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 the 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. A total of 17 healthy adults (35.8 ±
11.1 years, eight women and nine men) each performed 627.9 ± 129.0
walking strides, 962.9 ± 182.0 running strides, and 59.25 ± 1.8
handwritings. Using the conditional cycle-consistent generative
adversarial network (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, 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 was tested. 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.