ADHD Prediction via Time Series Ensemble fed Driving Simulator Data

Authors

DOI:

https://doi.org/10.32473/flairs.v34i1.128531

Keywords:

Driving Simulator, Time Series, Explainable Results, Player Modelling, DTW, k-NN, 1-D CNN, Ensembles

Abstract

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.

Author Biography

David Grethlein, Drexel University

PhD Candidate in the Computer Science Department at Drexel University.

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Published

2021-04-18

How to Cite

Grethlein, D., Sladek, A., & Ontañón, S. (2021). ADHD Prediction via Time Series Ensemble fed Driving Simulator Data. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128531

Issue

Section

Main Track Proceedings