Discrimination capability of linear and nonlinear gait features in group classification

The variability of human movement can be defined as normal variations occurring in motor activity and quantified using linear statistics or nonlinear methods. In the human movement field, linear and nonlinear measures of variability have been used to discriminate groups and conditions in different contexts. Indeed, some authors support the idea that these gait features provide complementary information about movement. However, it is unclear which type of gait variability measure best discriminates different groups or conditions, as a comparison of the discrimination capacity between linear and nonlinear gait variability features in different groups has not been assessed. Therefore, the main objective of this study was to test the discrimination capacity of linear and nonlinear gait features to determine which type of feature would be the most efficient for discriminating older and younger adults and between lower limb amputees and nonamputees using classification algorithms. Data from previously published studies were used. The classification task was performed using the k-nearest neighbors and random forest algorithms. Our results showed that using a combination of linear and nonlinear features resulted in the highest mean accuracy rates (>90%) in group classification, reinforcing the idea that these features are complementary and express different aspects of movement.