Video-based markerless two-dimensional gait analysis with automated processing is feasible, provides objective quantification of gait and complements the evaluation of gait in children with cerebral palsy

Abstract Background Gait analysis aids in evaluation, classification and follow-up of gait pattern over time in children with cerebral palsy (CP). The sagittal plane is of special interest to assess flexed knee gait and ankle joint deviations that commonly progress with age and indicate deterioration of gait. Although most children with CP are ambulatory, no objective quantification of gait is currently included in any of the known international follow-up programs. Can video-based 2-dimensional markerless (2D ML) gait analysis with automated processing be feasible for evaluation and classification of gait in children with CP? Methods Twenty children with bilateral CP with Gross Motor Function Classification Scale (GMFCS) levels I–III, from five regions in Sweden, were included from the national CP registry. A single RGB-Depth video camera, sensitive to depth and contrast, was positioned laterally to a green walkway and background, with four light sources. A previously validated markerless method was employed to estimate hip, knee and ankle kinematics in the sagittal plane, together with foot orientation in relation to the room, gait speed and step length. Results Mean age was 10.4 (range 6.8–16.1) years. Eight children were classified as GMFCS level I, eight as II and four as III. Setup took 15 minutes, acquisition 5–15 minutes and processing 10–15 minutes per child. With the 2D ML method deviations from normal could be determined and used to implement the classification of gait pattern, proposed by Rodda et al. 2001. Conclusion 2D ML assessment is feasible, since it is accessible, easy to perform and well tolerated by the children. The 2D ML adds consistency and quantifies objectively important gait variables. It is both relevant and reasonable to include 2D ML gait assessment in the evaluation of children with CP.