@inproceedings{rind_trustworthy_2022, title = {Trustworthy {Visual} {Analytics} in {Clinical} {Gait} {Analysis}: {A} {Case} {Study} for {Patients} with {Cerebral} {Palsy}}, isbn = {978-1-66549-356-7}, url = {https://arxiv.org/abs/2208.05232}, doi = {10.1109/TREX57753.2022.00006}, abstract = {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.}, booktitle = {Proc. 2022 {IEEE} {Workshop} on {TRust} and {EXpertise} in {Visual} {Analytics} ({TREX})}, publisher = {IEEE}, author = {Rind, Alexander and Slijepcevic, Djordje and Zeppelzauer, Matthias and Unglaube, Fabian and Kranzl, Andreas and Horsak, Brian}, year = {2022}, note = {Projekt: SoniVis Projekt: ReMoCap-Lab Projekt: I3D}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Data Science, Departement Medien und Digitale Technologien, Department Gesundheit, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Human-Computer Interaction, Institut für Creative Media Technologies, Machine Learning, SP CDHSI Motor Rehabilitation, Schriftpublikation, Visual Computing, Visualization, Vortrag, Wiss. Beitrag, best, best-arind, peer-reviewed}, pages = {7--15}, }