IntroductionThe aim of this study was to demonstrate and explore the ability of novel game-based perimetry to establish normal visual field thresholds in children.MethodsOne hundred and eighteen children (aged 8.0 ± 2.8 years old) with no history of visual field loss or significant medical history were recruited. Each child had one eye tested using a game-based visual field test ‘Caspar’s Castle’ at four retinal locations 12.7° (N = 118) from fixation. Thresholds were established repeatedly using up/down staircase algorithms with stimuli of varying diameter (luminance 20 cd/m2, duration 200 ms, background luminance 10 cd/m2). Relationships between threshold and age were determined along with measures of intra- and intersubject variability.ResultsThe Game-based visual field test was able to establish threshold estimates in the full range of children tested. Threshold size reduced with increasing age in children. Intrasubject variability and intersubject variability were inversely related to age in children.ConclusionsNormal visual field thresholds were established for specific locations in children using a novel game-based visual field test. These could be used as a foundation for developing a game-based perimetry screening test for children.
Associativity between feature models implies the automatic updating of different feature models of a part after changes are made in one of its feature models. This is an important requirement in a distributed and concurrent design environment, where integrity of part geometry has to be maintained through changes made in different task domains. The proposed algorithm takes multiple feature models of a part as input and modifies other feature models to reflect the changes made to a feature in a feature model. The proposed algorithm updates feature volumes in a model that has not been edited and then classifies the updated volumes to obtain the updated feature model. The spatial arrangement of feature faces and adjacency relationship between features are used to isolate features in a view that are affected by the modification. Feature volumes are updated based on the classification of the feature volume of the modified feature with respect to feature volumes of the model being updated. The algorithm is capable of handling all types of feature modifications namely, feature deletion, feature creation, and changes to feature location and parameters. In contrast to current art in automatic updating of feature models, the proposed algorithm does not use an intermediate representation, does not re-interpret the feature model from a low level representation and handles interacting features. Results of implementation on typical cases are presented.
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