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<lom:identifier>
  
<lom:catalog>DOI</lom:catalog>

  
<lom:entry>
  
<lom:langstring xml:lang="x-none">10.1109/TREX57753.2022.00006</lom:langstring>

  
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</lom:identifier>

  
<lom:title>
  
<lom:langstring xml:lang="en">Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy : Supplemental material</lom:langstring>

  
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<lom:langstring xml:lang="en">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.</lom:langstring>

  
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<lom:language>eng</lom:language>

  
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<lom:langstring xml:lang="en">Machine learning algorithms</lom:langstring>

  
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<lom:keyword>
  
<lom:langstring xml:lang="en">Visual analytics</lom:langstring>

  
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<lom:langstring xml:lang="en">Conferences</lom:langstring>

  
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<lom:keyword>
  
<lom:langstring xml:lang="en">Time series analysis</lom:langstring>

  
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<lom:keyword>
  
<lom:langstring xml:lang="en">Machine learning</lom:langstring>

  
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<lom:langstring xml:lang="en">Explainable AI</lom:langstring>

  
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<lom:datetime>2023-08-22T11:34:43.056Z</lom:datetime>

  
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<lom:vcard>BEGIN:VCARD
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N:Alexander;Rind;
FN:Rind Alexander
X-ORCID:https://orcid.org/0000-0001-8788-4600
END:VCARD</lom:vcard>

  
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<lom:vcard>BEGIN:VCARD
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N:Slijepcevic;Djordje;
FN:Djordje Slijepcevic
X-ORCID:https://orcid.org/0000-0002-2295-7466
END:VCARD</lom:vcard>

  
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<lom:vcard>BEGIN:VCARD
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N:Zeppelzauer;Matthias;
FN:Matthias Zeppelzauer
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<lom:vcard>BEGIN:VCARD
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N:Unglaube;Fabian;
FN:Fabian Unglaube
END:VCARD</lom:vcard>

  
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N:Kranzl;Andreas;
FN:Andreas Kranzl
END:VCARD</lom:vcard>

  
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<lom:vcard>BEGIN:VCARD
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N:Horsak;Brian;
FN:Brian Horsak
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