Abstract
In this paper we introduce a novel technique for modeling and recognizing gesture signals in 2D space. This technique is based on measuring the direction of the gradient of the movement trajectory as features of the gesture signal. Each gesture signal is represented as a time series of gradient angle values. These features are classified by applying a given classification method. In this article we compared the accuracy of a feed forward Artificial Neural Network with a Support Vector Machine using a radial kernel. The comparison was based on the recorded data of 13 gesture signals as training and testing data. The average accuracy of the ANN and SVM were 98.27% and 96.34% respectively. The false detection ratio was 3.83% for ANN and 8.45% for SVM, which suggests the ANN is more suitable for gesture signal recognition.
Original language | English |
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Title of host publication | Procedings - 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006 |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 701-704 |
Number of pages | 4 |
ISBN (Print) | 0769527280, 9780769527284 |
DOIs | |
Publication status | Published - 2006 |
Externally published | Yes |
Event | International Conference on Tools with Artificial Intelligence 2006 - Arlington, United States of America Duration: 13 Oct 2006 → 15 Oct 2006 Conference number: 18th |
Conference
Conference | International Conference on Tools with Artificial Intelligence 2006 |
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Abbreviated title | ICTAI 2006 |
Country/Territory | United States of America |
City | Arlington |
Period | 13/10/06 → 15/10/06 |