# Explaining the Unique Nature of Individual Gait Patterns with Deep Learning

arXiv: https://arxiv.org/abs/1808.04308 · HTML: https://mlsanity.com/paper/1808.04308/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2018.

## Built on by (most cited first, 17 of 17)

- [Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks](https://mlsanity.com/paper/2103.15449/) (2021)
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- [Explainable AI and Machine Learning Towards Human Gait Deterioration Analysis](https://mlsanity.com/paper/2306.07165/) (2023)
- [Explaining Deep Learning Models for Age-related Gait Classification based on time series acceleration](https://mlsanity.com/paper/2311.12089/) (2023)
- [A Framework For Gait-Based User Demography Estimation Using Inertial Sensors](https://mlsanity.com/paper/2402.09761/) (2024)
- [Value Prediction for Spatiotemporal Gait Data Using Deep Learning](https://mlsanity.com/paper/2403.07926/) (2024)
- [The Role of Explainable AI in Revolutionizing Human Health Monitoring: A Review](https://mlsanity.com/paper/2409.07347/) (2024)
- [Marker-free Human Gait Analysis using a Smart Edge Sensor System](https://mlsanity.com/paper/2411.09538/) (2024)
- [BiomechGPT: Extending Motion-Language Models to Clinical Motion Understanding](https://mlsanity.com/paper/2505.18465/) (2025)
- [Understanding the Representation of Older Adults in Motion Capture Locomotion Datasets](https://mlsanity.com/paper/2511.11713/) (2025)

## Abstract (arXiv)

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.