使用生理反应对执法人员的认知工作负载进行分类
David Wozniak1, Maryam Zahabi1
1Wm Michael Barnes '64 Department of Industrial & Systems Engineering, Texas A&M University, College Station, TX, USA.
Applied ergonomics
|May 11, 2024
概括
一个机器学习算法使用生理信号准确地预测警察的认知工作量. 这项技术可以增强车载系统,防止巡逻行动期间发生致命事故.
科学领域:
- 交通安全研究 交通安全研究
- 执法部门的运营效率 执法部门的运营效率
- 人与计算机在车辆中的互动.
背景情况:
- 机动车事故 (MVC) 是美国执法人员 (LEO) 死亡的主要原因.
- 新手LEO (nLEO) 在驾驶时经历了显著的认知工作负载,增加了碰撞风险.
- 在巡逻行动期间的次要任务加剧了LEO的认知负载.
研究的目的:
- 开发一个机器学习算法 (MLA) 来估计LEO认知工作负载.
- 评估使用生理数据进行实时工作负载监控的可行性.
- 为增强军官安全提供适应性车载系统的设计信息.
主要方法:
- 进行了一项涉及24名初学者LEO (nLEO) 的随行研究.
- 使用不引人注目的传感器记录了生理反应 (心率,眼睛运动,电动皮肤反应).
- 采用随机森林算法来分析生理数据并预测认知工作量.
主要成果:
- 随机森林MLA在预测认知工作负载方面取得了超过70%的准确性.
- 该算法的性能仅基于生理信号数据.
- 这表明了生理监测对工作负载评估的潜力.
结论:
- 一个经过验证的MLA可以实时估计LEO认知工作负载.
- 这项技术可以使自适应车载系统能够减轻风险.
- 减少认知工作量可以降低执法部门致命MVC的发生率.
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