Identifying On-road Scenarios Predictive of ADHD usingDriving Simulator Time Series Data

TL;DR

This paper presents a new algorithm, ISR, that identifies predictive on-road scenarios from driving simulator data to distinguish ADHD patients from controls, improving classification accuracy and providing behavioral insights.

Contribution

The novel ISR algorithm automatically detects predictive sub-intervals in driving data, enhancing ADHD classification and revealing relevant on-road scenarios.

Findings

+10% accuracy improvement over previous methods

Identified predictive sections align with key driving events

Enhanced understanding of ADHD-related driving behaviors

Abstract

In this paper we introduce a novel algorithm called Iterative Section Reduction (ISR) to automatically identify sub-intervals of spatiotemporal time series that are predictive of a target classification task. Specifically, using data collected from a driving simulator study, we identify which spatial regions (dubbed "sections") along the simulated routes tend to manifest driving behaviors that are predictive of the presence of Attention Deficit Hyperactivity Disorder (ADHD). Identifying these sections is important for two main reasons: (1) to improve predictive accuracy of the trained models by filtering out non-predictive time series sub-intervals, and (2) to gain insights into which on-road scenarios (dubbed events) elicit distinctly different driving behaviors from patients undergoing treatment for ADHD versus those that are not. Our experimental results show both improved performance over prior efforts (+10% accuracy) and good alignment between the predictive sections identified and scripted on-road events in the simulator (negotiating turns and curves).