Feature selection for grasp recognition from optical markers (Chang 2007)

Summary:
The goal of this paper is to create a system which can learn how to automatically create grasps for a robot manipulator provided examples from a human demonstrator. The positions of a set of 3D markers placed on the back of the hand are used as an orientation-independant set of characteristics which are used to classify hand poses into one of six categories of grasps. Since a linear logistic regression classifier is sufficient to predict grasps, one goal of the paper is to reduce the number of markers while retaining recognition rates.
To bypass considering the exponential number of possible feature sets, two greedy sequential wrapper methods were used to evaluate the addition or removal of single features. The wrapper methods' goal was to achieve local optimization by considering which single feature to add or remove, while retaining the highest recognition rate. To achieve hand orientation independance, three of the 3D markers were attached to a rigid part of the back of the hand that would serve to orient all the other marker positions.
A data set pairing grasp type with marker positions for grasps of 46 objects was collected from a total of three subjects. Using all 30 markers resulted in an accuracy of 91.5%, while using only five resulted in an accuracy of 86%. The set of markers selected by the backward selection differed from the set of markers selected by forward selection, and had a higher recognition rate.

Discussion:
I thought using the ratio of the highest class probability to the second highest class probability as a measure of confidence was a good idea.
I think the choice to initially use 30 markers was artificially high. The experimenters probably expected a significant decrease in marker count without a great loss in grasp recognition accuracy, simply because they chose a high number of markers.