  Machine learning (ML) models have proven effective in classifying gait
analysis data, e.g., binary classification of young vs. older adults. ML
models, however, lack in providing human understandable explanations for their
predictions. This "black-box" behavior impedes the understanding of which input
features the model predictions are based on. We investigated an Explainable
Artificial Intelligence method, i.e., Layer-wise Relevance Propagation (LRP),
for gait analysis data. The research question was: Which input features are
used by ML models to classify age-related differences in walking patterns? We
utilized a subset of the AIST Gait Database 2019 containing five bilateral
ground reaction force (GRF) recordings per person during barefoot walking of
healthy participants. Each input signal was min-max normalized before
concatenation and fed into a Convolutional Neural Network (CNN). Participants
were divided into three age groups: young (20-39 years), middle-aged (40-64
years), and older (65-79 years) adults. The classification accuracy and
relevance scores (derived using LRP) were averaged over a stratified ten-fold
cross-validation. The mean classification accuracy of 60.1% was clearly higher
than the zero-rule baseline of 37.3%. The confusion matrix shows that the CNN
distinguished younger and older adults well, but had difficulty modeling the
middle-aged adults.
