Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527, Japan.
Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527, Japan.
J Neuroeng Rehabil. 2023 Oct 18;20(1):139. doi: 10.1186/s12984-023-01263-z.
People who were previously hospitalised with stroke may have difficulty operating a motor vehicle, and their driving aptitude needs to be evaluated to prevent traffic accidents in today's car-based society. Although the association between motor-cognitive functions and driving aptitude has been extensively studied, motor-cognitive functions required for driving have not been elucidated.
In this paper, we propose a machine-learning algorithm that introduces sparse regularization to automatically select driving aptitude-related indices from 65 input indices obtained from 10 tests of motor-cognitive function conducted on 55 participants with stroke. Indices related to driving aptitude and their required tests can be identified based on the output probability of the presence or absence of driving aptitude to provide evidence for identifying subjects who must undergo the on-road driving test. We also analyzed the importance of the indices of motor-cognitive function tests in evaluating driving aptitude to further clarify the relationship between motor-cognitive function and driving aptitude.
The experimental results showed that the proposed method achieved predictive evaluation of the presence or absence of driving aptitude with high accuracy (area under curve 0.946) and identified a group of indices of motor-cognitive function tests that are strongly related to driving aptitude.
The proposed method is able to effectively and accurately unravel driving-related motor-cognitive functions from a panoply of test results, allowing for autonomous evaluation of driving aptitude in post-stroke individuals. This has the potential to reduce the number of screening tests required and the corresponding clinical workload, further improving personal and public safety and the quality of life of individuals with stroke.
曾因中风住院的人可能难以操作机动车，为了防止当今以汽车为基础的社会中的交通事故，需要对他们的驾驶能力进行评估。尽管已经广泛研究了运动认知功能与驾驶能力之间的关系，但仍未阐明驾驶所需的运动认知功能。
在本文中，我们提出了一种机器学习算法，该算法通过稀疏正则化从对 55 名中风参与者进行的 10 项运动认知功能测试中获得的 65 个输入指标中自动选择与驾驶能力相关的指标。可以根据存在或不存在驾驶能力的输出概率确定与驾驶能力相关的指标及其所需的测试，从而为识别必须进行道路驾驶测试的对象提供证据。我们还分析了运动认知功能测试指标在评估驾驶能力方面的重要性，以进一步阐明运动认知功能与驾驶能力之间的关系。
实验结果表明，该方法在预测存在或不存在驾驶能力方面具有很高的准确性（曲线下面积为 0.946），并确定了一组与驾驶能力密切相关的运动认知功能测试指标。
该方法能够有效地从大量测试结果中准确揭示与驾驶相关的运动认知功能，从而能够对中风患者的驾驶能力进行自主评估。这有可能减少所需的筛选测试数量和相应的临床工作量，进一步提高个人和公共安全以及中风患者的生活质量。
Zotero 插件
