Print Email Facebook Twitter Understanding Tooth Removal Procedures using Feature Engineering and Classification Models Title Understanding Tooth Removal Procedures using Feature Engineering and Classification Models Author de Graaf, Willem (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Cognitive Robotics) Contributor Kober, J. (mentor) Van Riet, T.C.T. (mentor) Harlaar, J. (graduation committee) Mazhar, O. (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering Date 2020-11-19 Abstract Tooth removal is one of the most performed surgical procedures worldwide. Despite the high amount of tooth removal procedures carried out each year, scientific understanding of these procedures is not present. Knowledge of force and torque behaviour is limited and knowledge about movements has never been subject to scientific research before. This study is an initial attempt to describe the factors that influence tooth removal in terms of forces, torques and movements. In-vitro measurements were performed that resulted in a dataset containing force, torque and movement time series of 181 human demonstrations of tooth extractions. This report showed how feature engineering and classification modelling were employed to find tooth removal explaining parameters in the dataset. The feature engineering process led to numerical features describing the force and movement (rotation) time series. The rotation features were found to be most descriptive in describing differences in tooth removal procedures. This introduced five distinct rotation strategies that grouped the human demonstrations based on similarity of extraction strategy. These groups have been used as classification labels in the supervised learning process. A Naive Bayes algorithm and a Logistic Regression algorithm were implemented as prediction models. These models showed that while the rotation features contributed the most to the prediction performance, there was need for additional force features to reach maximum prediction performance. The results showed how feature engineering and classification modelling are the first steps in understanding the procedure of tooth removal. Subject Feature EngineeringClassificationTooth RemovalUnderstanding To reference this document use: http://resolver.tudelft.nl/uuid:ce52aeb0-0018-4729-924a-61c3c6bef38f Part of collection Student theses Document type master thesis Rights © 2020 Willem de Graaf Files PDF Thesis_WdG_4617533_final.pdf 20.94 MB Close viewer /islandora/object/uuid:ce52aeb0-0018-4729-924a-61c3c6bef38f/datastream/OBJ/view