Name
Abstract | ||
|---|---|---|
Autism spectrum disorder (ASD) is a serious neurodevelopmental disorder that impairs a child's ability to communicate and interact with others. Usually, recognizing a child with ASD needs the diagnosis by pediatric psychiatrists. However, it is not only expensive and time-consuming, but also the results are influenced by subjective factors, such as the experience of a doctor. In this paper, we propose a novel method to automatically recognize ASD children in raw video data. Firstly, we use an eye tracking method to obtain the trajectory of eye movement. Then, accumulative histogram is introduced to analyze these trajectories. Afterwards, dimension reduction is applied to reducing the histogram dimension. Finally, support vector machine is used for classification. Since it is hardly to find public videos of autism, we collect a video dataset containing 189 videos captured from 53 ASD children and 136 typically developing children. Experimental results on our dataset show a high classification accuracy of 93.7
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>
, which demonstrates our method can effectively help recognize ASD children in a more efficient way. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICPR.2018.8545113 | 2018 24th International Conference on Pattern Recognition (ICPR) |
Keywords | Field | DocType |
Autism spectrum disorder,Accumulative histogram,Dimension reduction,Classification | Autism,Histogram,Dimensionality reduction,Pattern recognition,Computer science,Support vector machine,Feature extraction,Speech recognition,Eye movement,Eye tracking,Artificial intelligence,Autism spectrum disorder | Conference |
ISSN | ISBN | Citations |
1051-4651 | 978-1-5386-3789-0 | 1 |
PageRank | References | Authors |
0.39 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Li | 1 | 3 | 2.44 |
| Yihao Zhong | 2 | 1 | 0.39 |
| Gaoxiang Ouyang | 3 | 4 | 1.49 |