David E. Patterson

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Recently, the framework of the uncontrolled manifold (UCM) hypothesis has been used to study multi-finger synergies based on analysis of motor variability across large sets of trials. We introduce a similar method of analysis, which can be applied to single trials, and hence may be more relevant to studies of atypical populations. In one experiment, results(More)
The study addresses an issue of possible relations between the apparent "clumsiness" of persons with Down syndrome (DS) and changes in indices of finger coordination. We hypothesized that persons with DS would prefer less challenging, safer motor strategies reflected in finger coordination patterns. Maximal single- and multi-finger force production (MVC)(More)
We describe an interactive method to generate a set of fuzzy clusters for classes of interest of a given, labeled data set. The presented method is therefore best suited for applications where the focus of analysis lies on a model for the minority class or for small-to medium-size data sets. The clustering algorithm creates one-dimensional models of the(More)
BACKGROUND Previous trials of heart failure telemonitoring systems have produced inconsistent findings, largely due to diverse interventions and study designs. OBJECTIVES The objectives of this study are (1) to provide in-depth insight into the effects of telemonitoring on self-care and clinical management, and (2) to determine the features that enable(More)
We describe an interactive way to generate a set of clusters for a given data set. The clustering is done by constructing local histograms, which can then be used to visualize , select, and fine-tune potential cluster candidates. The accompanying algorithm can also generate clusters automatically , allowing for an automatic or semi-automatic clustering(More)
Often, it is desirable to represent a set of time series through typical shapes in order to detect common patterns. The algorithm presented here compares pieces of a different time series in order to find such similar shapes. The use of a fuzzy clustering technique based on fuzzy c-means allows us to detect shapes that belong to a certain group of typical(More)