Resumen
The purpose of this article is two-fold : (1) to select a set of bio-mechanical features to characterize arthroplasty candidates and, (2) design a surgical and non-surgical candidate classifier via decision trees. The biomechanical features are generated from 3D knee kinematic patterns, namely, flexion-extension, abduction-adduction, and tibial internal-external rotation measurements taken during gait recordings. The selection of features is done by incremental selection of biomechanical parametes in a classification tree of cross-sectional data. These features are then used to generate decision rules for classification. The effectiveness of the classifier is evaluated by receiver operating characteristic curve analysis, namely, the area under the curve (AUC), sensitivity, and specificity. The classification accuracy is 85% for AUC, 80% for sensitivity, and 90% for specificity. These results demonstrate the effectiveness of the selected biomechanical features and decision tree classifier to perform automatic and objective classification of surgical and non-surgical candidates for arthroplasty.
Idioma original | English |
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Título de la publicación alojada | 2016 International Symposium on Signal, Image, Video and Communications, ISIVC 2016 |
Editorial | Institute of Electrical and Electronics Engineers Inc. |
Páginas | 287-290 |
Número de páginas | 4 |
ISBN (versión digital) | 9781509036110 |
DOI | |
Estado | Published - abr. 6 2017 |
Evento | 2016 International Symposium on Signal, Image, Video and Communications, ISIVC 2016 - Tunis, Tunisia Duración: nov. 21 2016 → nov. 23 2016 |
Serie de la publicación
Nombre | 2016 International Symposium on Signal, Image, Video and Communications, ISIVC 2016 |
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Conference
Conference | 2016 International Symposium on Signal, Image, Video and Communications, ISIVC 2016 |
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País/Territorio | Tunisia |
Ciudad | Tunis |
Período | 11/21/16 → 11/23/16 |
Nota bibliográfica
Publisher Copyright:© 2016 IEEE.
ASJC Scopus Subject Areas
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Signal Processing