Visual Similarity of Pen Gestures

-A. Chris Long, Jr., James A. Landay, Lawrence A. Rowe, and Joseph Michiels

COMMENTS

1. Comment on Manoj's blog

SUMMARY

In this paper authors' goal to to study through experiments why users find gestures similar and in the process derive a predictive model for perceived gesture similarity which has a high correlation with actual observation. This model may be used as an advising tool by gesture designers. The author enlists the previous work in the field of Pen Input devices. Most relevant to authors' work is Attneave's extensive study in perceptual similarity. He found that logarithm of quantitative metric correlates with similarity. The authors conducted two experiments using the techniques of Multidimensional Scaling (MDS). MDS is a technique for reducing the number of dimensions of a data set, so that patterns can easily been seen by plotting data in 2 or 3 dimensions. In the first experiment a previously designer gesture set which varied widely in how people would perceive them was used. Participants were presented with all possible sets of three gestures (triad) and were asked to mark the one that seemed most different from others among each set. By plotting gestures generated by MDS, the authors were able to determine the features that contribute to similarity. By running regression analysis, the authors were able to derive a model of gesture similarity that correlated 0.74 with the reported gesture similarities. The MDS indicated that the optimal number of dimensions is 5. Some of the features correlated with perceived similarity were curviness, total absolute angle, density etc. Another surprising outcome of the experiment was that the participants seemed to be divided into 2 groups which had different perceptions of similarity of gestures. Experiment 2 was conducted to test the predictive power of the model derived in the first one. Three new gesture sets of nine gestures each were created. Each set achieved a variance in the one of features found to be correlated in the first experiment. 2 gestures from each of the 3 gesture sets were chosen and added to a fourth set to allow us to compare the three sets against each other. Each participant was shown all possible triads. The analysis shows that 3 was the optimal number of dimensions to be used. Meaning of these dimensions was not obvious as in the first experiment. The features that correlated with the dimentions were Log (aspect), total absolute abgle and density. the derived model had a correlation of 0.71 with observation. Based on correlation calculation (model derived from which experiment agrees with observation more), model derived from experiment 2 was found to be a better predictor. Future work could consst of extending these experiments. Participants could be asked to draw gestures and then mark dissimilarity.

DISCUSSION

This paper has impressed me because the emphasis is more on Human part of the human computer interaction. I could not identify any faults with the paper except for those pointed out by the author like- users were not made to draw the gestures before they could pick out the dissimilar one. Since eventually, users will have to draw the gestures. Consider this- I have two line gestures for scroll up and scroll down. For scroll up line gesture is drawn upwards and for scroll down it goes downwards. Users will feel this difference only when they actually draw these gestures and not by just looking at the pictures of these two seemingly similar gestures (even when the starting point is specified for each gesture).