Validation of markerless video-based gait analysis using pose estimation in toddlers with and without neurodevelopmental disorders

Jeffrey T Anderson ^1* Jan Stenum ² Ryan Roemmich ³ Rujuta Wilson ^1,4*

• ¹ University of California, Los Angeles, Los Angeles, United States
• ² Center for Movement Studies, Kennedy Krieger Institute, Baltimore, Maryland, United States
• ³ Department of Physical Medicine and Rehabilitation, School of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, United States
• ⁴ Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States

The onset of locomotion is a critical motor milestone in early childhood and increases engagement with the environment. Toddlers with neurodevelopmental disabilities often have atypical motor development that impacts later outcomes. Video-based gait analysis using pose estimation offers an alternative to standardized motor assessments which are subjective and difficult to ascertain in some populations, yet very little work has been done to determine its accuracy in young children. To fill this gap, this study aims to assess the feasibility and accuracy of pose estimation for gait analysis in children with a range of developmental levels. We analyzed the overground gait of 112 toddlers (M: 30 months, SD: 8 months) with and without developmental disabilities using the ProtoKinetics Zeno Walkway system. Simultaneously recorded videos were processed in OpenPose to perform pose estimation and a custom MATLAB workflow to calculate average spatiotemporal gait parameters. Pearson correlations were used to compare OpenPose with the Zeno Walkway for velocity, step length, and step time. A Bland-Altman analysis (difference vs. average) was used to assess the agreement between methodologies and determine the difference of means. Developmental levels were assessed using the Mullen Scales of Early Learning. Our analysis included children with autism (n = 77), non-autism developmental concerns (n= 6), tuberous sclerosis complex (n = 13), 22q deletion (n = 1), and typical development (n = 15). Mullen early learning composite scores ranged from 49 to 95 (m=80.91, sd=26.68). Velocity (r=0.87, p<0.0001), step length (r=0.79, p<0.0001), and step time (r=0.96, p<0.0001) were all highly correlated between OpenPose and the Zeno Walkway, with an absolute difference of means of 0.04 m/s, 0.03 m, and 0.01 s, respectively. Our results suggest that video-based gait analysis using pose estimation is accurate in toddlers with a range of developmental levels. Video-based gait analysis is low cost and can be implemented for remote data collection in natural environments such as a participant's home. These advantages open possibilities for using repeated measures to increase our knowledge of how gait ability changes over time in pediatric populations and improve clinical screening tools, particularly in those with neurodevelopmental disabilities who exhibit motor impairments.