  Machine learning (ML) techniques such as (deep) artificial neural networks
(DNN) are solving very successfully a plethora of tasks and provide new
predictive models for complex physical, chemical, biological and social
systems. However, in most cases this comes with the disadvantage of acting as a
black box, rarely providing information about what made them arrive at a
particular prediction. This black box aspect of ML techniques can be
problematic especially in medical diagnoses, so far hampering a clinical
acceptance. The present paper studies the uniqueness of individual gait
patterns in clinical biomechanics using DNNs. By attributing portions of the
model predictions back to the input variables (ground reaction forces and
full-body joint angles), the Layer-Wise Relevance Propagation (LRP) technique
reliably demonstrates which variables at what time windows of the gait cycle
are most relevant for the characterisation of gait patterns from a certain
individual. By measuring the time-resolved contribution of each input variable
to the prediction of ML techniques such as DNNs, our method describes the first
general framework that enables to understand and interpret non-linear ML
methods in (biomechanical) gait analysis and thereby supplies a powerful tool
for analysis, diagnosis and treatment of human gait.
