Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty

Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty

Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explainable Artificial Intelligence (XAI) has shown prom...

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Bibliographic Details
Main Authors: Carlo Dindorf, Wolfgang Teufl, Bertram Taetz, Gabriele Bleser, Michael Fröhlich
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Sensors
Subjects:
explainable artificial intelligence
inertial measurement unit
machine learning
biomechanics
gait
total hip replacement
Online Access:https://www.mdpi.com/1424-8220/20/16/4385

Internet

https://www.mdpi.com/1424-8220/20/16/4385

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