The applicability of markerless motion capture for clinical gait analysis in children with cerebral palsy.

The aim of this comparative, cross-sectional study was to determine whether markerless motion capture can track deviating gait patterns in children with cerebral palsy (CP) to a similar extent as marker-based motion capturing. Clinical gait analysis (CGA) was performed for 30 children with spastic CP and 15 typically developing (TD) children. Marker data were processed with the Human Body Model and video files with Theia3D markerless software, to calculate joint angles for both systems. Statistical parametric mapping paired t-tests were used to compare the trunk, pelvis, hip, knee and ankle joint angles, for both TD and CP, as well as for the deviation from the norm in the CP group. Individual differences were quantified using mean absolute differences. Markerless motion capture was able to track frontal plane angles and sagittal plane knee and ankle angles well, but individual deviations in pelvic tilt and transverse hip rotation as present in CP were not captured by the system. Markerless motion capture is a promising new method for CGA in children with CP, but requires improvement to better capture several clinically relevant deviations especially in pelvic tilt and transverse hip rotation.

Development of a core set of gait features and their potential underlying impairments to assist gait data interpretation in children with cerebral palsy.

Background: The interpretation of clinical gait data in children with cerebral palsy (CP) is time-consuming, requires extensive expertise and often lacks transparency. Here we aimed to develop a set of look-up tables to support this process, linking typical gait features as present in CP to their potential underlying impairments. Methods: We developed an initial core set of gait features and their potential underlying impairments based on biomechanical reasoning, literature and clinical experience. This core set was further specified through a Delphi process in a multidisciplinary group of experts in gait analysis of children with CP and evaluated on 20 patient cases. The likelihood of the listed gait feature-impairment relationships was scored by the expert panel on a five-point scale. Results: The final core set included 120 relevant gait feature-impairment relations including likelihood scores. This set was presented in the form of look-up tables in both directions, i.e., sorted by gait features with potential underlying impairment, and sorted by impairments with potential related gait features. The average likelihood score for the relations was 3.5 ± 0.6 (range 2.1-4.6). Conclusion: The developed set of look-up tables linking gait features and impairments, can assist gait analysts and clinicians in standardized biomechanical reasoning, to support treatment decision-making for gait impairments in children with CP.