July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Predicting early hazard detection from head scanning magnitude in individuals with hemianopia
Author Affiliations & Notes
  • Garrett Swan
    Schepens Eye Research Institute of Mass Eye and Ear, Dept Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States
  • Aliakbar Ahmadi
    Schepens Eye Research Institute of Mass Eye and Ear, Dept Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States
  • Alex R Bowers
    Schepens Eye Research Institute of Mass Eye and Ear, Dept Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Garrett Swan, None; Aliakbar Ahmadi, None; Alex Bowers, None
  • Footnotes
    Support  R01-EY025677, S10-RR028122, P30-EY003790
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 4258. doi:
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      Garrett Swan, Aliakbar Ahmadi, Alex R Bowers; Predicting early hazard detection from head scanning magnitude in individuals with hemianopia. Invest. Ophthalmol. Vis. Sci. 2019;60(9):4258.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Individuals with hemianopic field loss (HFL) can legally drive when the minimum field requirement is less than 90°, but the HFL may impair detection of hazards on the blind side posing a risk to all road users. Drivers with HFL could compensate by scanning toward the blind side; yet, prior studies suggest they often do not scan sufficiently far when large gaze (head + eye) scans are necessary e.g., 90° to the blind side when approaching an intersection. Given that head movements are easier to track than eye movements in on-road driving, we conducted a study utilizing a high-fidelity driving simulator to determine whether head scan rather than gaze scan magnitude could be used to predict early detection of hazards appearing in the periphery.

Methods : Ten subjects with complete HFL drove through 4 routes in a virtual city while their eyes and head were tracked (6-camera SmartEye remote tracker). Participants were instructed to obey traffic rules and press the horn as soon as they detected a motorcycle (MC: n = 10 per drive) that appeared at 60° eccentricity on the left or right approaching along a cross street on a collision course at four-way (+) intersections.

Results : Early MC detection rates were significantly worse on the blind than the seeing side (20% vs. 77%; p < 0.001). As expected, gaze scans were on average larger in the blind than seeing side for early detections (50.1° vs. 22.4°; p < 0.001) suggesting that MCs could be detected in peripheral vision on the seeing side but had to be directly fixated on the blind side. Head scan magnitude was highly correlated with gaze scan magnitude on the blind side (r = 0.9) and significantly predicted early detection for MCs on the blind (p < 0.001) but not the seeing side (p = 0.53). The minimum head scan magnitude that best predicted MC detection on the blind side was 20.9° (accuracy: 90%) using a machine learning classification tree.

Conclusions : These results suggest that head scan magnitude predicts early detection of hazards that appear in the blind, but not the seeing side. This difference is likely driven by individuals using peripheral vision in the seeing side, but needing to use large head and eye movements to see hazards in their blind side. The minimum head scan magnitude value may be a useful threshold for training compensatory strategies or for developing assistive technologies.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

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