  Three-dimensional clinical gait analysis is essential for selecting optimal
treatment interventions for patients with cerebral palsy (CP), but generates a
large amount of time series data. For the automated analysis of these data,
machine learning approaches yield promising results. However, due to their
black-box nature, such approaches are often mistrusted by clinicians. We
propose gaitXplorer, a visual analytics approach for the classification of
CP-related gait patterns that integrates Grad-CAM, a well-established
explainable artificial intelligence algorithm, for explanations of machine
learning classifications. Regions of high relevance for classification are
highlighted in the interactive visual interface. The approach is evaluated in a
case study with two clinical gait experts. They inspected the explanations for
a sample of eight patients using the visual interface and expressed which
relevance scores they found trustworthy and which they found suspicious.
Overall, the clinicians gave positive feedback on the approach as it allowed
them a better understanding of which regions in the data were relevant for the
classification.
