Driving and Post‐Concussion Changes in Neuropsychological Performance

Schultheis, Maria T.; Ang, Jocelyn Y.; Blake, Taylor; Bradley, Sandella J.; Hong, Gene; Martin, Danielle; Ricasa, Jocelyn C.; Sunderaraman, Preeti

doi:10.1016/j.pmrj.2012.09.615

Cited by 1 publication

(2 citation statements)

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“…This study is a part of a broader research effort examining driving performance across several different groups of participants and was measured with a virtual reality driving simulator (VRDS) developed by Digital MediaWorks Inc. (see Schultheis et al, 2012). In our study, we used the dataset analyzed in previous work (Guinosso et al, 2016;Schultheis et al, 2012) which examines the driving records of twenty-two individuals 1-4 days after a concussion, and twenty-two age and gendermatched healthy controls who completed the same driving task. Table 1 provides a detailed breakdown of the participants in the two studies.…”

Section: Experimental Datamentioning

confidence: 99%

“…More specifically, to better understand the driving behavior of participants, we used a dataset gathered from another study (Schultheis et al, 2012) that captured driving of a concussed group while performing behavioral tasks and is matched with a healthy control driving and performing the same tasks (Guinosso et al, 2016). In this study, we employ a model that uses sequences of several measures captured in the simulation environment and apply a deep convolutional neural network both to extract features and to classify the data.…”

Section: Introductionmentioning

confidence: 99%

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Understanding Driver Behavior after Concussion: A Machine-Learning Approach

Daniali

Salvucci

Schultheis

2020

Proceedings of the Human Factors and Ergonomics Society Annual

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Concussions are common cognitive impairments, but their effects on task performance in general, and on driving in particular, are not well understood. To better understand the effects of concussion on driving, we investigated previously gathered data on twenty-two people with a concussion, driving in a virtual-reality driving simulator (VRDS), and twenty-two non-concussed matched drivers. Participants were asked to per-form a behavioral task (either coin sorting or a verbal memory task) while driving. In this study, we chose a few common metrics from the VRDS and tracked their changes through time for each participant. Our pro-posed method—namely, the use of convolutional neural networks for classification and analysis—can accu-rately classify concussed driving and extract local features on driving sequences that translate to behavioral driving signatures. Overall, our method improves identification and understanding of clinically relevant driv-ing behaviors for concussed individuals and should generalize well to other types of impairments.

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Section: Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%