Feature selection for grasp classification
نویسنده
چکیده
Although the human hand is a complex biomechanical system, functional grasps may be described by small set of features. Supervised feature selection is used to evaluate the performance of reduced marker sets for grasp classification from motion capture data. Our reduced feature set maintains 85% grasp classification accuracy, compared to 90% accuracy from using the full 30 marker set. Using a linear classifier and as few as 5 surface markers allows for dramatic simplification of the experimental procedure and reduced computational cost for grasp classification.
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