2022
DOI: 10.32473/flairs.v35i.130539
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Extracting Sections of Simulated Driving Routes that Elicit Driving Responses Predictive of ADHD via Recursively Constructed Ensembles

Abstract: In this paper we introduce a novel algorithm called Iterative Section Reduction (ISR) to automatically identify spatial regions wherein time series were recorded 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 i… Show more

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Cited by 2 publications
(2 citation statements)
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“…We use an LSTM model as a deep learning method that can capture complex time-series relationships between the sensors and cognitive function. An LSTM model is also used in [26] to estimate drivers with ADHD.…”
Section: E Machine Learning Algorithmmentioning
confidence: 99%
“…We use an LSTM model as a deep learning method that can capture complex time-series relationships between the sensors and cognitive function. An LSTM model is also used in [26] to estimate drivers with ADHD.…”
Section: E Machine Learning Algorithmmentioning
confidence: 99%
“…We use an LSTM model as a deep learning method that can capture complex time-series relationships between the sensors and cognitive function. An LSTM model is also used in [26] to predict an drivers with ADHD.…”
Section: Feature Extractionmentioning
confidence: 99%