sebastian-lapuschkin/explaining-deep-clinical-gait-classification
Code and Data used for the paper "Explaining Machine Learning Models for Clinical Gait Analysis"
This helps clinical gait analysis experts understand why a machine learning model classifies a patient's gait in a particular way. It takes patient gait data (time series) and outputs explanations for the model's classification, highlighting which parts of the gait pattern were most important for the diagnosis. This is useful for biomechanical researchers, physiotherapists, and clinicians who use automated gait analysis.
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Use this if you need to understand the reasoning behind an AI's classification of gait patterns, rather than just getting a diagnostic label.
Not ideal if you are looking for a ready-to-use diagnostic tool, as this focuses on explaining existing model predictions, not creating new ones.
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Language
Python
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Last pushed
Dec 22, 2021
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