Explaining the Unique Nature of Individual Gait Patterns with Deep Learning
This provides a tool for clinicians to interpret gait analysis, potentially aiding diagnosis and treatment, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of interpreting black-box deep learning models in medical gait analysis by using Layer-Wise Relevance Propagation to identify key variables in gait patterns, resulting in a framework that reliably attributes predictions to specific input variables like ground reaction forces and joint angles.
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.