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Identification Rate of Simple and Complex Tactile Alerts in MUM-T Setup

机译:MUM-T设置中简单和复杂触觉警报的识别率

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Vibro-tactile interfaces were proposed as an alternative to enhance human-machine communication in information-rich domains. The current study aims to examine the effectiveness of two levels of tactile alerts when combined with visual alerts, in MUM-T (Manned UnManned Teaming) setup. In MUM-T, aside from their primary mission, mounted operators are responsible for supportive unmanned systems and must attend to their health. On the simple level, the alert provides information about a threat or a failure in the supportive unmanned systems, while in the complex level, the alert includes more specific information about the source of failure, that may require more effort to interpret. The experiment simulates an operational mission in which participants ride an autonomous ground patrol vehicle while identifying threats and targets in the area and being supported by two unmanned systems. Response accuracy to alerts and threat identification rates were measured. Results indicate that tactile alerts given in addition to visual alerts in a visually loaded and auditory noisy scene, improve task performance. Moreover, the complex level of tactile alerts did not impair performance compared to the simple level of tactile alerts and led to higher rate of identification in specific cases. Nevertheless, relatively high rates of false alarms (FA) for threats were observed, especially when tactile alerts were present, which can be explained by the payment matrix (no penalty) or by the assumption that adding tactile alerts may lead participants to be more vigilant, which can lead to higher correct identifications, but also to higher FA rates.
机译:提出了震动触觉接口作为在信息丰富的领域中增强人机通信的替代方法。当前的研究旨在在MUM-T(有人无人值守团队)设置中检查与视觉警报相结合的两个级别的触觉警报的有效性。在MUM-T中,除了主要任务外,安装好的操作员还负责支持无人驾驶系统,并且必须维护他们的健康。在简单级别上,警报提供有关支持性无人系统中的威胁或故障的信息,而在复杂级别上,警报包括有关故障来源的更具体的信息,这可能需要更多的精力来解释。该实验模拟了一个操作任务,参与者可以乘坐自动地面巡逻车,同时识别该地区的威胁和目标,并由两个无人驾驶系统提供支持。测量对警报的响应准确性和威胁识别率。结果表明,除了在视觉负载和听觉嘈杂的场景中提供视觉警报外,还提供触觉警报,可改善任务性能。此外,与简单级别的触觉警报相比,复杂级别的触觉警报不会影响性能,并且在特定情况下会导致较高的识别率。但是,观察到威胁的虚假警报(FA)的比率相对较高,尤其是在存在触觉警报的情况下,这可以通过付款矩阵(无罚金)或假设添加触觉警报可能会使参与者更加警惕的方式来解释。 ,这可以导致较高的正确识别率,但也可以导致较高的FA率。

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