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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.

The purpose of this paper is to present the design and implementation of a novel rule-based algorithm for the classification of sitting postures in the sagittal plane. The research focused on individuals with severe musculoskeletal problems and, thus, specific requirements for posture and pressure management. Clients' body shapes were captured using the Cardiff Body Match system developed by the Rehabilitation Engineering Unit, Cardiff and Vale University Health Board. The algorithm consists of four main steps: the first step is the symmetry line detection, the second step involves the mathematical analysis of the curvature of the backrest profile, the third step is the sitting posture classification and the fourth step is the extraction of the geometric parameters from the curve. The results show the classification system was successful in identifying four types of curves characterizing sitting postures using local derivatives as curve descriptors with an overall accuracy of 93.9%.

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