ADHD Prediction via Time Series Ensemble fed Driving Simulator Data
In this paper, we identify the on-road scenarios within a simulated driving environment where a group of clinical trial participants (n= 30) with and without Attention Deficit Hyper-activity Disorder (ADHD) drive perceivably different fromone another. We partition the simulated routes into smaller n...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2021-04-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Subjects: | |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/128531 |
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| Summary: | In this paper, we identify the on-road scenarios within a simulated driving environment where a group of clinical trial participants (n= 30) with and without Attention Deficit Hyper-activity Disorder (ADHD) drive perceivably different fromone another. We partition the simulated routes into smaller non-overlapping sections in order to determine which sections elicit behaviors that are predictive of ADHD. Then, we develop section-specific classifiers, which are used as voters in bagging ensemble classifiers. Our results show gains in classifying ADHD (increase in 5-fold average evaluation accuracy) over our previous efforts, as well as providing explainable evidence that driving behaviors indicative of ADHD tend to be exhibited in turns and curves. |
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| ISSN: | 2334-0754 2334-0762 |