  State-of-the-art machine learning (ML) models are highly effective in
classifying gait analysis data, however, they lack in providing explanations
for their predictions. This "black-box" characteristic makes it impossible to
understand on which input patterns, ML models base their predictions. The
present study investigates whether Explainable Artificial Intelligence methods,
i.e., Layer-wise Relevance Propagation (LRP), can be useful to enhance the
explainability of ML predictions in gait classification. The research question
was: Which input patterns are most relevant for an automated gender
classification model and do they correspond to characteristics identified in
the literature? We utilized a subset of the GAITREC dataset containing five
bilateral ground reaction force (GRF) recordings per person during barefoot
walking of 62 healthy participants: 34 females and 28 males. Each input signal
(right and left side) was min-max normalized before concatenation and fed into
a multi-layer Convolutional Neural Network (CNN). The classification accuracy
was obtained over a stratified ten-fold cross-validation. To identify
gender-specific patterns, the input relevance scores were derived using LRP.
The mean classification accuracy of the CNN with 83.3% showed a clear
superiority over the zero-rule baseline of 54.8%.
