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Bundled assessment to replace on-road test on driving function in stroke patients: a binary classification model via

Lu Huang1,2, Xin Liu3, Jiang Yi2

  • 1School of Nursing, Jilin University, Changchun, China.

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Summary

A new bundled assessment model accurately predicts stroke patients' driving ability, integrating cognitive, eye-tracking, and motor skills. This model can potentially replace on-road tests for evaluating driving function in stroke survivors.

Keywords:
drivingeye-trackingmotor-cognitive functionsrandom foreststroke

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Area of Science:

  • Neurology
  • Rehabilitation Medicine
  • Biomedical Engineering

Background:

  • Stroke survivors often face challenges in regaining driving ability.
  • Traditional on-road driving tests may not fully capture the complexities of driving function after stroke.
  • Objective assessment tools are needed to evaluate driving fitness in this population.

Purpose of the Study:

  • To develop and validate a predictive model for on-road driving test success in stroke patients.
  • To establish a bundled assessment integrating cognitive, ocular, and motor functions.
  • To explore the utility of a random forest algorithm for classifying driving ability.

Main Methods:

  • Collected clinical data from 38 stroke patients, including cognitive (Oxford Cognitive Screen), eye-tracking, motor (Fugl-Meyer Assessment-lower extremity, ankle strength, AROM), and simulated driving performance.
  • Classified patients into Success or Unsuccess groups based on on-road test outcomes.
  • Utilized a random forest algorithm to build a binary classification model.

Main Results:

  • The Success group showed better cognitive scores, distinct eye-tracking patterns (reduced pupil change, more fixations, longer fixation duration, faster saccade velocity), and superior motor function (higher FMA-LE, ankle strength, AROM).
  • Simulated driving performance revealed fewer errors (collisions, lane violations, incorrect maneuvers) in the Success group.
  • The random forest model achieved >83% accuracy in predicting on-road test outcomes using 56 distinct input variables.

Conclusions:

  • A bundled assessment incorporating cognitive, eye-tracking, motor, and simulated driving data can effectively predict on-road driving test success in stroke patients.
  • The developed random forest model demonstrates significant potential for clinical application in assessing driving fitness post-stroke.
  • This approach offers a promising alternative to traditional on-road testing for evaluating driving function.