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

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。自动化系统已广泛应用于各种环境中，例如运输、制造、医疗保健或生活。预计将受益于自动化系统发展的一个特定人群是65岁及以上的成年人。老年人已成为全球增长最快的年龄组。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，比如驾驶。在这种情况下，自动化系统（例如，自动车辆）可能会给老年人维持日常任务表现和独立性带来特别的好处。考虑到目前的自动化系统经常受到设计限制和需要人工干预，一个可靠的人机界面（HMI）是必要的，以协助人类在手动恢复过程中。此外，认知能力和身体能力的个体差异可能导致不同的任务表现。有必要研究非实足年龄因素，如认知和身体能力，如何影响成年人在复杂环境中的表现和健康。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，并更好地了解衰老过程如何影响与界面的交互。该项目的成果将有助于建立老龄化、自动化和人机交互方面的知识库，并为设计面向广泛用户群体的下一代自动化系统提供指导和建议。通过为人类与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来造福社会。PI还将在地方老年中心开展推广方案，例如举办讲习班和活动，教育成功老龄化的重要性，即在生命的后期生活积极和健康的生活方式。该项目旨在利用自动驾驶模拟进行一系列人体实验，以研究三个领域。第一个领域是探索多模式显示的效果，包括视觉（如增强现实）和触觉界面，以及老年人干预和接管自动化系统的速度。其次，研究数据丰富的复杂环境下，非时间因素对老年人任务绩效的影响。最后一个领域探讨是否视觉和触觉界面，作为多模态显示，可以减轻任务表现的个体差异。该项目将收集经验数据，并开发具有各种能力和局限性的人类行为和表现的计算模型。这些可以帮助研究老龄化、人为因素、包容性设计和框架的研究人员解决非时间因素如何影响复杂任务的表现。为了解决任务绩效的个体差异，本项目将研究自适应多模态显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功能/任务分配的模型。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.