Journal article
Pelvis feature extraction and classification of Cardiff body match rig base measurements for input into a knowledge-based system
Journal of Medical Engineering & Technology, Vol.36(8), pp.399-406
2012
PMID: 22946507
Abstract
The purpose of this paper is to determine whether it is possible to use an automated measurement tool to clinically classify clients who are wheelchair users with severe musculoskeletal deformities, replacing the current process which relies upon clinical engineers with advanced knowledge and skills. Clients' body shapes were captured using the Cardiff Body Match (CBM) Rig developed by the Rehabilitation Engineering Unit (REU) at Rookwood Hospital in Cardiff. A bespoke feature extraction algorithm was developed that estimates the position of external landmarks on clients' pelvises so that useful measurements can be obtained. The outputs of the feature extraction algorithms were compared to CBM measurements where the positions of the client's pelvis landmarks were known. The results show that using the extracted features facilitated classification. Qualitative analysis showed that the estimated positions of the landmark points were close enough to their actual positions to be useful to clinicians undertaking clinical assessments.
Details
- Title
- Pelvis feature extraction and classification of Cardiff body match rig base measurements for input into a knowledge-based system
- Authors
- Adam Partlow (Author) - University of GlamorganColin Gibson (Author) - Rookwood Hospital (United Kingdom)Janusz Kulon (Author) - University of GlamorganIan Wilson (Author) - University of GlamorganSteven Wilcox (Author) - University of Glamorgan
- Publication details
- Journal of Medical Engineering & Technology, Vol.36(8), pp.399-406
- Publisher
- Taylor & Francis
- Date published
- 2012
- DOI
- 10.3109/03091902.2012.712202
- ISSN
- 0309-1902; 1464-522X; 0309-1902
- PMID
- 22946507
- Organisation Unit
- Office of the Deputy Vice-Chancellor (Academic); University of the Sunshine Coast, Queensland
- Language
- English
- Record Identifier
- 99513871602621
- Output Type
- Journal article
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