Most studies examining gait asymmetry have focused on infants and toddlers and have tended to use subjective methods of evaluating movement. No previous studies have examined gait symmetry in older children with autism using objective motion capture systems. The purpose of this paper was to quantify gait symmetry in children with autism versus age-matched controls. Fourteen children with autism (N=14) and twenty-two (N=22) age, height, and weight-matched controls participated in the study. An eight camera Vicon motion capture system and four Kistler force plates were used to compute temporal-spatial parameters and symmetry indices during walking. Group differences in these measures were tested using MANOVAs. No significant differences between the autism and control group were found for any of the temporal-spatial measures or symmetry indices. Therefore, results suggest that children with autism demonstrate typical symmetry or interlimb movement during gait. Further research is needed to examine the use of different gait inputs to the symmetry indices (e.g., joint angles and moments). A greater awareness of the movement patterns associated with autism may increase our understanding of this disorder and have important implications for treatment planning.

Previous research has demonstrated that children with autism walk with atypical ankle kinematics and kinetics. Although these studies have utilized single-segment foot (SSF) data, multisegment foot (MSF) kinematics can provide further information on foot mechanics. Machine learning (ML) tools allow the combination of MSF kinematic features for classifying autism gait patterns. In this study, multiple ML models are investigated, and the most contributing features are determined. This study involved 19 children with autism and 21 age-matched controls performing walking trials. A 34-marker system and a 12-camera motion capture system were used to compute SSF and MSF angles during walking. Features extracted from these foot angles and their combinations were used to develop support vector machine (SVM) models. Additional techniques-S Hapley Additive exPlanations (SHAP) and the Shapley Additive Global importancE (SAGE) are used for local and global importance of the black-box ML models. The results suggest that models based on combinations of MSF kinematic features classify autism patterns with an accuracy of 96.3%, which is higher than using SSF kinematic features (83.8%). The relative angle between the metatarsal and midfoot segments had the highest contribution to the classification of autism gait patterns. The study demonstrated that kinematic features from MSF angles, supported by ML models, can provide an accurate and interpretable classification of autism and control patterns in children.

Background: Currently, age-related changes in foot mechanics are poorly understood. A greater understanding of the natural changes in foot motion is needed to improve our understanding of pathological foot conditions. Methods: The purpose of this study was to compare multisegment foot kinematic data during gait in younger and older individuals. Eleven (N = 11) adult male participants between the ages of 18 - 30 years (younger group; mean ± SD: 24.6 ± 3.0 years) and eleven (N = 11) adults aged 55 years or older (older group; mean ± SD: 65.0 ± 4.2 years) were recruited for the study. The foot was modeled as a four-segment rigid body model. Three-dimensional kinematic and kinetic gait parameters were recorded using an 8-camera Vicon MCam motion capture system and two Kistler force plates. A MANOVA was used to test for significant differences in mean temporal-spatial data, mean ranges of motion, and mean peak joint angle data between age groups. Results: No significant differences (P > 0.05) were found between the two age groups for any of the gait parameters. The results of the present study suggest that individuals aged 65.0 ± 4.2 years have foot mechanics that are comparable to younger walkers. Conclusions: As such, any deviations in motion at this age may be indicative of an underlying disease or disorder. doi:10.4021/jocmr984w