1.17 and 1.45 degrees, respectively. The variability in joint angles between visits was less than typically reported for marker-based methods. Gait outcomes measured with Theia3D were highly repeatable in patients with knee osteoarthritis providing further validation for its use in clinical assessment and longitudinal studies.

Clothing condition does not affect meaningful clinical interpretation in markerless motion capture.

Markerless motion capture allows whole-body movements to be captured without the need for physical markers to be placed on the body. This enables motion capture analyses to be conducted in more ecologically valid environments. However, the influences of varied clothing on video-based markerless motion capture assessments remain largely unexplored. This study investigated two types of clothing conditions, "Sport" (gym shirt and shorts) and "Street" (unrestricted casual clothing), on gait parameters during overground walking by 29 participants at self-selected speeds using markerless motion capture. Segment lengths, gait spatiotemporal parameters, and lower-limb kinematics were compared between the two clothing conditions. Mean differences in segment length for the forearm, upper arm, thigh, and shank between clothing conditions ranged from 0.2 cm for the forearm to 0.9 cm for the thigh (p < 0.05 for thigh and shank) but below typical marker placement errors (1 - 2 cm). Seven out of 9 gait spatiotemporal parameters demonstrated statistically significant differences between clothing conditions (p < 0.05), however, these differences were approximately ten times smaller than minimal detectable changes in movement-related pathologies including multiple sclerosis and cerebral palsy. Hip, knee, and ankle joint angle root-mean-square deviation values averaged 2.6° and were comparable to previously reported average inter-session variability for this markerless system (2.8°). The results indicate that clothing, a potential limiting factor in markerless motion capture performance, would negligibly alter meaningful clinical interpretations under the conditions investigated.