Can Anthropomorphic Interfaces Improve the Ergonomics and Safety Performance of Human-Machine Collaboration in Multitasking Scenarios?-An Example of Human-Machine Co-Driving in High-Speed Trains.

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Yunan Jiang, Jinyi Zhi
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Abstract

High-speed trains are some of the most important transportation vehicles requiring human-computer collaboration. This study investigated the effects of different types of icons on recognition performance and cognitive load during frequent observation and sudden takeover tasks in high-speed trains. The results of this study can be used to improve the efficiency of human-computer collaboration tasks and driving safety. In this study, 48 participants were selected for a simulated driving experiment on a high-speed train. The recognition reaction time, operation completion time, number of recognition errors, number of operation errors, SUS scale, and NASA-TLX questionnaire for the icons were all analyzed using analysis of variance (ANOVA) and the nonparametric Mann-Whitney U test. The results show that anthropomorphic icons can reduce the drivers' visual fatigue and mental load in frequent observation tasks due to the anthropomorphic facial features attracting driver attention through simple lines and improving visual search efficiency. However, for the sudden takeover human-computer collaboration task, the facial features of the anthropomorphic icons were not recognized in a short period of time. Additionally, due to the positive emotions produced by the facial features, the drivers did not perceive the suddenness and danger of the sudden takeover human-computer collaboration task, resulting in the traditional icons being more capable of arousing the drivers' alertness and helping them take over the task quickly. At the same time, neither type of icon triggered misrecognition or operation for sufficiently skilled drivers. These research results can provide guidance for the design of icons in human-computer collaborative interfaces for different types of driving tasks in high-speed trains, which can help improve the recognition speed, reaction speed, and safety of drivers.

拟人界面能改善多任务场景下人机协作的人机工程学和安全性能吗?——以高速列车人机协同驾驶为例。
高速列车是需要人机协作的最重要的交通工具之一。本研究考察了高速列车频繁观察和突发接管任务中不同类型图标对识别性能和认知负荷的影响。研究结果可用于提高人机协作任务的效率和驾驶安全。本研究选取48名参与者进行高速列车模拟驾驶实验。采用方差分析(ANOVA)和非参数Mann-Whitney U检验对图标的识别反应时间、操作完成时间、识别错误次数、操作错误次数、SUS量表和NASA-TLX问卷进行分析。结果表明,拟人化的人脸特征通过简单的线条吸引驾驶员的注意力,提高视觉搜索效率,可以减轻驾驶员在频繁观察任务中的视觉疲劳和心理负荷。然而,对于突然接管的人机协作任务,拟人化图标的面部特征在短时间内无法识别。此外,由于面部特征所产生的积极情绪,驾驶员没有察觉到突然接管人机协作任务的突发性和危险性,从而使传统图标更能唤起驾驶员的警觉性,帮助他们快速接管任务。同时,对于足够熟练的司机来说,这两种类型的图标都不会引发误认或操作。这些研究成果可以为高速列车不同类型驾驶任务的人机协同界面图标设计提供指导,有助于提高驾驶员的识别速度、反应速度和安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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