UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Probability-weighted ensemble support vector machine for intelligent recognition of moving wear debris from joint implant

Peng, Yeping; Yue, Hongtao; Wang, Song; Cao, Guangzhong; Wu, Hongkun; Liu, Chaozong; (2023) Probability-weighted ensemble support vector machine for intelligent recognition of moving wear debris from joint implant. Tribology International , 186 , Article 108583. 10.1016/j.triboint.2023.108583. Green open access

[thumbnail of Liu_Tribology-converted.pdf]
Preview
Text
Liu_Tribology-converted.pdf

Download (1MB) | Preview

Abstract

Friction-induced wear debris from joint implants are effective resources in investigating artificial joint wear and cellular immune response mechanisms. To improve the accuracy of wear debris analysis, an intelligent recognition method is developed for wear debris measurement under motion conditions. In this method, the multi-view image sequence of moving wear debris is captured to acquire the variations of aspect ratio, area, and roundness features. Then multiple SVM models are integrated to identify wear debris types based on weighted probability to improve the accuracy. The proposed method can achieve a classification accuracy of 90.51%, which is better than HIVE-COTE2.0, MultiRocket, and other time series classification algorithms. This method can be applied to monitor wear status of artificial joint articulating surfaces.

Type: Article
Title: Probability-weighted ensemble support vector machine for intelligent recognition of moving wear debris from joint implant
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.triboint.2023.108583
Publisher version: https://doi.org/10.1016/j.triboint.2023.108583
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Artificial joint, CLASSIFICATION, Engineering, Engineering, Mechanical, EXTRACTION, HIP, IMAGES, Intelligent recognition, Moving wear debris, PARTICLES, Probability -weighted ensemble SVM, RECONSTRUCTION, Science & Technology, SHAPE, Technology
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Ortho and MSK Science
URI: https://discovery.ucl.ac.uk/id/eprint/10174906
Downloads since deposit
3Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item