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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 promising results. The current study examined the influence of different input representations on a trained model’s accuracy, interpretability, as well as clinical relevancy using XAI methods. The gait of 27 healthy subjects and 20 subjects after total hip arthroplasty (THA) was recorded with an inertial measurement unit (IMU)-based system. Three different input representations were used for classification. Local Interpretable Model-Agnostic Explanations (LIME) was used for model interpretation. The best accuracy was achieved with automatically extracted features (mean accuracy Macc = 100%), followed by features based on simple descriptive statistics (Macc = 97.38%) and waveform data (Macc = 95.88%). Globally seen, sagittal movement of the hip, knee, and pelvis as well as transversal movement of the ankle were especially important for this specific classification task. The current work shows that the type of input representation crucially determines interpretability as well as clinical relevance. A combined approach using different forms of representations seems advantageous. The results might assist physicians and therapists finding and addressing individual pathologic gait patterns

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Author:Carlo DindorfORCiD, Wolfgang TeuflORCiD, Bertram TaetzORCiD, Gabriele BlaeserORCiD, Michael FröhlichORCiD
URN (permanent link):urn:nbn:de:hbz:386-kluedo-61593
ISSN:1424-8220
Parent Title (English):Sensors
Publisher:MDPI
Document Type:Article
Language of publication:English
Publication Date:2020/08/06
Year of Publication:2020
Publishing Institute:Technische Universität Kaiserslautern
Date of the Publication (Server):2020/12/16
Issue:2020, 20(16)
Number of page:14
Source:https://www.mdpi.com/1424-8220/20/16/4385
Faculties / Organisational entities:Fachbereich Sozialwissenschaften
DDC-Cassification:7 Künste und Unterhaltung, Architektur, Raumplanung / 796 Sport
Collections:Open-Access-Publikationsfonds
Licence (German):Zweitveröffentlichung