Name
Abstract | ||
|---|---|---|
Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in ubiquitous computing environment. This paper presents an acceleration-based gesture recognition approach, called FDSVM ( Frame-based Descriptor and multi-class SVM), which needs only a wearable 3-dimensional accelerometer. With FDSVM, firstly, the acceleration data of a gesture is collected and represented by a frame-based descriptor, to extract the discriminative information. Then a SVM-based multi-class gesture classifier is built for recognition in the nonlinear gesture feature space. Extensive experimental results on a data set with 3360 gesture samples of 12 gestures over weeks demonstrate that the proposed FDSVM approach significantly outperforms other four methods: DTW, Naïve Bayes, C4.5 and HMM. In the user-dependent case, FDSVM achieves the recognition rate of 99.38% for the 4 direction gestures and 95.21% for all the 12 gestures. In the user-independent case, it obtains the recognition rate of 98.93% for 4 gestures and 89.29% for 12 gestures. Compared to other accelerometer-based gesture recognition approaches reported in literature FDSVM gives the best resulrs for both user-dependent and user-independent cases. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02830-4_4 | UIC |
Keywords | Field | DocType |
acceleration-based gesture recognition approach,gesture sample,3-d accelerometer,nonlinear gesture feature space,svm-based multi-class gesture classifier,user-independent case,recognition rate,accelerometer-based gesture recognition,frame-based descriptor,gesture recognition,literature fdsvm,direction gesture,feature space,3 dimensional,human computer interaction | Computer vision,Feature vector,Activity recognition,Naive Bayes classifier,Computer science,Gesture,Support vector machine,Gesture recognition,Speech recognition,Sketch recognition,Artificial intelligence,Hidden Markov model | Conference |
Volume | ISSN | Citations |
5585 | 0302-9743 | 104 |
PageRank | References | Authors |
4.68 | 15 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiahui Wu | 1 | 194 | 14.61 |
| Gang Pan | 2 | 1501 | 123.57 |
| Daqing Zhang | 3 | 3619 | 217.31 |
| Guande Qi | 4 | 418 | 19.45 |
| Shijian Li | 5 | 1155 | 69.34 |