Explaining the unique nature of individual gait patterns with deep learning
By Fabian Horst, Sebastian Lapuschkin, Wojciech Samek, K. Müller, Wolfgang Immanuel Schöllhorn. 2018
255 citations recorded by the source corpus.
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.
Subfields: Biomedical Engineering, Physical Therapy, Sports Therapy and Rehabilitation
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In Biomedical Engineering
Citations in the corpus
- Towards Explainable Artificial Intelligence (2019, background): “These explainable non-linear models have already attracted attention in domains such as neuroscience [87,89,20], health [33,14,40], autonomous driving [31], drug design [70] and physics [78,72] and it can be expected that they will play a pivotal role in future scientific research.”
- Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021, background): “[77] introduce explanation techniques to the domain of human gait recognition and show that nonlinear learning models are not only the better predictors but also they can, at the same time, learn physiologically meaningful features for subject prediction, which aligns with expected features used by ”
- Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (2019, background): “These explainable non-linear models have already attracted attention in domains such as neuroscience [20,87,89], health [14,33,40], autonomous driving [31], drug design [70] and physics [72,78] and it can be expected that they will play a pivotal role in future scientific research.”
- Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond (2020, background): “For instance, [60] introduce interpretable machine learning methods to the domain of human gait recognition and show that non-linear learning models are not only the better predictors but that they can at the same time learn physiologically meaningful features for subject prediction which align…”