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

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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.