Browsing by Subject "Decision Support Tools"

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    Creating Effective Decision Aids for Complex Tasks
    (Usability Professionals' Association, 2008-08) Hayes, Caroline C.; Akhavi, Farnaz
    Engineering design tasks require designers to continually compare, weigh, and choose among many complex alternatives. The quality of these selection decisions directly impacts the quality, cost, and safety of the final product. Because of the high degree of uncertainty in predicting the performance of alternatives while they are still just sketches on the drawing board, and the high cost of poor choices, mathematical decision methods incorporating uncertainty have long held much appeal for product designers, at least from a theoretical standpoint. Yet, such methods have not been widely adopted in practical settings. The goals of this work are to begin understanding why this is so and to identify future questions that may lead to solutions. This paper summarizes the results of several studies by the authors: two laboratory studies in which we asked product designers to use various mathematical models to compare and select design alternatives, and a set of ethnographic studies in which we observed product designers as they worked so that we could better understand their actual practices and needs during decision making. Based on these studies, we concluded that the mathematical models, as formulated, are not well suited to designers’ needs and approaches. We propose a research agenda for developing new approaches that combine decision theoretic and usercentered methods to create tools that can make product designers’ decision making work easi
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    Development and Testing of Decision Support Tools in Gait Analysis
    (2016-04) Rozumalski, Adam
    Objectives Clinical gait analysis, as commonly prescribed for children with Cerebral Palsy, is a complex set of procedures which include examining data from several sources. The tools developed with this project will use that data to provide robust, repeatable, evidence-based guidance to highlight the most effective treatments for children with CP. These tools will also supply objective measures that can be included in outcome analysis. Methods Several mathematical techniques are used to find patterns within the gait date including: singular value decomposition of kinematic and kinetic data to measure gait pathology; k-means cluster analysis of those results to find recurring patterns; principal components analysis of physical exam findings to relate the gait patterns to physical function; and non-negative matrix factorization of electromyography data to measure motor control. Results The decomposition and scaling of the kinematic and kinetic data resulted in a set of indexes that are able to quantify gait pathology. The k-means cluster analysis reveals that there are repeatable patterns within the gait pathology. These patterns are related to clinical findings as calculated from principal components analysis. Clinical interpretations of motor control can be quantified as muscle synergies using non-negative matrix factorization. Interpretation These tools have proven to provide important quantitative information on treatment outcomes. When implemented in routine clinical gait analysis, these tools have the ability to provide evidence based guidance in treatment decisions.