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Despite the high number of total hip arthroplasty (THA) procedures performed each year, there is no common consensus on the best surgical approach. Gait is known to improve following THA although it does not return to what is typically quantified as normal, and surgical approach is believed to be a contributing factor. The current study evaluates postoperative hip function and provides an objective assessment following two common surgical approaches: the McFarland-Osborne direct lateral and the southern posterior. Faced with the common problem of providing an objective comparison from the wealth of data collected using motion analysis techniques, the current study investigates the application of an objective classification tool to provide information on the effectiveness of each surgery and to differentiate between the characteristics of hip function following the two approaches. Seven inputs for the classifier were determined through statistical analysis of the biomechanical data. The posterior approach group exhibited greater characteristics of non-pathological gait and displayed a greater range of functional ability as compared with the lateral approach cohort. The classification tool has proved to be successful in characterizing non-pathological and THA function but was insufficient in distinguishing between the two surgical cohorts.

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There are certain major obstacles to using motion analysis as an aid to clinical decision making. These include: the difficulty in comprehending large amounts of both corroborating and conflicting information; the subjectivity of data interpretation; the need for visualization; and the quantitative comparison of temporal waveform data. This paper seeks to overcome these obstacles by applying a hybrid approach to the analysis of motion analysis data using principal component analysis (PCA), the Dempster-Shafer (DS) theory of evidence and simplex plots. Specifically, the approach is used to characterise the differences between osteoarthritic (OA) and normal (NL) knee function data and to produce a hierarchy of those variables that are most discriminatory in the classification process. Comparisons of the results obtained with the hybrid approach are made with results from artificial neural network analyses.