# Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy

arXiv: https://arxiv.org/abs/2208.05232 · HTML: https://mlsanity.com/paper/2208.05232/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 10 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [On the Explanation of Machine Learning Predictions in Clinical Gait Analysis](https://mlsanity.com/paper/1912.07737/) (2019)
- [Automatic Classification of Functional Gait Disorders](https://mlsanity.com/paper/1712.06405/) (2017)
- [Motion Browser: Visualizing and Understanding Complex Upper Limb Movement Under Obstetrical Brachial Plexus Injuries](https://mlsanity.com/paper/1907.09146/) (2019)
- [KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis](https://mlsanity.com/paper/1707.06105/) (2017)

## Built on by (most cited first, 2 of 2)

- [Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running, and Sports Movements](https://mlsanity.com/paper/2503.03717/) (2025)
- [3D Digital Twin Visualization of Multiclass GRF-Based Gait Disorder Classification](https://mlsanity.com/paper/2609.12442/) (2026)

## Abstract (arXiv)

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.