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A New Soft Tissue Artifact Compensation Technique in Human Motion Analysis and Clinical Applications

出版	University of Florida, 2009
URL	http://books.google.com.hk/books?id=czmxAQAACAAJ&hl=&source=gbs_api

註釋ABSTRACT: Human motion analysis plays an important role in understanding normal function as well as pathological abnormalities of human musculoskeletal systems. Among different motion analysis techniques, skin marker-based stereophotogrammetry is the one used most widely in the biomechanical community. A major limitation of this technique is that motion-tracking markers are attached to skin surface of body segments and these markers can move relative to the underlying bone during activities. The relative movement between skin markers and the underlying bones is usually referred to as soft tissue artifact (STA) and it has been proved to be a major source of error of the technique. Much effort has been devoted by the research community to developing techniques to compensate for STA effects and improve motion analysis accuracy. However, the problem has not yet been solved satisfactorily. In the framework of this dissertation, a new STA compensation method was developed based on in vivo soft tissue movements and inter-subject similarities. First, it was demonstrated that soft tissue deformation on the lower extremity has inter-subject similarities, which was a new insight contrary to the prevailing opinion. Second, a simultaneous fluoroscopy and stereophotogrammetry study was conducted to assess STA in vivo on six subjects who had total knee arthroplasty (TKA), during a series of knee flexion movements and a step-up activity. Both inter-subject similarity and inter-motor-task similarity were observed on the STA results. Based on these similarities, a "universal" STA model was constructed using multilinear regression on the STA measurements obtained from multiple subjects and multiple activities. Third, from the "universal" STA model, a new STA compensation concept was implemented in two methods: an STA deduction (STAD) method and a directional weighted optimization (DWO) method