Identifying Underlying Individuality Across Running, Walking, and
Handwriting Patterns with Conditional Cycle--Consistent Generative
Adversarial Networks
Abstract
In recent years, the analysis of movement patterns has increasingly
focused on the individuality of movements. After long speculations about
weak individuality, strong individuality is now accepted, and the first
situation–dependent fine structures within it are already identified.
Methodologically, however, only signals of same movements have been
compared so far. The goal of this work is to detect cross-movement
commonalities of individual walking, running, and handwriting patterns
using data augmentation. 17 healthy adults (35.8 ± 11.1 years, 8
females, 9 males) each performed 627.9 ± 129.0 walking strides, 962.9 ±
182.0 running strides, and 59.25 ± 1.8 handwritings. Using the
conditional CycleGAN, conditioned on the participant’s class, a pairwise
transformation between the vertical ground reaction force during walking
and running and the vertical pen pressure during handwriting was learned
in the first step. In the second step, the original data of the
respective movements were used to artificially generate the other
movement data. In the third step, it was tested whether the artificially
generated data could be correctly assigned to a person via
classification using a support vector machine trained with original data
of the movement. The classification F1–score ranged from 46.8% for
handwriting data generated from walking data to 98.9% for walking data
generated from running data. Thus, cross–movement individual patterns
could be identified. Therefore, the methodology presented in this study
may help to enable cross–movement analysis and the artificial
generation of larger amounts of data.