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Engineering and electronics,.
| Content Provider | CiteSeerX |
|---|---|
| Author | Refice, Mario Savino, Michelina Adduci, Michele Caccia, Michele |
| Abstract | Abstract—Gestures represent an important channel of human communication, and they are “co-expressive ” with speech. For this reason, in human-machine interaction automatic gesture classification can be a valuable help in a number of tasks, like for example as a disambiguation aid in automatic speech recognition. Based on the hand gesture categorization proposed by D. McNeill in his reference works on gesture analysis, a new approach is here presented which classifies gestures using both their kinematic characteristics and their morphology stored as parameters of the templates pre-classified during the training phase of the procedure. In the experiment presented in this paper, an average of about 90 % of correctly classified gesture types is obtained, by using as templates only about 3 % of the total number of gestures produced by the subjects. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Total Number Disambiguation Aid Training Phase Abstract Gesture Hand Gesture Categorization Kinematic Characteristic Important Channel Human-machine Interaction Automatic Gesture Classification New Approach Automatic Speech Recognition Valuable Help Human Communication Gesture Type Gesture Analysis |
| Content Type | Text |
