CRII: HCC: Human-automation Interaction: Assistive and Adaptive Multimodal Interface to Support Older Adults in Complex Automated Systems

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。自动化系统已在广泛的环境中应用，如交通、制造业、医疗保健或生活。预计将从自动化系统的开发中受益的一个特殊人群是65岁及以上的成年人。老年人已经成为全球增长最快的年龄段。与年龄相关的认知和身体能力的普遍下降可能会限制老年人执行日常任务的能力，如驾驶。在这种情况下，自动化系统(例如，自动化车辆)可能会给老年人带来特别的好处，以维持日常任务表现和独立性。鉴于目前的自动化系统往往受到设计限制的限制，需要人工干预，因此在人工恢复过程中需要可靠的人机界面(HMI)来帮助人类。此外，认知能力和身体能力的个体差异可能会导致不同的任务表现。有必要研究非时间顺序的年龄因素，如认知和身体能力，可能如何影响成年人在复杂环境中的表现和幸福感。该项目旨在开发方法和工具，以支持自动化系统中具有不同能力的老年人，并更好地了解老龄化过程如何影响与界面的交互。该项目的成果将为老龄化、自动化和人机交互方面的知识库做出贡献，并为为广泛的用户群体设计下一代自动化系统提供指导和建议。通过为人-自动化交互的科学基础做出贡献，该项目将通过提高自动化系统中的人类安全和福祉而造福社会。PI还将在当地老年中心发起外展计划，如讲习班和活动，以教育成功老龄化的重要性，即在生命后期过上积极健康的生活方式。该项目旨在利用自动驾驶模拟进行一系列人体实验，以调查三个领域。第一个领域是探索多模式显示，包括视觉(例如，增强现实)和触觉界面，对老年人介入和接管自动化系统的速度的影响。其次，该项目将调查非时序因素对老年人在数据丰富的复杂环境中任务绩效的影响。最后一个领域探索视觉和触觉界面作为多模式显示，是否可以缓解任务绩效的个体差异。该项目将收集经验数据，并开发具有各种能力和限制的人类行为和表现的计算模型。这些可以帮助研究人员在老龄化、人为因素以及包容性设计和框架方面工作，以解决非时序因素如何影响复杂任务的绩效。为了解决任务绩效的个体差异，该项目将调查适应性多通道展示对老年人接管绩效变化的有效性。这项研究还将有助于设计特定于自动化接管和机器与人类之间的功能/任务分配的模型。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Automated systems have been applied in a wide range of environments, such as in transportation, manufacturing, healthcare, or living. One particular demographic expected to benefit from the development of automated systems is adults aged 65 years and older. Older adults have become the fastest-growing age group globally. General age-related declines in cognitive and physical abilities may limit older adults’ ability to perform daily tasks, such as driving. In this case, automated systems (e.g., automated vehicles), may bring particular benefits for older adults to maintain daily task performance and independence. Given that current automated systems are often constrained by design limits and require human interventions, a reliable human-machine interface (HMI) is necessary to assist humans during the manual recovery process. Additionally, individual differences in cognitive and physical capabilities may lead to different task performances. It is necessary to examine how non-chronological age factors, such as cognitive and physical abilities, may impact adults’ performance and wellbeing in complex environments. This project aims to develop methods and tools to support older adults with different abilities in automated systems and to better understand how the aging process affects interaction with an interface. The outcomes of the project will contribute to the knowledge base in aging, automation, and human-machine interactions, as well as provide guidelines and recommendations for the design of next-generation automated systems for a wide range of user groups. By contributing to the scientific basis of human-automation interactions, the project will benefit the society by increasing human safety and wellbeing in automated systems. The PI will also initiate outreach programs in local senior centers, such as workshops and activities to educate the importance of successful aging, i.e., living an active and healthy lifestyle at the later stages of life. This project aims to conduct a series of human-subject experiments using automated driving simulations to investigate three areas. The first area is to explore the effects of multimodal displays, both visual (e.g., augmented reality) and tactile interfaces, on how quickly older adults intervene and takeover from an automated system. Second, the project will investigate the impacts of non-chronological factors on older adults’ task performance in data-rich complex environments. The final area explores whether visual and tactile interfaces, as multimodal displays, can mitigate individual differences in task performance. The project will collect empirical data and develop computational models on human behavior and performance with various capabilities and limitations. These could help researchers working in aging, human factors, and inclusive design and frameworks that address how non-chronological factors affect performance on complex tasks. For addressing individual differences in task performance, the project will investigate the effectiveness of adaptive multimodal displays on older adults’ takeover performance change. The research will also contribute designing models specific to automation takeovers and function/task allocation between machines and humans.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.