The Dissimilarity-Consensus Tool &
Children Whole-Body Gestures Dataset
This tool implements the dissimilarity-consensus (τ-C) method described in Vatavu (2019), an approach to computing objective measures of consensus (or agreement) between users' gesture preferences
to support data analysis in end-user gesture elicitation studies (Wobbrock et al., 2009). The τ-C method models and quantifies the relationship between users' consensus over gesture articulation
and numerical measures of gesture dissimilarity, e.g., Dynamic Time Warping or Hausdorff distances, by employing growth curves and logistic functions.
Growth curves, such as those illustrated in the figure below, quantify the numerical relationship between consensus (C) and the tolerance in gesture dissimilarity (τ), below which
two gestures are considered similar in terms of their articulations.
Publications
Resources
Supplementary data consisting of illustrations of growth curves and logistic models for the experimental conditions not presented in the CHI paper regarding various dissimilarity functions and aggregators are available
here .
Our gesture dataset composed of 1312 whole-body gestures elicited from 30 children, ages 3 to 6 years, is freely available to download for research purposes: dataset.rar .
Our C# and R code implements the dissimilarity-consensus (τ-C) method described in Vatavu (2019) and represents the companion application to our children gestures dataset. The application reads the dataset, visualizes the gestures, computes growth curves and logistic models, and produces the plot visualizations from the paper by running R code. To read and visualize the dataset, the .NET Framework Runtime needs to be installed. To compute logistic curves, R must be installed as well and the path to Rscript.exe must be added to the system PATH environment variable. The video below demonstrates how to use the application: