The Uniqueness of Gait Patterns Differs Across Data Modalities and Walking Conditions

This paper investigates the level of information that different data modalities—including ground reaction force, joint angles and angular velocities, and the body's centre of mass—hold in terms of the uniqueness of the gait pattern. The study uniquely extends across three walking speeds and five step frequencies per speed, including a total of 15 walking conditions. Using the classification accuracy of a machine learning algorithm as a proxy to the uniqueness of gait signature, the study provides a better understanding of the gait patterns across different modalities and walking conditions in the context of inter and intra-subject gait variability.